| Contrastive Cross-domain Recommendation in Matching |
Ruobing Xie, Qi Liu, Liangdong Wang, Shukai Liu, Bo Zhang, Leyu Lin |
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code |
12 |
| Graph-Flashback Network for Next Location Recommendation |
Xuan Rao, Lisi Chen, Yong Liu, Shuo Shang, Bin Yao, Peng Han |
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9 |
| Meta-Learned Metrics over Multi-Evolution Temporal Graphs |
Dongqi Fu, Liri Fang, Ross Maciejewski, Vetle I. Torvik, Jingrui He |
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9 |
| FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients |
Zaixi Zhang, Xiaoyu Cao, Jinyuan Jia, Neil Zhenqiang Gong |
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8 |
| Surrogate for Long-Term User Experience in Recommender Systems |
Yuyan Wang, Mohit Sharma, Can Xu, Sriraj Badam, Qian Sun, Lee Richardson, Lisa Chung, Ed H. Chi, Minmin Chen |
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7 |
| Multi-modal Siamese Network for Entity Alignment |
Liyi Chen, Zhi Li, Tong Xu, Han Wu, Zhefeng Wang, Nicholas Jing Yuan, Enhong Chen |
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7 |
| GraphMAE: Self-Supervised Masked Graph Autoencoders |
Zhenyu Hou, Xiao Liu, Yukuo Cen, Yuxiao Dong, Hongxia Yang, Chunjie Wang, Jie Tang |
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code |
7 |
| MSDR: Multi-Step Dependency Relation Networks for Spatial Temporal Forecasting |
Dachuan Liu, Jin Wang, Shuo Shang, Peng Han |
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code |
7 |
| Joint Knowledge Graph Completion and Question Answering |
Lihui Liu, Boxin Du, Jiejun Xu, Yinglong Xia, Hanghang Tong |
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7 |
| Graph Neural Networks for Multimodal Single-Cell Data Integration |
Hongzhi Wen, Jiayuan Ding, Wei Jin, Yiqi Wang, Yuying Xie, Jiliang Tang |
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code |
7 |
| Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendation |
Yuhao Yang, Chao Huang, Lianghao Xia, Yuxuan Liang, Yanwei Yu, Chenliang Li |
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code |
6 |
| CommerceMM: Large-Scale Commerce MultiModal Representation Learning with Omni Retrieval |
Licheng Yu, Jun Chen, Animesh Sinha, Mengjiao Wang, Yu Chen, Tamara L. Berg, Ning Zhang |
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6 |
| Learning to Rotate: Quaternion Transformer for Complicated Periodical Time Series Forecasting |
Weiqi Chen, Wenwei Wang, Bingqing Peng, Qingsong Wen, Tian Zhou, Liang Sun |
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code |
6 |
| FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learning |
Zhen Wang, Weirui Kuang, Yuexiang Xie, Liuyi Yao, Yaliang Li, Bolin Ding, Jingren Zhou |
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code |
6 |
| CrossCBR: Cross-view Contrastive Learning for Bundle Recommendation |
Yunshan Ma, Yingzhi He, An Zhang, Xiang Wang, TatSeng Chua |
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code |
5 |
| Data-Efficient Brain Connectome Analysis via Multi-Task Meta-Learning |
Yi Yang, Yanqiao Zhu, Hejie Cui, Xuan Kan, Lifang He, Ying Guo, Carl Yang |
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5 |
| Matrix Profile XXIV: Scaling Time Series Anomaly Detection to Trillions of Datapoints and Ultra-fast Arriving Data Streams |
Yue Lu, Renjie Wu, Abdullah Mueen, Maria A. Zuluaga, Eamonn J. Keogh |
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code |
5 |
| ROLAND: Graph Learning Framework for Dynamic Graphs |
Jiaxuan You, Tianyu Du, Jure Leskovec |
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code |
5 |
| Multiplex Heterogeneous Graph Convolutional Network |
Pengyang Yu, Chaofan Fu, Yanwei Yu, Chao Huang, Zhongying Zhao, Junyu Dong |
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5 |
| ERNIE-GeoL: A Geography-and-Language Pre-trained Model and its Applications in Baidu Maps |
Jizhou Huang, Haifeng Wang, Yibo Sun, Yunsheng Shi, Zhengjie Huang, An Zhuo, Shikun Feng |
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5 |
| ChemicalX: A Deep Learning Library for Drug Pair Scoring |
Benedek Rozemberczki, Charles Tapley Hoyt, Anna Gogleva, Piotr Grabowski, Klas Karis, Andrej Lamov, Andriy Nikolov, Sebastian Nilsson, Michaël Ughetto, Yu Wang, Tyler Derr, Benjamin M. Gyori |
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5 |
| DuARE: Automatic Road Extraction with Aerial Images and Trajectory Data at Baidu Maps |
Jianzhong Yang, Xiaoqing Ye, Bin Wu, Yanlei Gu, Ziyu Wang, Deguo Xia, Jizhou Huang |
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5 |
| TwHIN: Embedding the Twitter Heterogeneous Information Network for Personalized Recommendation |
Ahmed ElKishky, Thomas Markovich, Serim Park, Chetan Verma, Baekjin Kim, Ramy Eskander, Yury Malkov, Frank Portman, Sofía Samaniego, Ying Xiao, Aria Haghighi |
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4 |
| Multi-Task Fusion via Reinforcement Learning for Long-Term User Satisfaction in Recommender Systems |
Qihua Zhang, Junning Liu, Yuzhuo Dai, Yiyan Qi, Yifan Yuan, Kunlun Zheng, Fan Huang, Xianfeng Tan |
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4 |
| Feature-aware Diversified Re-ranking with Disentangled Representations for Relevant Recommendation |
Zihan Lin, Hui Wang, Jingshu Mao, Wayne Xin Zhao, Cheng Wang, Peng Jiang, JiRong Wen |
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4 |
| Counteracting User Attention Bias in Music Streaming Recommendation via Reward Modification |
Xiao Zhang, Sunhao Dai, Jun Xu, Zhenhua Dong, Quanyu Dai, JiRong Wen |
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4 |
| Knowledge-enhanced Black-box Attacks for Recommendations |
Jingfan Chen, Wenqi Fan, Guanghui Zhu, Xiangyu Zhao, Chunfeng Yuan, Qing Li, Yihua Huang |
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code |
4 |
| Towards Universal Sequence Representation Learning for Recommender Systems |
Yupeng Hou, Shanlei Mu, Wayne Xin Zhao, Yaliang Li, Bolin Ding, JiRong Wen |
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code |
4 |
| On Structural Explanation of Bias in Graph Neural Networks |
Yushun Dong, Song Wang, Yu Wang, Tyler Derr, Jundong Li |
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4 |
| SMORE: Knowledge Graph Completion and Multi-hop Reasoning in Massive Knowledge Graphs |
Hongyu Ren, Hanjun Dai, Bo Dai, Xinyun Chen, Denny Zhou, Jure Leskovec, Dale Schuurmans |
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4 |
| Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakage |
Yu Wang, Yuying Zhao, Yushun Dong, Huiyuan Chen, Jundong Li, Tyler Derr |
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4 |
| COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive Learning |
Yifei Zhang, Hao Zhu, Zixing Song, Piotr Koniusz, Irwin King |
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4 |
| How does Heterophily Impact the Robustness of Graph Neural Networks?: Theoretical Connections and Practical Implications |
Jiong Zhu, Junchen Jin, Donald Loveland, Michael T. Schaub, Danai Koutra |
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4 |
| Company-as-Tribe: Company Financial Risk Assessment on Tribe-Style Graph with Hierarchical Graph Neural Networks |
Wendong Bi, Bingbing Xu, Xiaoqian Sun, Zidong Wang, Huawei Shen, Xueqi Cheng |
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4 |
| Distributed Hybrid CPU and GPU training for Graph Neural Networks on Billion-Scale Heterogeneous Graphs |
Da Zheng, Xiang Song, Chengru Yang, Dominique LaSalle, George Karypis |
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4 |
| Graph Neural Networks: Foundation, Frontiers and Applications |
Lingfei Wu, Peng Cui, Jian Pei, Liang Zhao, Xiaojie Guo |
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4 |
| Deconfounding Duration Bias in Watch-time Prediction for Video Recommendation |
Ruohan Zhan, Changhua Pei, Qiang Su, Jianfeng Wen, Xueliang Wang, Guanyu Mu, Dong Zheng, Peng Jiang, Kun Gai |
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3 |
| Learning Binarized Graph Representations with Multi-faceted Quantization Reinforcement for Top-K Recommendation |
Yankai Chen, Huifeng Guo, Yingxue Zhang, Chen Ma, Ruiming Tang, Jingjie Li, Irwin King |
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3 |
| Addressing Unmeasured Confounder for Recommendation with Sensitivity Analysis |
Sihao Ding, Peng Wu, Fuli Feng, Yitong Wang, Xiangnan He, Yong Liao, Yongdong Zhang |
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3 |
| Disentangled Ontology Embedding for Zero-shot Learning |
Yuxia Geng, Jiaoyan Chen, Wen Zhang, Yajing Xu, Zhuo Chen, Jeff Z. Pan, Yufeng Huang, Feiyu Xiong, Huajun Chen |
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3 |
| Detecting Arbitrary Order Beneficial Feature Interactions for Recommender Systems |
Yixin Su, Yunxiang Zhao, Sarah M. Erfani, Junhao Gan, Rui Zhang |
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3 |
| AdaFS: Adaptive Feature Selection in Deep Recommender System |
Weilin Lin, Xiangyu Zhao, Yejing Wang, Tong Xu, Xian Wu |
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3 |
| ClusterEA: Scalable Entity Alignment with Stochastic Training and Normalized Mini-batch Similarities |
Yunjun Gao, Xiaoze Liu, Junyang Wu, Tianyi Li, Pengfei Wang, Lu Chen |
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3 |
| Source Localization of Graph Diffusion via Variational Autoencoders for Graph Inverse Problems |
Chen Ling, Junji Jiang, Junxiang Wang, Liang Zhao |
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3 |
| Variational Flow Graphical Model |
Shaogang Ren, Belhal Karimi, Dingcheng Li, Ping Li |
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3 |
| Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values |
Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark E. Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana |
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| GBPNet: Universal Geometric Representation Learning on Protein Structures |
Sarp Aykent, Tian Xia |
Representation learning of protein 3D structures is challenging and essential for applications, e.g., computational protein design or protein engineering. Recently, geometric deep learning has achieved great success in non-Euclidean domains. Although protein can be represented as a graph naturally, it remains under-explored mainly due to the significant challenges in modeling the complex representations and capturing the inherent correlation in the 3D structure modeling. Several challenges include: 1) It is challenging to extract and preserve multi-level rotation and translation equivariant information during learning. 2) Difficulty in developing appropriate tools to effectively leverage the input spatial representations to capture complex geometries across the spatial dimension. 3) Difficulty in incorporating various geometric features and preserving the inherent structural relations. In this work, we introduce geometric bottleneck perceptron, and a general SO(3)-equivariant message passing neural network built on top of it for protein structure representation learning. The proposed geometric bottleneck perceptron can be incorporated into diverse network architecture backbones to process geometric data in different domains. This research shed new light on geometric deep learning in 3D structure studies. Empirically, we demonstrate the strength of our proposed approach on three core downstream tasks, where our model achieves significant improvements and outperforms existing benchmarks. The implementation is available at https://github.com/sarpaykent/GBPNet. |
蛋白质三维结构的表示学习是具有挑战性和必要的应用，例如，计算蛋白质设计或蛋白质工程。近年来，几何深度学习在非欧几里德领域取得了巨大的成功。虽然蛋白质可以自然地表示为一个图形，但是它仍然没有得到充分的开发，主要是由于在建模复杂的表示和捕获三维结构建模中的内在关联方面的重大挑战。这些挑战包括: 1)在学习过程中提取和保存多层次旋转和翻译等变信息是一个挑战。2)难以开发合适的工具来有效地利用输入空间表示来捕获跨空间维度的复杂几何图形。3)难以结合各种几何特征和保持固有的结构关系。本文介绍了几何瓶颈感知器，并在此基础上构建了一个通用的 SO (3)等变信息传递神经网络，用于蛋白质结构表示学习。提出的几何瓶颈感知器可以整合到不同的网络结构骨架中，用于处理不同领域的几何数据。本研究为三维结构研究中的几何深度学习提供了新的思路。实际上，我们在三个核心的下游任务中展示了我们提议的方法的优势，在这些任务中，我们的模型实现了显著的改进，并优于现有的基准测试。有关实施方案可于 https://github.com/sarpaykent/gbpnet 索取。 |
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| Motif Prediction with Graph Neural Networks |
Maciej Besta, Raphael Grob, Cesare Miglioli, Nicola Bernold, Grzegorz Kwasniewski, Gabriel Gjini, Raghavendra Kanakagiri, Saleh Ashkboos, Lukas Gianinazzi, Nikoli Dryden, Torsten Hoefler |
Link prediction is one of the central problems in graph mining. However, recent studies highlight the importance of higher-order network analysis, where complex structures called motifs are the first-class citizens. We first show that existing link prediction schemes fail to effectively predict motifs. To alleviate this, we establish a general motif prediction problem and we propose several heuristics that assess the chances for a specified motif to appear. To make the scores realistic, our heuristics consider - among others - correlations between links, i.e., the potential impact of some arriving links on the appearance of other links in a given motif. Finally, for highest accuracy, we develop a graph neural network (GNN) architecture for motif prediction. Our architecture offers vertex features and sampling schemes that capture the rich structural properties of motifs. While our heuristics are fast and do not need any training, GNNs ensure highest accuracy of predicting motifs, both for dense (e.g., k-cliques) and for sparse ones (e.g., k-stars). We consistently outperform the best available competitor by more than 10% on average and up to 32% in area under the curve. Importantly, the advantages of our approach over schemes based on uncorrelated link prediction increase with the increasing motif size and complexity. We also successfully apply our architecture for predicting more arbitrary clusters and communities, illustrating its potential for graph mining beyond motif analysis. |
