embed causality to enhance interpretability with T45 #179
hyunjimoon
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from here and main take away is three factors of Koopman spectral theory (representing nonlinear dynamics as infinite-dimensional linear operator acting on the space of all possible measurement functions of the system)
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Angie: To factorize effects with different time delays, how do you design categories (in the first place)? Do you have any literature to build on? I've been classifying parameters based on behavior/uncertainty etc in #7 (comment) Xinyu: There is blog post for this: https://spatiotemporal-data.github.io/posts/time_varying_model/ |
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dingyi, angie (2024-01-03)
angie's perception of dingyi's research plan
some questions
Q1. agent (:= body capable of affecting observation larger than its measurement precision) level for 3? gov? institution? corporation (uber, lyft, grab)? startup?
Q2. what other measures does 3 have?
xinyu, angie (2024-01-16)
Angie shared her interest in using proxy reward function to model entrepreneurial decision making which proceeds with three phases:
Xinyu shared his interest in state space model - sequence; (fft: TlogT, inverse (matrix)); imputation - change btw bf and af covid
interested in physics driven spatio-temporal learning (including manifold and laplacian)
Xinyu introduced Data-Driven Economic NMPC Using Reinforcement Learning
Angie asked difference with his approach with birthday problem Bayesian workflow which is relatively interpretable based on time series component decomposition; Xinyu answered space part is added ("spatio"-temporal) in his approach and improved efficiency from FFT
Xinyu shared interest in Physics-informed dynamic mode decomposition, Angie introduced Geometric Deep Learning Grids, Groups, Graphs, Geodesics, and Gauges, website, interview of the authors
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