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cs.LG2026
The Weight Gram Matrix Captures Sequential Feature Linearization in Deep Networks
Taehun Cha, Daniel Beaglehole, Adityanarayanan Radhakrishnan +1
Understanding how deep neural networks learn representations remains a central challenge in machine learning theory. In this work, we propose a feature-centric framework for analyz…
cs.LG2024
ABC3: Active Bayesian Causal Inference with Cohn Criteria in Randomized Experiments
Taehun Cha, Donghun Lee
In causal inference, randomized experiment is a de facto method to overcome various theoretical issues in observational study. However, the experimental design requires expensive c…