链接预测是图挖掘的核心问题之一。然而，最近的研究强调了高阶网络分析的重要性，在这种网络分析中，被称为图案的复杂结构是一等公民。我们首先证明了现有的链路预测方案不能有效地预测图案。为了解决这个问题，我们建立了一个通用的主题预测问题，并提出了几种启发式算法来评估特定主题出现的可能性。为了使得分数更加真实，我们的启发式方法考虑了链接之间的相关性，也就是说，一些到达的链接对给定主题中其他链接的外观的潜在影响。最后，为了获得最高的精度，我们开发了一个图形神经网络(GNN)结构用于模体预测。我们的体系结构提供了顶点特征和抽样方案，这些特征和抽样方案捕获了图案丰富的结构属性。虽然我们的启发式算法是快速的，不需要任何训练，GNN 确保预测图案的最高准确性，无论是对于密集的(例如，k- 团)和稀疏的(例如，k- 星)。我们始终超越最好的竞争对手超过10% 的平均水平和高达32% 的面积下的曲线。重要的是，与基于不相关链路预测的方案相比，我们的方法的优势随着基序大小和复杂度的增加而增加。我们还成功地应用了我们的体系结构来预测更多的任意集群和社区，说明了它在图形挖掘方面的潜力超越了主题分析。 |
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| Efficient Orthogonal Multi-view Subspace Clustering |
Mansheng Chen, ChangDong Wang, Dong Huang, JianHuang Lai, Philip S. Yu |
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3 |
| Local Evaluation of Time Series Anomaly Detection Algorithms |
Alexis Huet, José Manuel Navarro, Dario Rossi |
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3 |
| Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective |
Wei Jin, Xiaorui Liu, Yao Ma, Charu C. Aggarwal, Jiliang Tang |
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3 |
| Learned Token Pruning for Transformers |
Sehoon Kim, Sheng Shen, David Thorsley, Amir Gholami, Woosuk Kwon, Joseph Hassoun, Kurt Keutzer |
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3 |
| KPGT: Knowledge-Guided Pre-training of Graph Transformer for Molecular Property Prediction |
Han Li, Dan Zhao, Jianyang Zeng |
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3 |
| Learning Causal Effects on Hypergraphs |
Jing Ma, Mengting Wan, Longqi Yang, Jundong Li, Brent J. Hecht, Jaime Teevan |
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3 |
| Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting |
Zezhi Shao, Zhao Zhang, Fei Wang, Yongjun Xu |
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3 |
| GPPT: Graph Pre-training and Prompt Tuning to Generalize Graph Neural Networks |
Mingchen Sun, Kaixiong Zhou, Xin He, Ying Wang, Xin Wang |
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3 |
| Reinforcement Subgraph Reasoning for Fake News Detection |
Ruichao Yang, Xiting Wang, Yiqiao Jin, Chaozhuo Li, Jianxun Lian, Xing Xie |
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3 |
| Unsupervised Key Event Detection from Massive Text Corpora |
Yunyi Zhang, Fang Guo, Jiaming Shen, Jiawei Han |
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3 |
| Learning Sparse Latent Graph Representations for Anomaly Detection in Multivariate Time Series |
Siho Han, Simon S. Woo |
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3 |
| DuIVA: An Intelligent Voice Assistant for Hands-free and Eyes-free Voice Interaction with the Baidu Maps App |
Jizhou Huang, Haifeng Wang, Shiqiang Ding, Shaolei Wang |
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3 |
| A New Generation of Perspective API: Efficient Multilingual Character-level Transformers |
Alyssa Lees, Vinh Q. Tran, Yi Tay, Jeffrey Sorensen, Jai Prakash Gupta, Donald Metzler, Lucy Vasserman |
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3 |
| OAG-BERT: Towards a Unified Backbone Language Model for Academic Knowledge Services |
Xiao Liu, Da Yin, Jingnan Zheng, Xingjian Zhang, Peng Zhang, Hongxia Yang, Yuxiao Dong, Jie Tang |
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3 |
| Fed-LTD: Towards Cross-Platform Ride Hailing via Federated Learning to Dispatch |
Yansheng Wang, Yongxin Tong, Zimu Zhou, Ziyao Ren, Yi Xu, Guobin Wu, Weifeng Lv |
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3 |
| Graph Attention Multi-Layer Perceptron |
Wentao Zhang, Ziqi Yin, Zeang Sheng, Yang Li, Wen Ouyang, Xiaosen Li, Yangyu Tao, Zhi Yang, Bin Cui |
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| ItemSage: Learning Product Embeddings for Shopping Recommendations at Pinterest |
Paul Baltescu, Haoyu Chen, Nikil Pancha, Andrew Zhai, Jure Leskovec, Charles Rosenberg |
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| Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning |
Xiaolei Wang, Kun Zhou, JiRong Wen, Wayne Xin Zhao |
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| Device-cloud Collaborative Recommendation via Meta Controller |
Jiangchao Yao, Feng Wang, Xichen Ding, Shaohu Chen, Bo Han, Jingren Zhou, Hongxia Yang |
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| MolSearch: Search-based Multi-objective Molecular Generation and Property Optimization |
Mengying Sun, Jing Xing, Han Meng, Huijun Wang, Bin Chen, Jiayu Zhou |
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| Invariant Preference Learning for General Debiasing in Recommendation |
Zimu Wang, Yue He, Jiashuo Liu, Wenchao Zou, Philip S. Yu, Peng Cui |
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| Automatic Controllable Product Copywriting for E-Commerce |
Xiaojie Guo, Qingkai Zeng, Meng Jiang, Yun Xiao, Bo Long, Lingfei Wu |
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| Multi-task Hierarchical Classification for Disk Failure Prediction in Online Service Systems |
Yudong Liu, Hailan Yang, Pu Zhao, Minghua Ma, Chengwu Wen, Hongyu Zhang, Chuan Luo, Qingwei Lin, Chang Yi, Jiaojian Wang, Chenjian Zhang, Paul Wang, Yingnong Dang, Saravan Rajmohan, Dongmei Zhang |
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| EGM: Enhanced Graph-based Model for Large-scale Video Advertisement Search |
Tan Yu, Jie Liu, Yi Yang, Yi Li, Hongliang Fei, Ping Li |
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| Saliency-Regularized Deep Multi-Task Learning |
Guangji Bai, Liang Zhao |
Multi-task learning (MTL) is a framework that enforces multiple learning tasks to share their knowledge to improve their generalization abilities. While shallow multi-task learning can learn task relations, it can only handle pre-defined features. Modern deep multi-task learning can jointly learn latent features and task sharing, but they are obscure in task relation. Also, they pre-define which layers and neurons should share across tasks and cannot learn adaptively. To address these challenges, this paper proposes a new multi-task learning framework that jointly learns latent features and explicit task relations by complementing the strength of existing shallow and deep multitask learning scenarios. Specifically, we propose to model the task relation as the similarity between tasks' input gradients, with a theoretical analysis of their equivalency. In addition, we innovatively propose a multi-task learning objective that explicitly learns task relations by a new regularizer. Theoretical analysis shows that the generalizability error has been reduced thanks to the proposed regularizer. Extensive experiments on several multi-task learning and image classification benchmarks demonstrate the proposed method's effectiveness, efficiency as well as reasonableness in the learned task relation patterns. |
多任务学习(MTL)是一种强制多任务共享知识以提高其泛化能力的学习框架。浅层多任务学习虽然可以学习任务关系，但只能处理预定义的特征。现代深度多任务学习可以联合学习任务的潜在特征和任务共享，但在任务关系方面较为模糊。此外，他们预先定义了哪些层和神经元应该跨任务共享，而不能自适应地学习。针对这些挑战，本文提出了一种新的多任务学习框架，通过补充现有浅层和深层多任务学习场景的优势，联合学习潜在特征和显性任务关系。具体来说，我们提出将任务关系建模为任务输入梯度之间的相似性，并对其等效性进行了理论分析。此外，我们创新地提出了一个多任务学习目标，通过一个新的正则化器显式学习任务关系。理论分析表明，该正则化器可以减小泛化误差。通过对多个多任务学习和图像分类基准的大量实验，证明了该方法在学习任务关系模式方面的有效性、高效性和合理性。 |
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| Discovering Significant Patterns under Sequential False Discovery Control |
Sebastian Dalleiger, Jilles Vreeken |
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| Connecting Low-Loss Subspace for Personalized Federated Learning |
SeokJu Hahn, Minwoo Jeong, Junghye Lee |
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| Dual-Geometric Space Embedding Model for Two-View Knowledge Graphs |
Roshni G. Iyer, Yunsheng Bai, Wei Wang, Yizhou Sun |
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| Rep2Vec: Repository Embedding via Heterogeneous Graph Adversarial Contrastive Learning |
Yiyue Qian, Yiming Zhang, Qianlong Wen, Yanfang Ye, Chuxu Zhang |
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| Multi-Agent Graph Convolutional Reinforcement Learning for Dynamic Electric Vehicle Charging Pricing |
Weijia Zhang, Hao Liu, Jindong Han, Yong Ge, Hui Xiong |
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| Learning Backward Compatible Embeddings |
Weihua Hu, Rajas Bansal, Kaidi Cao, Nikhil Rao, Karthik Subbian, Jure Leskovec |
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| EdgeWatch: Collaborative Investigation of Data Integrity at the Edge based on Blockchain |
Bo Li, Qiang He, Liang Yuan, Feifei Chen, Lingjuan Lyu, Yun Yang |
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| Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameters |
Xiangru Lian, Binhang Yuan, Xuefeng Zhu, Yulong Wang, Yongjun He, Honghuan Wu, Lei Sun, Haodong Lyu, Chengjun Liu, Xing Dong, Yiqiao Liao, Mingnan Luo, Congfei Zhang, Jingru Xie, Haonan Li, Lei Chen, Renjie Huang, Jianying Lin, Chengchun Shu, Xuezhong Qiu, Zhishan Liu, Dongying Kong, Lei Yuan, Hai Yu, Sen Yang, Ce Zhang, Ji Liu |
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| HiPAL: A Deep Framework for Physician Burnout Prediction Using Activity Logs in Electronic Health Records |
Hanyang Liu, Sunny S. Lou, Benjamin C. Warner, Derek R. Harford, Thomas George Kannampallil, Chenyang Lu |
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| Multiwave COVID-19 Prediction from Social Awareness Using Web Search and Mobility Data |
Jiawei Xue, Takahiro Yabe, Kota Tsubouchi, Jianzhu Ma, Satish V. Ukkusuri |
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| Make Fairness More Fair: Fair Item Utility Estimation and Exposure Re-Distribution |
Jiayin Wang, Weizhi Ma, Jiayu Li, Hongyu Lu, Min Zhang, Biao Li, Yiqun Liu, Peng Jiang, Shaoping Ma |
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| Adaptive Model Pooling for Online Deep Anomaly Detection from a Complex Evolving Data Stream |
Susik Yoon, Youngjun Lee, JaeGil Lee, Byung Suk Lee |
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| Automatically Discovering User Consumption Intents in Meituan |
Yinfeng Li, Chen Gao, Xiaoyi Du, Huazhou Wei, Hengliang Luo, Depeng Jin, Yong Li |
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| Streaming Hierarchical Clustering Based on Point-Set Kernel |
Xin Han, Ye Zhu, Kai Ming Ting, DeChuan Zhan, Gang Li |
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| Compressing Deep Graph Neural Networks via Adversarial Knowledge Distillation |
Huarui He, Jie Wang, Zhanqiu Zhang, Feng Wu |
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code |
2 |
| UD-GNN: Uncertainty-aware Debiased Training on Semi-Homophilous Graphs |
Yang Liu, Xiang Ao, Fuli Feng, Qing He |
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code |
2 |
| Interpreting Trajectories from Multiple Views: A Hierarchical Self-Attention Network for Estimating the Time of Arrival |
Zebin Chen, Xiaolin Xiao, YueJiao Gong, Jun Fang, Nan Ma, Hua Chai, Zhiguang Cao |
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2 |
| Causal Inference-Based Root Cause Analysis for Online Service Systems with Intervention Recognition |
Mingjie Li, Zeyan Li, Kanglin Yin, Xiaohui Nie, Wenchi Zhang, Kaixin Sui, Dan Pei |
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2 |
| RES: A Robust Framework for Guiding Visual Explanation |
Yuyang Gao, Tong Steven Sun, Guangji Bai, Siyi Gu, Sungsoo Ray Hong, Liang Zhao |
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code |
2 |
| ProActive: Self-Attentive Temporal Point Process Flows for Activity Sequences |
Vinayak Gupta, Srikanta Bedathur |
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code |
2 |
| Communication-Efficient Robust Federated Learning with Noisy Labels |
Junyi Li, Jian Pei, Heng Huang |
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code |
2 |
| Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNN |
Kuan Li, Yang Liu, Xiang Ao, Jianfeng Chi, Jinghua Feng, Hao Yang, Qing He |
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2 |
| Mask and Reason: Pre-Training Knowledge Graph Transformers for Complex Logical Queries |
Xiao Liu, Shiyu Zhao, Kai Su, Yukuo Cen, Jiezhong Qiu, Mengdi Zhang, Wei Wu, Yuxiao Dong, Jie Tang |
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code |
2 |
| Learning on Graphs with Out-of-Distribution Nodes |
Yu Song, Donglin Wang |
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2 |
| Causal Attention for Interpretable and Generalizable Graph Classification |
Yongduo Sui, Xiang Wang, Jiancan Wu, Min Lin, Xiangnan He, TatSeng Chua |
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2 |
| Graph Neural Networks with Node-wise Architecture |
Zhen Wang, Zhewei Wei, Yaliang Li, Weirui Kuang, Bolin Ding |
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code |
2 |
| CLARE: A Semi-supervised Community Detection Algorithm |
Xixi Wu, Yun Xiong, Yao Zhang, Yizhu Jiao, Caihua Shan, Yiheng Sun, Yangyong Zhu, Philip S. Yu |
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code |
2 |
| Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting |
Junchen Ye, Zihan Liu, Bowen Du, Leilei Sun, Weimiao Li, Yanjie Fu, Hui Xiong |
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2 |
| M3Care: Learning with Missing Modalities in Multimodal Healthcare Data |
Chaohe Zhang, Xu Chu, Liantao Ma, Yinghao Zhu, Yasha Wang, Jiangtao Wang, Junfeng Zhao |
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2 |
| SAMCNet: Towards a Spatially Explainable AI Approach for Classifying MxIF Oncology Data |
Majid Farhadloo, Carl Molnar, Gaoxiang Luo, Yan Li, Shashi Shekhar, Rachel L. Maus, Svetomir N. Markovic, Alexey A. Leontovich, Raymond Moore |
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2 |
| No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices |
