Value-based deep RL shows TD-overfitting: large batches degrade Q-function accuracy for small networks but not large ones, enabling compute-optimal splits between model size and update frequency.
Auto-Encoding Knockoff Generator for FDR Controlled Variable Selection
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abstract
A new statistical procedure (Model-X \cite{candes2018}) has provided a way to identify important factors using any supervised learning method controlling for FDR. This line of research has shown great potential to expand the horizon of machine learning methods beyond the task of prediction, to serve the broader needs in scientific researches for interpretable findings. However, the lack of a practical and flexible method to generate knockoffs remains the major obstacle for wide application of Model-X procedure. This paper fills in the gap by proposing a model-free knockoff generator which approximates the correlation structure between features through latent variable representation. We demonstrate our proposed method can achieve FDR control and better power than two existing methods in various simulated settings and a real data example for finding mutations associated with drug resistance in HIV-1 patients.
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cs.LG 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
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Compute-Optimal Scaling for Value-Based Deep RL
Value-based deep RL shows TD-overfitting: large batches degrade Q-function accuracy for small networks but not large ones, enabling compute-optimal splits between model size and update frequency.