Ruixuan Liu, Fangzhao Wu, Chuhan Wu, Yanlin Wang, Lingjuan Lyu, Hong Chen, Xing Xie |
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code |
2 |
| What Makes Good Contrastive Learning on Small-Scale Wearable-based Tasks? |
Hangwei Qian, Tian Tian, Chunyan Miao |
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code |
2 |
| Shallow and Deep Non-IID Learning on Complex Data |
Longbing Cao, Philip S. Yu, Zhilin Zhao |
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code |
2 |
| Gradual AutoML using Lale |
Martin Hirzel, Kiran Kate, Parikshit Ram, Avraham Shinnar, Jason Tsay |
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code |
2 |
| Robust Time Series Analysis and Applications: An Industrial Perspective |
Qingsong Wen, Linxiao Yang, Tian Zhou, Liang Sun |
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code |
2 |
| PECOS: Prediction for Enormous and Correlated Output Spaces |
HsiangFu Yu, Jiong Zhang, WeiCheng Chang, JyunYu Jiang, Wei Li, ChoJui Hsieh |
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code |
2 |
| Extracting Relevant Information from User's Utterances in Conversational Search and Recommendation |
Ali Montazeralghaem, James Allan |
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1 |
| Uni-Retriever: Towards Learning the Unified Embedding Based Retriever in Bing Sponsored Search |
Jianjin Zhang, Zheng Liu, Weihao Han, Shitao Xiao, Ruicheng Zheng, Yingxia Shao, Hao Sun, Hanqing Zhu, Premkumar Srinivasan, Weiwei Deng, Qi Zhang, Xing Xie |
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| An Online Multi-task Learning Framework for Google Feed Ads Auction Models |
Ning Ma, Mustafa Ispir, Yuan Li, Yongpeng Yang, Zhe Chen, Derek Zhiyuan Cheng, Lan Nie, Kishor Barman |
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1 |
| NxtPost: User To Post Recommendations In Facebook Groups |
Kaushik Rangadurai, Yiqun Liu, Siddarth Malreddy, Xiaoyi Liu, Piyush Maheshwari, Vishwanath Sangale, Fedor Borisyuk |
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| ReprBERT: Distilling BERT to an Efficient Representation-Based Relevance Model for E-Commerce |
Shaowei Yao, Jiwei Tan, Xi Chen, Juhao Zhang, Xiaoyi Zeng, Keping Yang |
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| Learning Supplementary NLP Features for CTR Prediction in Sponsored Search |
Dong Wang, Shaoguang Yan, Yunqing Xia, Kavé Salamatian, Weiwei Deng, Qi Zhang |
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| AutoShard: Automated Embedding Table Sharding for Recommender Systems |
Daochen Zha, Louis Feng, Bhargav Bhushanam, Dhruv Choudhary, Jade Nie, Yuandong Tian, Jay Chae, Yinbin Ma, Arun Kejariwal, Xia Hu |
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| On-Device Learning for Model Personalization with Large-Scale Cloud-Coordinated Domain Adaption |
Yikai Yan, Chaoyue Niu, Renjie Gu, Fan Wu, Shaojie Tang, Lifeng Hua, Chengfei Lyu, Guihai Chen |
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| Debiasing Learning for Membership Inference Attacks Against Recommender Systems |
Zihan Wang, Na Huang, Fei Sun, Pengjie Ren, Zhumin Chen, Hengliang Luo, Maarten de Rijke, Zhaochun Ren |
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| Automatic Generation of Product-Image Sequence in E-commerce |
Xiaochuan Fan, Chi Zhang, Yong Yang, Yue Shang, Xueying Zhang, Zhen He, Yun Xiao, Bo Long, Lingfei Wu |
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| Semantic Retrieval at Walmart |
Alessandro Magnani, Feng Liu, Suthee Chaidaroon, Sachin Yadav, Praveen Reddy Suram, Ajit Puthenputhussery, Sijie Chen, Min Xie, Anirudh Kashi, Tony Lee, Ciya Liao |
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| Training Large-Scale News Recommenders with Pretrained Language Models in the Loop |
Shitao Xiao, Zheng Liu, Yingxia Shao, Tao Di, Bhuvan Middha, Fangzhao Wu, Xing Xie |
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| DDR: Dialogue Based Doctor Recommendation for Online Medical Service |
Zhi Zheng, Zhaopeng Qiu, Hui Xiong, Xian Wu, Tong Xu, Enhong Chen, Xiangyu Zhao |
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1 |
| FedMSplit: Correlation-Adaptive Federated Multi-Task Learning across Multimodal Split Networks |
Jiayi Chen, Aidong Zhang |
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1 |
| A Spectral Representation of Networks: The Path of Subgraphs |
Shengmin Jin, Hao Tian, Jiayu Li, Reza Zafarani |
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1 |
| Condensing Graphs via One-Step Gradient Matching |
Wei Jin, Xianfeng Tang, Haoming Jiang, Zheng Li, Danqing Zhang, Jiliang Tang, Bing Yin |
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1 |
| RGVisNet: A Hybrid Retrieval-Generation Neural Framework Towards Automatic Data Visualization Generation |
Yuanfeng Song, Xuefang Zhao, Raymond ChiWing Wong, Di Jiang |
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1 |
| Clustering with Fair-Center Representation: Parameterized Approximation Algorithms and Heuristics |
Suhas Thejaswi, Ameet Gadekar, Bruno Ordozgoiti, Michal Osadnik |
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| Towards Representation Alignment and Uniformity in Collaborative Filtering |
Chenyang Wang, Yuanqing Yu, Weizhi Ma, Min Zhang, Chong Chen, Yiqun Liu, Shaoping Ma |
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| Comprehensive Fair Meta-learned Recommender System |
Tianxin Wei, Jingrui He |
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1 |
| RetroGraph: Retrosynthetic Planning with Graph Search |
Shufang Xie, Rui Yan, Peng Han, Yingce Xia, Lijun Wu, Chenjuan Guo, Bin Yang, Tao Qin |
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| Ultrahyperbolic Knowledge Graph Embeddings |
Bo Xiong, Shichao Zhu, Mojtaba Nayyeri, Chengjin Xu, Shirui Pan, Chuan Zhou, Steffen Staab |
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| HICF: Hyperbolic Informative Collaborative Filtering |
Menglin Yang, Zhihao Li, Min Zhou, Jiahong Liu, Irwin King |
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1 |
| Improving Social Network Embedding via New Second-Order Continuous Graph Neural Networks |
Yanfu Zhang, Shangqian Gao, Jian Pei, Heng Huang |
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1 |
| SoccerCPD: Formation and Role Change-Point Detection in Soccer Matches Using Spatiotemporal Tracking Data |
Hyunsung Kim, Bit Kim, Dongwook Chung, Jinsung Yoon, SangKi Ko |
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1 |
| Multi-Aspect Dense Retrieval |
Weize Kong, Swaraj Khadanga, Cheng Li, Shaleen Kumar Gupta, Mingyang Zhang, Wensong Xu, Michael Bendersky |
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| Multi-objective Optimization of Notifications Using Offline Reinforcement Learning |
Prakruthi Prabhakar, Yiping Yuan, Guangyu Yang, Wensheng Sun, Ajith Muralidharan |
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1 |
| Seq2Event: Learning the Language of Soccer Using Transformer-based Match Event Prediction |
Ian Simpson, Ryan J. Beal, Duncan Locke, Timothy J. Norman |
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1 |
| Friend Recommendations with Self-Rescaling Graph Neural Networks |
Xiran Song, Jianxun Lian, Hong Huang, Mingqi Wu, Hai Jin, Xing Xie |
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| 4SDrug: Symptom-based Set-to-set Small and Safe Drug Recommendation |
Yanchao Tan, Chengjun Kong, Leisheng Yu, Pan Li, Chaochao Chen, Xiaolin Zheng, Vicki Hertzberg, Carl Yang |
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| Interpretable Personalized Experimentation |
Han Wu, Sarah Tan, Weiwei Li, Mia Garrard, Adam Obeng, Drew Dimmery, Shaun Singh, Hanson Wang, Daniel R. Jiang, Eytan Bakshy |
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| Graph-based Representation Learning for Web-scale Recommender Systems |
Ahmed ElKishky, Michael M. Bronstein, Ying Xiao, Aria Haghighi |
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| concept2code: Deep Reinforcement Learning for Conversational AI |
Omprakash Sonie, Abir Chakraborty, Ankan Mullick |
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1 |
| Variational Inference for Training Graph Neural Networks in Low-Data Regime through Joint Structure-Label Estimation |
Danning Lao, Xinyu Yang, Qitian Wu, Junchi Yan |
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1 |
| Pairwise Adversarial Training for Unsupervised Class-imbalanced Domain Adaptation |
Weili Shi, Ronghang Zhu, Sheng Li |
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1 |
| TrajGAT: A Graph-based Long-term Dependency Modeling Approach for Trajectory Similarity Computation |
Di Yao, Haonan Hu, Lun Du, Gao Cong, Shi Han, Jingping Bi |
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1 |
| A/B Testing Intuition Busters: Common Misunderstandings in Online Controlled Experiments |
Ron Kohavi, Alex Deng, Lukas Vermeer |
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| Pricing the Long Tail by Explainable Product Aggregation and Monotonic Bandits |
Marco Mussi, Gianmarco Genalti, Francesco Trovò, Alessandro Nuara, Nicola Gatti, Marcello Restelli |
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| PinnerFormer: Sequence Modeling for User Representation at Pinterest |
Nikil Pancha, Andrew Zhai, Jure Leskovec, Charles Rosenberg |
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| Open-Domain Aspect-Opinion Co-Mining with Double-Layer Span Extraction |
Mohna Chakraborty, Adithya Kulkarni, Qi Li |
The aspect-opinion extraction tasks extract aspect terms and opinion terms from reviews. The supervised extraction methods achieve state-of-the-art performance but require large-scale human-annotated training data. Thus, they are restricted for open-domain tasks due to the lack of training data. This work addresses this challenge and simultaneously mines aspect terms, opinion terms, and their correspondence in a joint model. We propose an Open-Domain Aspect-Opinion Co-Mining (ODAO) method with a Double-Layer span extraction framework. Instead of acquiring human annotations, ODAO first generates weak labels for unannotated corpus by employing rules-based on universal dependency parsing. Then, ODAO utilizes this weak supervision to train a double-layer span extraction framework to extract aspect terms (ATE), opinion terms (OTE), and aspect-opinion pairs (AOPE). ODAO applies canonical correlation analysis as an early stopping indicator to avoid the model over-fitting to the noise to tackle the noisy weak supervision. ODAO applies a self-training process to gradually enrich the training data to tackle the weak supervision bias issue. We conduct extensive experiments and demonstrate the power of the proposed ODAO. The results on four benchmark datasets for aspect-opinion co-extraction and pair extraction tasks show that ODAO can achieve competitive or even better performance compared with the state-of-the-art fully supervised methods. |
方面意见提取任务从评论中提取方面术语和意见术语。有监督的提取方法取得了最先进的性能，但需要大规模的人工注释的训练数据。因此，由于缺乏训练数据，它们在开放域任务中受到限制。这项工作解决了这个挑战，同时挖掘方面术语，意见术语，以及它们在联合模型中的对应关系。我们提出了一个开放领域的方面-意见共同挖掘(ODAO)方法与双层跨度提取框架。ODAO 不是获取人工注释，而是首先通过使用基于通用依赖解析的规则为未注释的语料库生成弱标签。然后，ODAO 利用这种弱监督训练一个双层跨度提取框架来提取方面术语(ATE)、观点术语(OTE)和方面-观点对(AOPE)。《噪音管制条例》采用典型相关分析作为及早停止的指标，以避免模型过分配合噪音，以对付噪音较大而监管薄弱的情况。ODAO 采用自我训练过程，逐步丰富训练数据，解决监督偏差问题。我们进行了广泛的实验，并演示了所提出的 ODAO 的功能。通过对四个基准数据集的侧面意见协同提取和对提取任务的实验结果表明，ODAO 算法可以获得比现有全监督算法更好的性能。 |
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| Spatio-Temporal Trajectory Similarity Learning in Road Networks |
Ziquan Fang, Yuntao Du, Xinjun Zhu, Danlei Hu, Lu Chen, Yunjun Gao, Christian S. Jensen |
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| Detecting Cash-out Users via Dense Subgraphs |
Yingsheng Ji, Zheng Zhang, Xinlei Tang, Jiachen Shen, Xi Zhang, Guangwen Yang |
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| Semantic Enhanced Text-to-SQL Parsing via Iteratively Learning Schema Linking Graph |
Aiwei Liu, Xuming Hu, Li Lin, Lijie Wen |
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| Semi-supervised Drifted Stream Learning with Short Lookback |
Weijieying Ren, Pengyang Wang, Xiaolin Li, Charles E. Hughes, Yanjie Fu |
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| State Dependent Parallel Neural Hawkes Process for Limit Order Book Event Stream Prediction and Simulation |
Zijian Shi, John Cartlidge |
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| Improving Data-driven Heterogeneous Treatment Effect Estimation Under Structure Uncertainty |
Christopher Tran, Elena Zheleva |
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| Variational Graph Author Topic Modeling |
Delvin Ce Zhang, Hady Wirawan Lauw |
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| Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems |
Jack FitzGerald, Shankar Ananthakrishnan, Konstantine Arkoudas, Davide Bernardi, Abhishek Bhagia, Claudio Delli Bovi, Jin Cao, Rakesh Chada, Amit Chauhan, Luoxin Chen, Anurag Dwarakanath, Satyam Dwivedi, Turan Gojayev, Karthik Gopalakrishnan, Thomas Gueudré, Dilek HakkaniTur, Wael Hamza, Jonathan J. Hüser, Kevin Martin Jose, Haidar Khan, Beiye Liu, Jianhua Lu, Alessandro Manzotti, Pradeep Natarajan, Karolina Owczarzak, Gokmen Oz, Enrico Palumbo, Charith Peris, Chandana Satya Prakash, Stephen Rawls, Andy Rosenbaum, Anjali Shenoy, Saleh Soltan, Mukund Harakere Sridhar, Lizhen Tan, Fabian Triefenbach, Pan Wei, Haiyang Yu, Shuai Zheng, Gökhan Tür, Prem Natarajan |
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| Augmenting Log-based Anomaly Detection Models to Reduce False Anomalies with Human Feedback |
Tong Jia, Ying Li, Yong Yang, Gang Huang, Zhonghai Wu |
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| DNA-Stabilized Silver Nanocluster Design via Regularized Variational Autoencoders |
Fariha Moomtaheen, Matthew Killeen, James T. Oswald, Anna GonzàlezRosell, Peter Mastracco, Alexander Gorovits, Stacy M. Copp, Petko Bogdanov |
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| Amazon SageMaker Model Monitor: A System for Real-Time Insights into Deployed Machine Learning Models |
David Nigenda, Zohar Karnin, Muhammad Bilal Zafar, Raghu Ramesha, Alan Tan, Michele Donini, Krishnaram Kenthapadi |
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| A Graph Learning Based Framework for Billion-Scale Offline User Identification |
Daixin Wang, Zujian Weng, Zhengwei Wu, Zhiqiang Zhang, Peng Cui, Hongwei Zhao, Jun Zhou |
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| Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator |
Tailin Wu, Qinchen Wang, Yinan Zhang, Rex Ying, Kaidi Cao, Rok Sosic, Ridwan Jalali, Hassan Hamam, Marko Maucec, Jure Leskovec |
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| User Engagement in Mobile Health Applications |
Babaniyi Yusuf Olaniyi, Ana Fernández del Río, África Periáñez, Lauren Bellhouse |
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| Advances in Exploratory Data Analysis, Visualisation and Quality for Data Centric AI Systems |
Hima Patel, Shanmukha C. Guttula, Ruhi Sharma Mittal, Naresh Manwani, Laure BertiÉquille, Abhijit Manatkar |
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| Submodular Feature Selection for Partial Label Learning |
WeiXuan Bao, JunYi Hang, MinLing Zhang |
Partial label learning induces a multi-class classifier from training examples each associated with a candidate label set where the ground-truth label is concealed. Feature selection improves the generalization ability of learning system via selecting essential features for classification from the original feature set, while the task of partial label feature selection is challenging due to ambiguous labeling information. In this paper, the first attempt towards partial label feature selection is investigated via mutual-information-based dependency maximization. Specifically, the proposed approach SAUTE iteratively maximizes the dependency between selected features and labeling information, where the value of mutual information is estimated from confidence-based latent variable inference. In each iteration, the near-optimal features are selected greedily according to properties of submodular mutual information function, while the density of latent label variable is inferred with the help of updated labeling confidences over candidate labels by resorting to kNN aggregation in the induced lower-dimensional feature space. Extensive experiments over synthetic as well as real-world partial label data sets show that the generalization ability of well-established partial label learning algorithms can be significantly improved after coupling with the proposed feature selection approach. |
部分标签学习从训练样本中归纳出一个多类分类器，每个样本与一个隐藏地面真实标签的候选标签集相关联。特征选择通过从原始特征集中选择分类所需的基本特征来提高学习系统的泛化能力，而部分标记特征选择则由于标记信息不明确而面临挑战。本文首次研究了基于互信息的依赖最大化方法在部分标签特征选择中的应用。特别地，提出的方法 SAUTE 迭代地最大化选择的特征和标记信息之间的依赖性，其中互信息的价值是估计基于置信度的潜变量推断。在每次迭代中，根据子模互信息函数的性质贪婪地选择接近最优的特征，利用诱导的低维特征空间中的 kNN 聚集，借助候选标签上更新的标签置信度推断潜在标签变量的密度。通过对合成和实际部分标签数据集的大量实验表明，与所提出的特征选择方法相结合，可以显著提高已有部分标签学习算法的泛化能力。 |
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| Practical Lossless Federated Singular Vector Decomposition over Billion-Scale Data |
Di Chai, Leye Wang, Junxue Zhang, Liu Yang, Shuowei Cai, Kai Chen, Qiang Yang |
With the enactment of privacy-preserving regulations, e.g., GDPR, federated SVD is proposed to enable SVD-based applications over different data sources without revealing the original data. However, many SVD-based applications cannot be well supported by existing federated SVD solutions. The crux is that these solutions, adopting either differential privacy (DP) or homomorphic encryption (HE), suffer from accuracy loss caused by unremovable noise or degraded efficiency due to inflated data. In this paper, we propose FedSVD, a practical lossless federated SVD method over billion-scale data, which can simultaneously achieve lossless accuracy and high efficiency. At the heart of FedSVD is a lossless matrix masking scheme delicately designed for SVD: 1) While adopting the masks to protect private data, FedSVD completely removes them from the final results of SVD to achieve lossless accuracy; and 2) As the masks do not inflate the data, FedSVD avoids extra computation and communication overhead during the factorization to maintain high efficiency. Experiments with real-world datasets show that FedSVD is over 10000x faster than the HE-based method and has 10 orders of magnitude smaller error than the DP-based solution (ε=0.1, δ=0.1) on SVD tasks. We further build and evaluate FedSVD over three real-world applications: principal components analysis (PCA), linear regression (LR), and latent semantic analysis (LSA), to show its superior performance in practice. On federated LR tasks, compared with two state-of-the-art solutions: FATE [17] and SecureML [19], FedSVD-LR is 100x faster than SecureML and 10x faster than FATE. |
随着 GDPR 等隐私保护规则的制定，联邦奇异值分解被提出，以使基于奇异值分解的应用能够在不同数据源之间进行而不暴露原始数据。然而，现有的联邦 SVD 解决方案不能很好地支持许多基于 SVD 的应用程序。问题的关键是，这些解决方案，无论是采用差分隐私(DP)或同态加密(HE) ，都会受到不可去除的噪声或因数据膨胀而导致效率降低所造成的精度损失。在本文中，我们提出了 FedSVD，一种实用的无损联邦 SVD 方法，它可以同时达到无损精度和高效率。FedSVD 的核心是一种为 SVD 精心设计的无损矩阵掩蔽方案: 1)在采用掩蔽保护私有数据的同时，FedSVD 从 SVD 的最终结果中完全去除掩蔽，以实现无损精度; 2)由于掩蔽不会使数据膨胀，FedSVD 避免了因子分解过程中的额外计算和通信开销，保持了高效率。实际数据集的实验表明，在奇异值分解任务中，FedSVD 比基于 HE 的方法快10000倍以上，并且比基于 DP 的方法(ε = 0.1，δ = 0.1)误差小10数量级。我们进一步构建和评估 FedSVD 在三个现实世界中的应用: 主成分分析(PCA)、线性回归分析(LR)和潜在语义学分析(LSA) ，以显示其在实践中的卓越性能。在联邦 LR 任务上，与 FATE [17]和 SecureML [19]这两种最先进的解决方案相比，FedSVD-LR 比 SecureML 快100倍，比 FATE 快10倍。 |
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| Efficient Join Order Selection Learning with Graph-based Representation |
Jin Chen, Guanyu Ye, Yan Zhao, Shuncheng Liu, Liwei Deng, Xu Chen, Rui Zhou, Kai Zheng |
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| RLogic: Recursive Logical Rule Learning from Knowledge Graphs |
Kewei Cheng, Jiahao Liu, Wei Wang, Yizhou Sun |
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| TARNet: Task-Aware Reconstruction for Time-Series Transformer |
Ranak Roy Chowdhury, Xiyuan Zhang, Jingbo Shang, Rajesh K. Gupta, Dezhi Hong |
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| Sufficient Vision Transformer |
Zhi Cheng, Xiu Su, Xueyu Wang, Shan You, Chang Xu |
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| Collaboration Equilibrium in Federated Learning |
Sen Cui, Jian Liang, Weishen Pan, Kun Chen, Changshui Zhang, Fei Wang |
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| Robust Event Forecasting with Spatiotemporal Confounder Learning |
Songgaojun Deng, Huzefa Rangwala, Yue Ning |
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| Robust Inverse Framework using Knowledge-guided Self-Supervised Learning: An application to Hydrology |
Rahul Ghosh, Arvind Renganathan, Kshitij Tayal, Xiang Li, Ankush Khandelwal, Xiaowei Jia, Christopher J. Duffy, John Nieber, Vipin Kumar |
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| Core-periphery Partitioning and Quantum Annealing |
Catherine F. Higham, Desmond J. Higham, Francesco Tudisco |
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1 |
| Few-Shot Fine-Grained Entity Typing with Automatic Label Interpretation and Instance Generation |
Jiaxin Huang, Yu Meng, Jiawei Han |
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1 |
| Global Self-Attention as a Replacement for Graph Convolution |
Md. Shamim Hussain, Mohammed J. Zaki, Dharmashankar Subramanian |
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1 |
| JuryGCN: Quantifying Jackknife Uncertainty on Graph Convolutional Networks |
Jian Kang, Qinghai Zhou, Hanghang Tong |
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| Graph Rationalization with Environment-based Augmentations |
Gang Liu, Tong Zhao, Jiaxin Xu, Tengfei Luo, Meng Jiang |
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| Graph-in-Graph Network for Automatic Gene Ontology Description Generation |
Fenglin Liu, Bang Yang, Chenyu You, Xian Wu, Shen Ge, Adelaide Woicik, Sheng Wang |
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| Geometer: Graph Few-Shot Class-Incremental Learning via Prototype Representation |
Bin Lu, Xiaoying Gan, Lina Yang, Weinan Zhang, Luoyi Fu, Xinbing Wang |
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| Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer |
Bin Lu, Xiaoying Gan, Weinan Zhang, Huaxiu Yao, Luoyi Fu, Xinbing Wang |
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1 |
| Learning Differential Operators for Interpretable Time Series Modeling |
Yingtao Luo, Chang Xu, Yang Liu, Weiqing Liu, Shun Zheng, Jiang Bian |
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1 |
| Core-periphery Models for Hypergraphs |
Marios Papachristou, Jon M. Kleinberg |
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1 |
| Synthesising Audio Adversarial Examples for Automatic Speech Recognition |
Xinghua Qu, Pengfei Wei, Mingyong Gao, Zhu Sun, Yew Soon Ong, Zejun Ma |
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| On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification |
Erik Schultheis, Marek Wydmuch, Rohit Babbar, Krzysztof Dembczynski |
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| ERNet: Unsupervised Collective Extraction and Registration in Neuroimaging Data |
Yao Su, Zhentian Qian, Lifang He, Xiangnan Kong |
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1 |
| Towards an Optimal Asymmetric Graph Structure for Robust Semi-supervised Node Classification |
Zixing Song, Yifei Zhang, Irwin King |
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1 |
| Stabilizing Voltage in Power Distribution Networks via Multi-Agent Reinforcement Learning with Transformer |
Minrui Wang, Mingxiao Feng, Wengang Zhou, Houqiang Li |
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| Task-Adaptive Few-shot Node Classification |
Song Wang, Kaize Ding, Chuxu Zhang, Chen Chen, Jundong Li |
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1 |
| Disentangled Dynamic Heterogeneous Graph Learning for Opioid Overdose Prediction |
Qianlong Wen, Zhongyu Ouyang, Jianfei Zhang, Yiyue Qian, Yanfang Ye, Chuxu Zhang |
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1 |
| Multi-fidelity Hierarchical Neural Processes |
Dongxia Wu, Matteo Chinazzi, Alessandro Vespignani, YiAn Ma, Rose Yu |
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1 |
| Availability Attacks Create Shortcuts |
Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, TieYan Liu |
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1 |
| Model Degradation Hinders Deep Graph Neural Networks |
Wentao Zhang, Zeang Sheng, Ziqi Yin, Yuezihan Jiang, Yikuan Xia, Jun Gao, Zhi Yang, Bin Cui |
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1 |
| Contrastive Learning with Complex Heterogeneity |
Lecheng Zheng, Jinjun Xiong, Yada Zhu, Jingrui He |
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1 |
| AntiBenford Subgraphs: Unsupervised Anomaly Detection in Financial Networks |
Tianyi Chen, Charalampos E. Tsourakakis |
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1 |
| Talent Demand-Supply Joint Prediction with Dynamic Heterogeneous Graph Enhanced Meta-Learning |
Zhuoning Guo, Hao Liu, Le Zhang, Qi Zhang, Hengshu Zhu, Hui Xiong |
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1 |
| Greykite: Deploying Flexible Forecasting at Scale at LinkedIn |
Reza Hosseini, Albert Chen, Kaixu Yang, Sayan Patra, Yi Su, Saad Eddin Al Orjany, Sishi Tang, Parvez Ahammad |
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| A Fully Differentiable Set Autoencoder |
Nikita Janakarajan, Jannis Born, Matteo Manica |
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1 |
| Precision CityShield Against Hazardous Chemicals Threats via Location Mining and Self-Supervised Learning |
Jiahao Ji, Jingyuan Wang, Junjie Wu, Boyang Han, Junbo Zhang, Yu Zheng |
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| Towards Learning Disentangled Representations for Time Series |
Yuening Li, Zhengzhang Chen, Daochen Zha, Mengnan Du, Jingchao Ni, Denghui Zhang, Haifeng Chen, Xia Hu |
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1 |
| CS-RAD: Conditional Member Status Refinement and Ability Discovery for Social Network Applications |
Yiming Ma |
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1 |
| GraphWorld: Fake Graphs Bring Real Insights for GNNs |
John Palowitch, Anton Tsitsulin, Brandon Mayer, Bryan Perozzi |
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| Temporal Multimodal Multivariate Learning |
Hyoshin Park, Justice Darko, Niharika Deshpande, Venktesh Pandey, Hui Su, Masahiro Ono, Dedrick Barkely, Larkin Folsom, Derek J. Posselt, Steve Chien |
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1 |
| Downscaling Earth System Models with Deep Learning |
Sungwon Park, Karandeep Singh, Arjun Nellikkattil, Elke Zeller, TungDuong Mai, Meeyoung Cha |
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1 |
| DocLayNet: A Large Human-Annotated Dataset for Document-Layout Segmentation |
Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, Peter W. J. Staar |
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1 |
| What is the Most Effective Intervention to Increase Job Retention for this Disabled Worker? |
Ha Xuan Tran, Thuc Duy Le, Jiuyong Li, Lin Liu, Jixue Liu, Yanchang Zhao, Tony Waters |
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1 |
| Reinforcement Learning-based Placement of Charging Stations in Urban Road Networks |
Leonie von Wahl, Nicolas Tempelmeier, Ashutosh Sao, Elena Demidova |
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1 |
| Learning to Discover Causes of Traffic Congestion with Limited Labeled Data |
Mudan Wang, Huan Yan, Hongjie Sui, Fan Zuo, Yue Liu, Yong Li |
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1 |
| A Framework for Multi-stage Bonus Allocation in Meal Delivery Platform |
Zhuolin Wu, Li Wang, Fangsheng Huang, Linjun Zhou, Yu Song, Chengpeng Ye, Pengyu Nie, Hao Ren, Jinghua Hao, Renqing He, Zhizhao Sun |
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| Uncertainty Quantification of Sparse Travel Demand Prediction with Spatial-Temporal Graph Neural Networks |
Dingyi Zhuang, Shenhao Wang, Haris N. Koutsopoulos, Jinhua Zhao |
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| Effective Social Network-Based Allocation of COVID-19 Vaccines |
Jiangzhuo Chen, Stefan Hoops, Achla Marathe, Henning S. Mortveit, Bryan L. Lewis, Srinivasan Venkatramanan, Arash Haddadan, Parantapa Bhattacharya, Abhijin Adiga, Anil Vullikanti, Aravind Srinivasan, Mandy L. Wilson, Gal Ehrlich, Maier Fenster, Stephen G. Eubank, Christopher L. Barrett, Madhav V. Marathe |
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1 |
| Automatic Phenotyping by a Seed-guided Topic Model |
Ziyang Song, Yuanyi Hu, Aman Verma, David L. Buckeridge, Yue Li |
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1 |
| Activity Trajectory Generation via Modeling Spatiotemporal Dynamics |
Yuan Yuan, Jingtao Ding, Huandong Wang, Depeng Jin, Yong Li |
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1 |
| Multimodal AutoML for Image, Text and Tabular Data |
Nick Erickson, Xingjian Shi, James Sharpnack, Alexander J. Smola |
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1 |
| Model Monitoring in Practice: Lessons Learned and Open Challenges |
Krishnaram Kenthapadi, Himabindu Lakkaraju, Pradeep Natarajan, Mehrnoosh Sameki |
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1 |
| Algorithmic Fairness on Graphs: Methods and Trends |
Jian Kang, Hanghang Tong |
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1 |
| A Practical Introduction to Federated Learning |
Yaliang Li, Bolin Ding, Jingren Zhou |
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1 |
| Toolkit for Time Series Anomaly Detection |
Dhaval Patel, Dzung Phan, Markus Mueller, Amaresh Rajasekharan |
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1 |
| Epidemic Forecasting with a Data-Centric Lens |
Alexander Rodríguez, Harshavardhan Kamarthi, B. Aditya Prakash |
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1 |
| EXTR: Click-Through Rate Prediction with Externalities in E-Commerce Sponsored Search |
Chi Chen, Hui Chen, Kangzhi Zhao, Junsheng Zhou, Li He, Hongbo Deng, Jian Xu, Bo Zheng, Yong Zhang, Chunxiao Xing |
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0 |
| PARSRec: Explainable Personalized Attention-fused Recurrent Sequential Recommendation Using Session Partial Actions |
Ehsan Gholami, Mohammad Motamedi, Ashwin Aravindakshan |
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| Pretraining Representations of Multi-modal Multi-query E-commerce Search |
Xinyi Liu, Wanxian Guan, Lianyun Li, Hui Li, Chen Lin, Xubin Li, Si Chen, Jian Xu, Hongbo Deng, Bo Zheng |
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| Deep Search Relevance Ranking in Practice |
Linsey Pang, Wei Liu, Kenghao Chang, Xue Li, Moumita Bhattacharya, Xianjing Liu, Stephen Guo |
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0 |
| Debiasing the Cloze Task in Sequential Recommendation with Bidirectional Transformers |
Khalil Damak, Sami Khenissi, Olfa Nasraoui |
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| A Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Prediction |
Quanyu Dai, Haoxuan Li, Peng Wu, Zhenhua Dong, XiaoHua Zhou, Rui Zhang, Rui Zhang, Jie Sun |
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| User-Event Graph Embedding Learning for Context-Aware Recommendation |
Dugang Liu, Mingkai He, Jinwei Luo, Jiangxu Lin, Meng Wang, Xiaolian Zhang, Weike Pan, Zhong Ming |
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| Adversarial Gradient Driven Exploration for Deep Click-Through Rate Prediction |
Kailun Wu, Weijie Bian, Zhangming Chan, Lejian Ren, Shiming Xiang, Shuguang Han, Hongbo Deng, Bo Zheng |
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| Graph-based Multilingual Language Model: Leveraging Product Relations for Search Relevance |
Nurendra Choudhary, Nikhil Rao, Karthik Subbian, Chandan K. Reddy |
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| ASPIRE: Air Shipping Recommendation for E-commerce Products via Causal Inference Framework |
Abhirup Mondal, Anirban Majumder, Vineet Chaoji |
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| Improving Relevance Modeling via Heterogeneous Behavior Graph Learning in Bing Ads |
Bochen Pang, Chaozhuo Li, Yuming Liu, Jianxun Lian, Jianan Zhao, Hao Sun, Weiwei Deng, Xing Xie, Qi Zhang |
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| Type Linking for Query Understanding and Semantic Search |
Giorgos Stoilos, Nikos Papasarantopoulos, Pavlos Vougiouklis, Patrik Bansky |
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| Combo-Fashion: Fashion Clothes Matching CTR Prediction with Item History |
Chenxu Zhu, Peng Du, Weinan Zhang, Yong Yu, Yang Cao |
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| Reward Optimizing Recommendation using Deep Learning and Fast Maximum Inner Product Search |
Imad Aouali, Amine Benhalloum, Martin Bompaire, Achraf Ait Sidi Hammou, Sergey Ivanov, Benjamin Heymann, David Rohde, Otmane Sakhi, Flavian Vasile, Maxime Vono |
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| Low-rank Nonnegative Tensor Decomposition in Hyperbolic Space |
Bo Hui, WeiShinn Ku |
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| Personalized Chit-Chat Generation for Recommendation Using External Chat Corpora |
Changyu Chen, Xiting Wang, Xiaoyuan Yi, Fangzhao Wu, Xing Xie, Rui Yan |
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| G2NET: A General Geography-Aware Representation Network for Hotel Search Ranking |
Jia Xu, Fei Xiong, Zulong Chen, Mingyuan Tao, Liangyue Li, Quan Lu |
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| Avoiding Biases due to Similarity Assumptions in Node Embeddings |
Deepayan Chakrabarti |
Node embeddings are vectors, one per node, that capture a graph's structure. The basic structure is the adjacency matrix of the graph. Recent methods also make assumptions about the similarity of unlinked nodes. However, such assumptions can lead to unintentional but systematic biases against groups of nodes. Calculating similarities between far-off nodes is also difficult under privacy constraints and in dynamic graphs. Our proposed embedding, called NEWS, makes no similarity assumptions, avoiding potential risks to privacy and fairness. NEWS is parameter-free, enables fast link prediction, and has linear complexity. These gains from avoiding assumptions do not significantly affect accuracy, as we show via comparisons against several existing methods on $21$ real-world networks. Code is available at https://github.com/deepayan12/news. |
节点嵌入是向量，每个节点一个，它捕获图的结构。基本结构是图形的邻接矩阵。最近的方法也对未链接节点的相似性做了假设。然而，这样的假设可能会导致对节点群的无意的但是系统性的偏见。在隐私约束和动态图中，计算远程节点之间的相似度也很困难。我们提出的嵌入，称为新闻，没有相似的假设，避免了隐私和公平的潜在风险。NEWS 是无参数的，支持快速链路预测，具有线性复杂度。这些从避免假设中获得的收益不会显著影响准确性，正如我们通过比较现有的几种方法在 $21 $真实世界的网络上所显示的。密码可于 https://github.com/deepayan12/news 索取。 |
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| Task-optimized User Clustering based on Mobile App Usage for Cold-start Recommendations |
Bulou Liu, Bing Bai, Weibang Xie, Yiwen Guo, Hao Chen |
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| Promotheus: An End-to-End Machine Learning Framework for Optimizing Markdown in Online Fashion E-commerce |
Eleanor Loh, Jalaj Khandelwal, Brian Regan, Duncan A. Little |
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| Scalar is Not Enough: Vectorization-based Unbiased Learning to Rank |
Mouxiang Chen, Chenghao Liu, Zemin Liu, Jianling Sun |
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| Efficient Approximate Algorithms for Empirical Variance with Hashed Block Sampling |
Xingguang Chen, Fangyuan Zhang, Sibo Wang |
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| Towards a Native Quantum Paradigm for Graph Representation Learning: A Sampling-based Recurrent Embedding Approach |
Ge Yan, Yehui Tang, Junchi Yan |
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| Toward Real-life Dialogue State Tracking Involving Negative Feedback Utterances |
Puhai Yang, Heyan Huang, Wei Wei, XianLing Mao |
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| M-Mix: Generating Hard Negatives via Multi-sample Mixing for Contrastive Learning |
Shaofeng Zhang, Meng Liu, Junchi Yan, Hengrui Zhang, Lingxiao Huang, Xiaokang Yang, Pinyan Lu |
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| Modeling Persuasion Factor of User Decision for Recommendation |
Chang Liu, Chen Gao, Yuan Yuan, Chen Bai, Lingrui Luo, Xiaoyi Du, Xinlei Shi, Hengliang Luo, Depeng Jin, Yong Li |
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| Lion: A GPU-Accelerated Online Serving System for Web-Scale Recommendation at Baidu |
Hao Liu, Qian Gao, Xiaochao Liao, Guangxing Chen, Hao Xiong, Silin Ren, Guobao Yang, Zhiwei Zha |
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| CognitionNet: A Collaborative Neural Network for Play Style Discovery in Online Skill Gaming Platform |
Rukma Talwadker, Surajit Chakrabarty, Aditya Pareek, Tridib Mukherjee, Deepak Saini |
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| FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling |
Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang, Xing Xie |
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| Mixture of Virtual-Kernel Experts for Multi-Objective User Profile Modeling |
Zhenhui Xu, Meng Zhao, Liqun Liu, Lei Xiao, Xiaopeng Zhang, Bifeng Zhang |
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| Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph |
ChinChia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang |
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| Medical Symptom Detection in Intelligent Pre-Consultation Using Bi-directional Hard-Negative Noise Contrastive Estimation |
Shiwei Zhang, Jichao Sun, Yu Huang, Xueqi Ding, Yefeng Zheng |
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| User-tag Profile Modeling in Recommendation System via Contrast Weighted Tag Masking |
Chenxu Zhu, Peng Du, Xianghui Zhu, Weinan Zhang, Yong Yu, Yang Cao |
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| AI for Social Impact: Results from Deployments for Public Health and Conversation |
Milind Tambe |
With the maturing of AI and multiagent systems research, we have a tremendous opportunity to direct these advances towards addressing complex societal problems. I will focus on domains of public health and conservation, and address one key cross-cutting challenge: how to effectively deploy our limited intervention resources in these problem domains. I will present results from work around the globe in using AI for challenges in public health such as Maternal and Child care interventions, HIV prevention, and in conservation such as endangered wildlife protection. Achieving social impact in these domains often requires methodological advances. To that end, I will highlight key research advances in multiagent reasoning and learning, in particular in, restless multiarmed bandits, influence maximization in social networks, computational game theory and decision-focused learning. In pushing this research agenda, our ultimate goal is to facilitate local communities and non-profits to directly benefit from advances in AI tools and techniques |
随着人工智能和多智能体系统研究的成熟，我们有一个巨大的机会来指导这些进步，以解决复杂的社会问题。我将侧重于公共卫生和保护领域，并解决一个关键的跨领域挑战: 如何在这些问题领域有效部署我们有限的干预资源。我将介绍全球在利用人工智能应对公共卫生挑战方面的工作成果，如母婴保健干预、艾滋病毒预防以及濒危野生动物保护等方面的工作。要在这些领域产生社会影响，往往需要方法上的进步。为此，我将重点介绍多智能体推理和学习方面的关键研究进展，特别是在不安分的多武装匪徒、社交网络中的影响最大化、计算博弈理论和决策集中学习方面。在推动这一研究议程的过程中，我们的最终目标是促进当地社区和非营利组织直接受益于人工智能工具和技术的进步 |
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| Noisy Interactive Graph Search |
Qianhao Cong, Jing Tang, Kai Han, Yuming Huang, Lei Chen, Yeow Meng Chee |
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| LinE: Logical Query Reasoning over Hierarchical Knowledge Graphs |
Zijian Huang, MengFen Chiang, WangChien Lee |
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| Transfer Learning based Search Space Design for Hyperparameter Tuning |
Yang Li, Yu Shen, Huaijun Jiang, Tianyi Bai, Wentao Zhang, Ce Zhang, Bin Cui |
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| Graph Structural Attack by Perturbing Spectral Distance |
Lu Lin, Ethan Blaser, Hongning Wang |
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| Practical Counterfactual Policy Learning for Top-K Recommendations |
Yaxu Liu, JuiNan Yen, BoWen Yuan, Rundong Shi, Peng Yan, ChihJen Lin |
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| FedWalk: Communication Efficient Federated Unsupervised Node Embedding with Differential Privacy |
Qiying Pan, Yifei Zhu |
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| Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking |
Yuta Saito, Thorsten Joachims |
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| Knowledge Enhanced Search Result Diversification |
Zhan Su, Zhicheng Dou, Yutao Zhu, JiRong Wen |
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| Self-Supervised Hypergraph Transformer for Recommender Systems |
Lianghao Xia, Chao Huang, Chuxu Zhang |
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| MetaPTP: An Adaptive Meta-optimized Model for Personalized Spatial Trajectory Prediction |
Yuan Xu, Jiajie Xu, Jing Zhao, Kai Zheng, An Liu, Lei Zhao, Xiaofang Zhou |
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| Nimble GNN Embedding with Tensor-Train Decomposition |
Chunxing Yin, Da Zheng, Israt Nisa, Christos Faloutsos, George Karypis, Richard W. Vuduc |
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| MDP2 Forest: A Constrained Continuous Multi-dimensional Policy Optimization Approach for Short-video Recommendation |
Sizhe Yu, Ziyi Liu, Shixiang Wan, Jia Zheng, Zang Li, Fan Zhou |
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| RCAD: Real-time Collaborative Anomaly Detection System for Mobile Broadband Networks |
Azza H. Ahmed, Michael A. Riegler, Steven Alexander Hicks, Ahmed Elmokashfi |
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| Generalizable Floorplanner through Corner Block List Representation and Hypergraph Embedding |
Mohammad Amini, Zhanguang Zhang, Surya Penmetsa, Yingxue Zhang, Jianye Hao, Wulong Liu |
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| Amazon Shop the Look: A Visual Search System for Fashion and Home |
Ming Du, Arnau Ramisa, Amit Kumar K. C, Sampath Chanda, Mengjiao Wang, Neelakandan Rajesh, Shasha Li, Yingchuan Hu, Tao Zhou, Nagashri Lakshminarayana, Son Tran, Doug Gray |
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| Affective Signals in a Social Media Recommender System |
Jane DwivediYu, YiChia Wang, Lijing Qin, Cristian CantonFerrer, Alon Y. Halevy |
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| Collaborative Intelligence Orchestration: Inconsistency-Based Fusion of Semi-Supervised Learning and Active Learning |
Jiannan Guo, Yangyang Kang, Yu Duan, Xiaozhong Liu, Siliang Tang, Wenqiao Zhang, Kun Kuang, Changlong Sun, Fei Wu |
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| Rax: Composable Learning-to-Rank Using JAX |
Rolf Jagerman, Xuanhui Wang, Honglei Zhuang, Zhen Qin, Michael Bendersky, Marc Najork |
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| AutoFAS: Automatic Feature and Architecture Selection for Pre-Ranking System |
Xiang Li, Xiaojiang Zhou, Yao Xiao, Peihao Huang, Dayao Chen, Sheng Chen, Yunsen Xian |
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| Duplex Conversation: Towards Human-like Interaction in Spoken Dialogue Systems |
TingEn Lin, Yuchuan Wu, Fei Huang, Luo Si, Jian Sun, Yongbin Li |
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| Rapid Regression Detection in Software Deployments through Sequential Testing |
Michael Lindon, Chris Sanden, Vaché Shirikian |
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| Retrieval-Based Gradient Boosting Decision Trees for Disease Risk Assessment |
Handong Ma, Jiahang Cao, Yuchen Fang, Weinan Zhang, Wenbo Sheng, Shaodian Zhang, Yong Yu |
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| Towards Reliable Detection of Dielectric Hotspots in Thermal Images of the Underground Distribution Network |
François Mirallès, Luc Cauchon, MarcAndré Magnan, François Grégoire, Mouhamadou Makhtar Dione, Arnaud Zinflou |
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| CERAM: Coverage Expansion for Recommendations by Associating Discarded Models |
Yoshiki Matsune, Kota Tsubouchi, Nobuhiko Nishio |
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| Intelligent Request Strategy Design in Recommender System |
Xufeng Qian, Yue Xu, Fuyu Lv, Shengyu Zhang, Ziwen Jiang, Qingwen Liu, Xiaoyi Zeng, TatSeng Chua, Fei Wu |
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| Profiling Deep Learning Workloads at Scale using Amazon SageMaker |
Nathalie Rauschmayr, Sami Kama, Muhyun Kim, Miyoung Choi, Krishnaram Kenthapadi |
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| Generative Adversarial Networks Enhanced Pre-training for Insufficient Electronic Health Records Modeling |
Houxing Ren, Jingyuan Wang, Wayne Xin Zhao |
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| Recommendation in Offline Stores: A Gamification Approach for Learning the Spatiotemporal Representation of Indoor Shopping |
Jongkyung Shin, Changhun Lee, Chiehyeon Lim, Yunmo Shin, Junseok Lim |
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| CausalInt: Causal Inspired Intervention for Multi-Scenario Recommendation |
Yichao Wang, Huifeng Guo, Bo Chen, Weiwen Liu, Zhirong Liu, Qi Zhang, Zhicheng He, Hongkun Zheng, Weiwei Yao, Muyu Zhang, Zhenhua Dong, Ruiming Tang |
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| COSSUM: Towards Conversation-Oriented Structured Summarization for Automatic Medical Insurance Assessment |
Sheng Xu, Xiaojun Wan, Sen Hu, Mengdi Zhou, Teng Xu, Hongbin Wang, Haitao Mi |
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| Scale Calibration of Deep Ranking Models |
Le Yan, Zhen Qin, Xuanhui Wang, Michael Bendersky, Marc Najork |
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code |
0 |
| Multi-task Envisioning Transformer-based Autoencoder for Corporate Credit Rating Migration Early Prediction |
Han Yue, Steve Q. Xia, Hongfu Liu |
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code |
0 |
| Felicitas: Federated Learning in Distributed Cross Device Collaborative Frameworks |
Qi Zhang, Tiancheng Wu, Peichen Zhou, Shan Zhou, Yuan Yang, Xiulang Jin |
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code |
0 |
| Reducing the Friction for Building Recommender Systems with Merlin |
Sara Rabhi, Ronay Ak, Marc Romeijn, Gabriel de Souza Pereira Moreira, Benedikt D. Schifferer |
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code |
0 |
| Modern Theoretical Tools for Designing Information Retrieval System |
Da Xu, Chuanwei Ruan |
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code |
0 |
| Data Science and Artificial Intelligence for Responsible Recommendations |
Shoujin Wang, Ninghao Liu, Xiuzhen Zhang, Yan Wang, Francesco Ricci, Bamshad Mobasher |
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code |
0 |
| User Behavior Pre-training for Online Fraud Detection |
Can Liu, Yuncong Gao, Li Sun, Jinghua Feng, Hao Yang, Xiang Ao |
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code |
0 |
| Sampling-based Estimation of the Number of Distinct Values in Distributed Environment |
Jiajun Li, Zhewei Wei, Bolin Ding, Xiening Dai, Lu Lu, Jingren Zhou |
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code |
0 |
| Sample-Efficient Kernel Mean Estimator with Marginalized Corrupted Data |
Xiaobo Xia, Shuo Shan, Mingming Gong, Nannan Wang, Fei Gao, Haikun Wei, Tongliang Liu |
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code |
0 |
| Accurate Node Feature Estimation with Structured Variational Graph Autoencoder |
Jaemin Yoo, Hyunsik Jeon, Jinhong Jung, U Kang |
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code |
0 |
| Semantic Aware Answer Sentence Selection Using Self-Learning Based Domain Adaptation |
Rajdeep Sarkar, Sourav Dutta, Haytham Assem, Mihael Arcan, John P. McCrae |
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code |
0 |
| CONFLUX: A Request-level Fusion Framework for Impression Allocation via Cascade Distillation |
XiaoYu Wang, Bin Tan, Yonghui Guo, Tao Yang, Dongbo Huang, Lan Xu, Nikolaos M. Freris, Hao Zhou, Xiangyang Li |
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0 |
| Multi Armed Bandit vs. A/B Tests in E-commence - Confidence Interval and Hypothesis Test Power Perspectives |
Ding Xiang, Rebecca West, Jiaqi Wang, Xiquan Cui, Jinzhou Huang |
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code |
0 |
| CausalMTA: Eliminating the User Confounding Bias for Causal Multi-touch Attribution |
Di Yao, Chang Gong, Lei Zhang, Sheng Chen, Jingping Bi |
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code |
0 |
| Why Data Scientists Prefer Glassbox Machine Learning: Algorithms, Differential Privacy, Editing and Bias Mitigation |
Rich Caruana, Harsha Nori |
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code |
0 |
| Efficient Machine Learning on Large-Scale Graphs |
Parker Erickson, Victor E. Lee, Feng Shi, Jiliang Tang |
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code |
0 |
| FreeKD: Free-direction Knowledge Distillation for Graph Neural Networks |
Kaituo Feng, Changsheng Li, Ye Yuan, Guoren Wang |
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code |
0 |
| SIPF: Sampling Method for Inverse Protein Folding |
Tianfan Fu, Jimeng Sun |
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code |
0 |
| Antibody Complementarity Determining Regions (CDRs) design using Constrained Energy Model |
Tianfan Fu, Jimeng Sun |
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code |
0 |
| Partial Label Learning with Semantic Label Representations |
Shuo He, Lei Feng, Fengmao Lv, Wen Li, Guowu Yang |
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code |
0 |
| HyperLogLogLog: Cardinality Estimation With One Log More |
Matti Karppa, Rasmus Pagh |
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code |
0 |
| SOS: Score-based Oversampling for Tabular Data |
Jayoung Kim, Chaejeong Lee, Yehjin Shin, Sewon Park, Minjung Kim, Noseong Park, Jihoon Cho |
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code |
0 |
| Domain Adaptation in Physical Systems via Graph Kernel |
Haoran Li, Hanghang Tong, Yang Weng |
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code |
0 |
| RL2: A Call for Simultaneous Representation Learning and Rule Learning for Graph Streams |
Qu Liu, Tingjian Ge |
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code |
0 |
| Learning Models of Individual Behavior in Chess |
Reid McIlroyYoung, Russell Wang, Siddhartha Sen, Jon M. Kleinberg, Ashton Anderson |
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code |
0 |
| Nonlinearity Encoding for Extrapolation of Neural Networks |
Gyoung S. Na, Chanyoung Park |
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code |
0 |
| Neural Bandit with Arm Group Graph |
Yunzhe Qi, Yikun Ban, Jingrui He |
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code |
0 |
| Importance Prioritized Policy Distillation |
Xinghua Qu, Yew Soon Ong, Abhishek Gupta, Pengfei Wei, Zhu Sun, Zejun Ma |
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code |
0 |
| DICE: Domain-attack Invariant Causal Learning for Improved Data Privacy Protection and Adversarial Robustness |
Qibing Ren, Yiting Chen, Yichuan Mo, Qitian Wu, Junchi Yan |
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code |
0 |
| Balancing Bias and Variance for Active Weakly Supervised Learning |
Hitesh Sapkota, Qi Yu |
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code |
0 |
| Learning Optimal Priors for Task-Invariant Representations in Variational Autoencoders |
Hiroshi Takahashi, Tomoharu Iwata, Atsutoshi Kumagai, Sekitoshi Kanai, Masanori Yamada, Yuki Yamanaka, Hisashi Kashima |
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0 |
| Aligning Dual Disentangled User Representations from Ratings and Textual Content |
NhuThuat Tran, Hady W. Lauw |
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code |
0 |
| Estimating Individualized Causal Effect with Confounded Instruments |
Haotian Wang, Wenjing Yang, Longqi Yang, Anpeng Wu, Liyang Xu, Jing Ren, Fei Wu, Kun Kuang |
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code |
0 |
| Streaming Graph Neural Networks with Generative Replay |
Junshan Wang, Wenhao Zhu, Guojie Song, Liang Wang |
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code |
0 |
| Domain Adaptation with Dynamic Open-Set Targets |
Jun Wu, Jingrui He |
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code |
0 |
| Non-stationary A/B Tests |
Yuhang Wu, Zeyu Zheng, Guangyu Zhang, Zuohua Zhang, Chu Wang |
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code |
0 |
| Enhancing Machine Learning Approaches for Graph Optimization Problems with Diversifying Graph Augmentation |
ChenHsu Yang, ChihYa Shen |
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code |
0 |
| Learning Classifiers under Delayed Feedback with a Time Window Assumption |
Shota Yasui, Masahiro Kato |
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code |
0 |
| Intrinsic-Motivated Sensor Management: Exploring with Physical Surprise |
Jingyi Yuan, Yang Weng, Erik Blasch |
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code |
0 |
| Dual Bidirectional Graph Convolutional Networks for Zero-shot Node Classification |
Qin Yue, Jiye Liang, Junbiao Cui, Liang Bai |
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code |
0 |
| Physics-infused Machine Learning for Crowd Simulation |
Guozhen Zhang, Zihan Yu, Depeng Jin, Yong Li |
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code |
0 |
| Few-shot Heterogeneous Graph Learning via Cross-domain Knowledge Transfer |
Qiannan Zhang, Xiaodong Wu, Qiang Yang, Chuxu Zhang, Xiangliang Zhang |
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code |
0 |
| Adaptive Learning for Weakly Labeled Streams |
ZhenYu Zhang, Yuyang Qian, YuJie Zhang, Yuan Jiang, ZhiHua Zhou |
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code |
0 |
| Adaptive Fairness-Aware Online Meta-Learning for Changing Environments |
Chen Zhao, Feng Mi, Xintao Wu, Kai Jiang, Latifur Khan, Feng Chen |
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code |
0 |
| Physics-Guided Graph Meta Learning for Predicting Water Temperature and Streamflow in Stream Networks |
Shengyu Chen, Jacob A. Zwart, Xiaowei Jia |
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code |
0 |
| ILASR: Privacy-Preserving Incremental Learning for Automatic Speech Recognition at Production Scale |
Gopinath Chennupati, Milind Rao, Gurpreet Chadha, Aaron Eakin, Anirudh Raju, Gautam Tiwari, Anit Kumar Sahu, Ariya Rastrow, Jasha Droppo, Andy Oberlin, Buddha Nandanoor, Prahalad Venkataramanan, Zheng Wu, Pankaj Sitpure |
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| Large-Scale Acoustic Automobile Fault Detection: Diagnosing Engines Through Sound |
Dennis Fedorishin, Justas Birgiolas, Deen Dayal Mohan, Livio Forte, Philip Schneider, Srirangaraj Setlur, Venu Govindaraju |
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0 |
| Real-Time Rideshare Driver Supply Values Using Online Reinforcement Learning |
Benjamin Han, Hyungjun Lee, Sébastien Martin |
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code |
0 |
| Three-Stage Root Cause Analysis for Logistics Time Efficiency via Explainable Machine Learning |
Shiqi Hao, Yang Liu, Yu Wang, Yuan Wang, Wenming Zhe |
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code |
0 |
| Unsupervised Learning Style Classification for Learning Path Generation in Online Education Platforms |
Zhicheng He, Wei Xia, Kai Dong, Huifeng Guo, Ruiming Tang, Dingyin Xia, Rui Zhang |
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code |
0 |
| Analyzing Online Transaction Networks with Network Motifs |
Jiawei Jiang, Yusong Hu, Xiaosen Li, Wen Ouyang, Zhitao Wang, Fangcheng Fu, Bin Cui |
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code |
0 |
| COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation |
Yashal Shakti Kanungo, Gyanendra Das, Pooja A, Sumit Negi |
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code |
0 |
| Fast Mining and Forecasting of Co-evolving Epidemiological Data Streams |
Tasuku Kimura, Yasuko Matsubara, Koki Kawabata, Yasushi Sakurai |
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code |
0 |
| Design Domain Specific Neural Network via Symbolic Testing |
Hui Li, Xing Fu, Ruofan Wu, Jinyu Xu, Kai Xiao, Xiaofu Chang, Weiqiang Wang, Shuai Chen, Leilei Shi, Tao Xiong, Yuan Qi |
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code |
0 |
| Arbitrary Distribution Modeling with Censorship in Real-Time Bidding Advertising |
Xu Li, Michelle Ma Zhang, Zhenya Wang, Youjun Tong |
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code |
0 |
| Para-Pred: Addressing Heterogeneity for City-Wide Indoor Status Estimation in On-Demand Delivery |
Wei Liu, Yi Ding, Shuai Wang, Yu Yang, Desheng Zhang |
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0 |
| Uncovering the Heterogeneous Effects of Preference Diversity on User Activeness: A Dynamic Mixture Model |
Yunfei Lu, Peng Cui, Linyun Yu, Lei Li, Wenwu Zhu |
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code |
0 |
| Looper: An End-to-End ML Platform for Product Decisions |
Igor L. Markov, Hanson Wang, Nitya S. Kasturi, Shaun Singh, Mia R. Garrard, Yin Huang, Sze Wai Celeste Yuen, Sarah Tran, Zehui Wang, Igor Glotov, Tanvi Gupta, Peng Chen, Boshuang Huang, Xiaowen Xie, Michael Belkin, Sal Uryasev, Sam Howie, Eytan Bakshy, Norm Zhou |
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code |
0 |
| Proactively Reducing the Hate Intensity of Online Posts via Hate Speech Normalization |
Sarah Masud, Manjot Bedi, Mohammad Aflah Khan, Md. Shad Akhtar, Tanmoy Chakraborty |
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code |
0 |
| Human-in-the-Loop Large-Scale Predictive Maintenance of Workstations |
Alexander V. Nikitin, Samuel Kaski |
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code |
0 |
| Regional-Local Adversarially Learned One-Class Classifier Anomalous Sound Detection in Global Long-Term Space |
Yu Sha, Shuiping Gou, Johannes Faber, Bo Liu, Wei Li, Stefan Schramm, Horst Stoecker, Thomas Steckenreiter, Domagoj Vnucec, Nadine Wetzstein, Andreas Widl, Kai Zhou |
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0 |
| Septor: Seismic Depth Estimation Using Hierarchical Neural Networks |
M. Ashraf Siddiquee, Vinicius M. A. Souza, Glenn Eli Baker, Abdullah Mueen |
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code |
0 |
| Optimizing Long-Term Efficiency and Fairness in Ride-Hailing via Joint Order Dispatching and Driver Repositioning |
Jiahui Sun, Haiming Jin, Zhaoxing Yang, Lu Su, Xinbing Wang |
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| NENYA: Cascade Reinforcement Learning for Cost-Aware Failure Mitigation at Microsoft 365 |
Lu Wang, Pu Zhao, Chao Du, Chuan Luo, Mengna Su, Fangkai Yang, Yudong Liu, Qingwei Lin, Min Wang, Yingnong Dang, Hongyu Zhang, Saravan Rajmohan, Dongmei Zhang |
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0 |
| ROI-Constrained Bidding via Curriculum-Guided Bayesian Reinforcement Learning |
Haozhe Wang, Chao Du, Panyan Fang, Shuo Yuan, Xuming He, Liang Wang, Bo Zheng |
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code |
0 |
| Adaptive Multi-view Rule Discovery for Weakly-Supervised Compatible Products Prediction |
Rongzhi Zhang, Rebecca West, Xiquan Cui, Chao Zhang |
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code |
0 |
| DESCN: Deep Entire Space Cross Networks for Individual Treatment Effect Estimation |
Kailiang Zhong, Fengtong Xiao, Yan Ren, Yaorong Liang, Wenqing Yao, Xiaofeng Yang, Ling Cen |
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0 |
| RBG: Hierarchically Solving Large-Scale Routing Problems in Logistic Systems via Reinforcement Learning |
Zefang Zong, Hansen Wang, Jingwei Wang, Meng Zheng, Yong Li |
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0 |
| Scalable Online Disease Diagnosis via Multi-Model-Fused Actor-Critic Reinforcement Learning |
Weijie He, Ting Chen |
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code |
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| Reinforcement Learning Enhances the Experts: Large-scale COVID-19 Vaccine Allocation with Multi-factor Contact Network |
Qianyue Hao, Wenzhen Huang, Fengli Xu, Kun Tang, Yong Li |
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0 |
| The Battlefront of Combating Misinformation and Coping with Media Bias |
Yi R. Fung, KungHsiang Huang, Preslav Nakov, Heng Ji |
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code |
0 |
| Large-Scale Information Extraction under Privacy-Aware Constraints |
Rajeev Gupta, Ranganath Kondapally |
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code |
0 |
| Online Clustering: Algorithms, Evaluation, Metrics, Applications and Benchmarking |
Jacob Montiel, HoangAnh Ngo, MinhHuong Le Nguyen, Albert Bifet |
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0 |
| Automated Machine Learning & Tuning with FLAML |
Chi Wang, Qingyun Wu, Xueqing Liu, Luis Quintanilla |
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| Decision Intelligence and Analytics for Online Marketplaces: Jobs, Ridesharing, Retail and Beyond |
Zhiwei (Tony) Qin, Liangjie Hong, Rui Song, Hongtu Zhu, Mohammed Korayem, Haiyan Luo, Michael I. Jordan |
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| Machine Learning for Materials Science (MLMS) |
Avadhut Sardeshmukh, Sreedhar Reddy, Gautham B. P., Ankit Agrawal |
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| The Power of (Statistical) Relational Thinking |
Lise Getoor |
Taking into account relational structure during data mining can lead to better results, both in terms of quality and computational efficiency. This structure may be captured in the schema, in links between entities (e.g., graphs) or in rules describing the domain (e.g., knowledge graphs). Further, for richly structured prediction problems, there is often a need for a mix of both logical reasoning and statistical inference. In this talk, I will give an introduction to the field of Statistical Relational Learning (SRL), and I'll identify useful tips and tricks for exploiting structure in both the input and output space. I'll describe our recent work on highly scalable approaches for statistical relational inference. I'll close by introducing a broader interpretation of relational thinking that reveals new research opportunities (and challenges!). |
在数据挖掘过程中考虑到关系结构，可以在质量和计算效率方面取得更好的结果。这种结构可以在模式、实体之间的链接(例如图形)或描述领域的规则(例如知识图形)中捕获。此外，对于结构丰富的预测问题，通常需要同时考虑逻辑推理和推论统计学。在这个演讲中，我将介绍统计关系学习(SRL)领域，并且我将确定在输入和输出空间中利用结构的有用提示和技巧。我将描述我们最近关于统计关系推理的高度可伸缩方法的工作。最后，我将介绍关系思维的更广泛的解释，揭示新的研究机会(和挑战!). |
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| Beyond Traditional Characterizations in the Age of Data: Big Models, Scalable Algorithms, and Meaningful Solutions |
ShangHua Teng |
What are data and network models? What are efficient algorithms? What are meaningful solutions? Big Data, Network Sciences, and Machine Learning have fundamentally challenged the basic characterizations in computing, from the conventional graph-theoretical modeling of networks to the traditional polynomial-time worst-case measures of efficiency: For a long time, graphs have been widely used for defining the structure of social and information networks. However, real-world network data and phenomena are much richer and more complex than what can be captured by nodes and edges. Network data is multifaceted, and thus network sciences require new theories, going beyond classic graph theory and graph-theoretical frameworks, to capture the multifaceted data. More than ever before, it is not just desirable, but essential, that efficient algorithms should be scalable. In other words, their complexity should be nearly linear or even sub-linear with respect to the problem size. Thus, scalability, not just polynomial-time computability, should be elevated as the central complexity notion for characterizing efficient computation. |
什么是数据和网络模型？什么是高效算法？什么是有意义的解决方案？大数据、网络科学和机器学习从根本上挑战了计算的基本特征，从传统的网络图形理论建模到传统的多项式时间最坏情况的效率度量: 长期以来，图形被广泛用于定义社会和信息网络的结构。然而，真实世界的网络数据和现象比节点和边所能捕获的要丰富和复杂得多。网络数据是多方面的，因此网络科学需要超越经典图论和图论框架的新理论来捕获多方面的数据。与以往任何时候相比，有效的算法应该是可伸缩的，这不仅是可取的，而且是必要的。换句话说，相对于问题的大小，它们的复杂度应该接近线性，甚至是次线性。因此，可伸缩性，而不仅仅是多项式时间的可计算性，应该被提升为表征有效计算的核心复杂性概念。 |
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| Multi-Variate Time Series Forecasting on Variable Subsets |
Jatin Chauhan, Aravindan Raghuveer, Rishi Saket, Jay Nandy, Balaraman Ravindran |
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0 |
| HyperAid: Denoising in Hyperbolic Spaces for Tree-fitting and Hierarchical Clustering |
Eli Chien, Puoya Tabaghi, Olgica Milenkovic |
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| Scalable Differentially Private Clustering via Hierarchically Separated Trees |
Vincent CohenAddad, Alessandro Epasto, Silvio Lattanzi, Vahab Mirrokni, Andres Muñoz Medina, David Saulpic, Chris Schwiegelshohn, Sergei Vassilvitskii |
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| Framing Algorithmic Recourse for Anomaly Detection |
Debanjan Datta, Feng Chen, Naren Ramakrishnan |
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| Fair Labeled Clustering |
Seyed A. Esmaeili, Sharmila Duppala, John P. Dickerson, Brian Brubach |
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| On Aligning Tuples for Regression |
Chenguang Fang, Shaoxu Song, Yinan Mei, Ye Yuan, Jianmin Wang |
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| Optimal Interpretable Clustering Using Oblique Decision Trees |
Magzhan Gabidolla, Miguel Á. CarreiraPerpiñán |
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| Finding Meta Winning Ticket to Train Your MAML |
Dawei Gao, Yuexiang Xie, Zimu Zhou, Zhen Wang, Yaliang Li, Bolin Ding |
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| BLISS: A Billion scale Index using Iterative Re-partitioning |
Gaurav Gupta, Tharun Medini, Anshumali Shrivastava, Alexander J. Smola |
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| Subset Node Anomaly Tracking over Large Dynamic Graphs |
Xingzhi Guo, Baojian Zhou, Steven Skiena |
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| Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand Prediction |
Liangzhe Han, Xiaojian Ma, Leilei Sun, Bowen Du, Yanjie Fu, Weifeng Lv, Hui Xiong |
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| Quantifying and Reducing Registration Uncertainty of Spatial Vector Labels on Earth Imagery |
Wenchong He, Zhe Jiang, Marcus Kriby, Yiqun Xie, Xiaowei Jia, Da Yan, Yang Zhou |
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| AdaAX: Explaining Recurrent Neural Networks by Learning Automata with Adaptive States |
Dat Hong, Alberto Maria Segre, Tong Wang |
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| Flexible Modeling and Multitask Learning using Differentiable Tree Ensembles |
Shibal Ibrahim, Hussein Hazimeh, Rahul Mazumder |
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| Selective Cross-City Transfer Learning for Traffic Prediction via Source City Region Re-Weighting |
Yilun Jin, Kai Chen, Qiang Yang |
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| CoRGi: Content-Rich Graph Neural Networks with Attention |
Jooyeon Kim, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Cheng Zhang, Miltiadis Allamanis |
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| ExMeshCNN: An Explainable Convolutional Neural Network Architecture for 3D Shape Analysis |
Seonggyeom Kim, DongKyu Chae |
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| In Defense of Core-set: A Density-aware Core-set Selection for Active Learning |
Yeachan Kim, Bonggun Shin |
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| Modeling Network-level Traffic Flow Transitions on Sparse Data |
Xiaoliang Lei, Hao Mei, Bin Shi, Hua Wei |
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| FlowGEN: A Generative Model for Flow Graphs |
Furkan Kocayusufoglu, Arlei Silva, Ambuj K. Singh |
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0 |
| The DipEncoder: Enforcing Multimodality in Autoencoders |
Collin Leiber, Lena G. M. Bauer, Michael Neumayr, Claudia Plant, Christian Böhm |
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0 |
| HierCDF: A Bayesian Network-based Hierarchical Cognitive Diagnosis Framework |
Jiatong Li, Fei Wang, Qi Liu, Mengxiao Zhu, Wei Huang, Zhenya Huang, Enhong Chen, Yu Su, Shijin Wang |
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| Mining Spatio-Temporal Relations via Self-Paced Graph Contrastive Learning |
Rongfan Li, Ting Zhong, Xinke Jiang, Goce Trajcevski, Jin Wu, Fan Zhou |
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| PAC-Wrap: Semi-Supervised PAC Anomaly Detection |
Shuo Li, Xiayan Ji, Edgar Dobriban, Oleg Sokolsky, Insup Lee |
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0 |
| TransBO: Hyperparameter Optimization via Two-Phase Transfer Learning |
Yang Li, Yu Shen, Huaijun Jiang, Wentao Zhang, Zhi Yang, Ce Zhang, Bin Cui |
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code |
0 |
| Sparse Conditional Hidden Markov Model for Weakly Supervised Named Entity Recognition |
Yinghao Li, Le Song, Chao Zhang |
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0 |
| Deep Representations for Time-varying Brain Datasets |
Sikun Lin, Shuyun Tang, Scott T. Grafton, Ambuj K. Singh |
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| Partial-Quasi-Newton Methods: Efficient Algorithms for Minimax Optimization Problems with Unbalanced Dimensionality |
Chengchang Liu, Shuxian Bi, Luo Luo, John C. S. Lui |
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| Label-enhanced Prototypical Network with Contrastive Learning for Multi-label Few-shot Aspect Category Detection |
Han Liu, Feng Zhang, Xiaotong Zhang, Siyang Zhao, Junjie Sun, Hong Yu, Xianchao Zhang |
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| Fair Representation Learning: An Alternative to Mutual Information |
Ji Liu, Zenan Li, Yuan Yao, Feng Xu, Xiaoxing Ma, Miao Xu, Hanghang Tong |
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| S2RL: Do We Really Need to Perceive All States in Deep Multi-Agent Reinforcement Learning? |
Shuang Luo, Yinchuan Li, Jiahui Li, Kun Kuang, Furui Liu, Yunfeng Shao, Chao Wu |
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| ML4S: Learning Causal Skeleton from Vicinal Graphs |
Pingchuan Ma, Rui Ding, Haoyue Dai, Yuanyuan Jiang, Shuai Wang, Shi Han, Dongmei Zhang |
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| Non-stationary Time-aware Kernelized Attention for Temporal Event Prediction |
Yu Ma, Zhining Liu, Chenyi Zhuang, Yize Tan, Yi Dong, Wenliang Zhong, Jinjie Gu |
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| Discovering Invariant and Changing Mechanisms from Data |
Sarah Mameche, David Kaltenpoth, Jilles Vreeken |
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| Minimizing Congestion for Balanced Dominators |
Yosuke Mizutani, Annie Staker, Blair D. Sullivan |
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| Learning Fair Representation via Distributional Contrastive Disentanglement |
Changdae Oh, Heeji Won, Junhyuk So, Taero Kim, Yewon Kim, Hosik Choi, Kyungwoo Song |
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| MetaV: A Meta-Verifier Approach to Task-Agnostic Model Fingerprinting |
Xudong Pan, Yifan Yan, Mi Zhang, Min Yang |
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| Predicting Opinion Dynamics via Sociologically-Informed Neural Networks |
Maya Okawa, Tomoharu Iwata |
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| Bilateral Dependency Optimization: Defending Against Model-inversion Attacks |
Xiong Peng, Feng Liu, Jingfeng Zhang, Long Lan, Junjie Ye, Tongliang Liu, Bo Han |
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| Compute Like Humans: Interpretable Step-by-step Symbolic Computation with Deep Neural Network |
Shuai Peng, Di Fu, Yong Cao, Yijun Liang, Gu Xu, Liangcai Gao, Zhi Tang |
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code |
0 |
| Evaluating Knowledge Graph Accuracy Powered by Optimized Human-machine Collaboration |
Yifan Qi, Weiguo Zheng, Liang Hong, Lei Zou |
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Yuan Qiu, Wei Dong, Ke Yi, Bin Wu, Feifei Li |
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| External Knowledge Infusion for Tabular Pre-training Models with Dual-adapters |
Can Qin, Sungchul Kim, Handong Zhao, Tong Yu, Ryan A. Rossi, Yun Fu |
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| p-Meta: Towards On-device Deep Model Adaptation |
Zhongnan Qu, Zimu Zhou, Yongxin Tong, Lothar Thiele |
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| Fair and Interpretable Models for Survival Analysis |
Md. Mahmudur Rahman, Sanjay Purushotham |
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| A Generalized Backward Compatibility Metric |
Tomoya Sakai |
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Wei Shen, Yang Yang, Yinan Liu |
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0 |
| Deep Learning for Prognosis Using Task-fMRI: A Novel Architecture and Training Scheme |
Ge Shi, Jason Smucny, Ian Davidson |
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JieJing Shao, Yunlu Xu, Zhanzhan Cheng, YuFeng Li |
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| GUIDE: Group Equality Informed Individual Fairness in Graph Neural Networks |
Weihao Song, Yushun Dong, Ninghao Liu, Jundong Li |
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| Robust and Informative Text Augmentation (RITA) via Constrained Worst-Case Transformations for Low-Resource Named Entity Recognition |
Hyunwoo Sohn, Baekkwan Park |
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| pureGAM: Learning an Inherently Pure Additive Model |
Xingzhi Sun, Ziyu Wang, Rui Ding, Shi Han, Dongmei Zhang |
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Jianhui Sun, Mengdi Huai, Kishlay Jha, Aidong Zhang |
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0 |
| Dense Feature Tracking of Atmospheric Winds with Deep Optical Flow |
Thomas J. Vandal, Kate Duffy, Will McCarty, Akira Sewnath, Ramakrishna R. Nemani |
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| Incremental Cognitive Diagnosis for Intelligent Education |
Shiwei Tong, Jiayu Liu, Yuting Hong, Zhenya Huang, Le Wu, Qi Liu, Wei Huang, Enhong Chen, Dan Zhang |
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| A Model-Agnostic Approach to Differentially Private Topic Mining |
Han Wang, Jayashree Sharma, Shuya Feng, Kai Shu, Yuan Hong |
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| Toward Learning Robust and Invariant Representations with Alignment Regularization and Data Augmentation |
Haohan Wang, Zeyi Huang, Xindi Wu, Eric P. Xing |
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| Group-wise Reinforcement Feature Generation for Optimal and Explainable Representation Space Reconstruction |
Dongjie Wang, Yanjie Fu, Kunpeng Liu, Xiaolin Li, Yan Solihin |
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| Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsing |
Lihan Wang, Bowen Qin, Binyuan Hui, Bowen Li, Min Yang, Bailin Wang, Binhua Li, Jian Sun, Fei Huang, Luo Si, Yongbin Li |
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| Partial Label Learning with Discrimination Augmentation |
Wei Wang, MinLing Zhang |
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| An Embedded Feature Selection Framework for Control |
Jiawen Wei, Fangyuan Wang, Wanxin Zeng, Wenwei Lin, Ning Gui |
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| SagDRE: Sequence-Aware Graph-Based Document-Level Relation Extraction with Adaptive Margin Loss |
Ying Wei, Qi Li |
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| Beyond Point Prediction: Capturing Zero-Inflated & Heavy-Tailed Spatiotemporal Data with Deep Extreme Mixture Models |
Tyler Wilson, Andrew McDonald, Asadullah Hill Galib, PangNing Tan, Lifeng Luo |
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Yue Wu, Jesús A. De Loera |
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| Robust Tensor Graph Convolutional Networks via T-SVD based Graph Augmentation |
Zhebin Wu, Lin Shu, Ziyue Xu, Yaomin Chang, Chuan Chen, Zibin Zheng |
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Ziran Xiong, Wanli Shi, Bin Gu |
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Jie Yan, Yunlei Lu, Liting Chen, Si Qin, Yixin Fang, Qingwei Lin, Thomas Moscibroda, Saravan Rajmohan, Dongmei Zhang |
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Jing Yang, Kai Xie, Ning An |
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Rui Yang, Jie Wang, Zijie Geng, Mingxuan Ye, Shuiwang Ji, Bin Li, Feng Wu |
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Qingping Yang, Yixuan Cao, Ping Luo |
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Changchang Yin, Ruoqi Liu, Jeffrey M. Caterino, Ping Zhang |
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Muchao Ye, Jinghui Chen, Chenglin Miao, Ting Wang, Fenglong Ma |
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Weiyu Zhang, Yiyang Ma, Di Zhu, Lei Dong, Yu Liu |
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Ruijie Zhao, Xianwen Deng, Zhicong Yan, Jun Ma, Zhi Xue, Yijun Wang |
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| The Good, the Bad, and the Outliers: A Testing Framework for Decision Optimization Model Learning |
Orit Davidovich, GheorgheTeodor Bercea, Segev Wasserkrug |
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Tao Feng, Tong Xia, Xiaochen Fan, Huandong Wang, Zefang Zong, Yong Li |
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Sudipta Kar, Giuseppe Castellucci, Simone Filice, Shervin Malmasi, Oleg Rokhlenko |
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| Reinforcement Learning in the Wild: Scalable RL Dispatching Algorithm Deployed in Ridehailing Marketplace |
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| Generalized Deep Mixed Models |
Jun Shi, Chengming Jiang, Aman Gupta, Mingzhou Zhou, Yunbo Ouyang, Qiang Charles Xiao, Qingquan Song, Yi (Alice) Wu, Haichao Wei, Huiji Gao |
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Aseem Srivastava, Tharun Suresh, Sarah Peregrine Lord, Md. Shad Akhtar, Tanmoy Chakraborty |
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Yueyang Su, Di Yao, Xiaokai Chu, Wenbin Li, Jingping Bi, Shiwei Zhao, Runze Wu, Shize Zhang, Jianrong Tao, Hao Deng |
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| Representative Routes Discovery from Massive Trajectories |
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| CMMD: Cross-Metric Multi-Dimensional Root Cause Analysis |
Shifu Yan, Caihua Shan, Wenyi Yang, Bixiong Xu, Dongsheng Li, Lili Qiu, Jie Tong, Qi Zhang |
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| TAG: Toward Accurate Social Media Content Tagging with a Concept Graph |
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Chongsheng Zhang, Bin Wang, Ke Chen, Ruixing Zong, Bofeng Mo, Yi Men, George Almpanidis, Shanxiong Chen, Xiangliang Zhang |
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| Sparx: Distributed Outlier Detection at Scale |
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Johan Kok Zhi Kang, Suwei Yang, Suriya Venkatesan, Sien Yi Tan, Feng Cheng, Bingsheng He |
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| Medical Dialogue Response Generation with Pivotal Information Recalling |
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| Hyperbolic Neural Networks: Theory, Architectures and Applications |
Nurendra Choudhary, Nikhil Rao, Karthik Subbian, Srinivasan H. Sengamedu, Chandan K. Reddy |
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| Toward Graph Minimally-Supervised Learning |
Kaize Ding, Chuxu Zhang, Jie Tang, Nitesh V. Chawla, Huan Liu |
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| Frontiers of Graph Neural Networks with DIG |
Shuiwang Ji, Meng Liu, Yi Liu, Youzhi Luo, Limei Wang, Yaochen Xie, Zhao Xu, Haiyang Yu |
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Yu Meng, Jiaxin Huang, Yu Zhang, Jiawei Han |
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Brad Rees, Xiaoyun Wang, Joe Eaton, Onur Yilmaz, Rick Ratzel, Dominque LaSalle |
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Yuta Saito, Thorsten Joachims |
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| Towards Adversarial Learning: From Evasion Attacks to Poisoning Attacks |
Wentao Wang, Han Xu, Yuxuan Wan, Jie Ren, Jiliang Tang |
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| Graph Neural Networks in Life Sciences: Opportunities and Solutions |
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| Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection |
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| Anomaly Detection for Spatiotemporal Data in Action |
Guang Yang, Ninad Kulkarni, Paavani Dua, Dipika Khullar, Alex Anto Chirayath |
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| HoloViz: Visualization and Interactive Dashboards in Python |
Sophia Yang, Marc Skov Madsen, James A. Bednar |
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| AdKDD 2022 |
Abraham Bagherjeiran, Nemanja Djuric, Mihajlo Grbovic, KuangChih Lee, Kun Liu, Wei Liu, Linsey Pang, Vladan Radosavljevic, Suju Rajan, Kexin Xie |
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| Fragile Earth: AI for Climate Mitigation, Adaptation, and Environmental Justice |
Naoki Abe, Kathleen Buckingham, Bistra Dilkina, Emre Eftelioglu, Auroop R. Ganguly, James Hodson, Ramakrishnan Kannan, Rose Yu |
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| Data-driven Humanitarian Mapping and Policymaking: Toward Planetary-Scale Resilience, Equity, and Sustainability |
Snehalkumar (Neil) S. Gaikwad, Shankar Iyer, Dalton D. Lunga, Takahiro Yabe, Xiaofan Liang, Bhavani Ananthabhotla, Nikhil Behari, Sreelekha Guggilam, Guanghua Chi |
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| ANDEA: Anomaly and Novelty Detection, Explanation, and Accommodation |
Guansong Pang, Jundong Li, Anton van den Hengel, Longbing Cao, Thomas G. Dietterich |
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| Visualization in Data Science VDS @ KDD 2022 |
Claudia Plant, Nina C. Hubig, Junming Shao, Alvitta Ottley, Liang Gou, Torsten Möller, Adam Perer, Alexander Lex, Anamaria Crisan |
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| Deep Learning on Graphs: Methods and Applications (DLG-KDD2022) |
Lingfei Wu, Jian Pei, Jiliang Tang, Yinglong Xia, Xiaojie Guo |
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