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stat.ML2026
Bayesian Neural Networks via Dirichlet Process Priors on the Data-Generating Distribution for Randomized Value Functions in Deep Reinforcement Learning
Sumit Vashishtha
Bayesian neural networks (BNNs) offer a principled framework for quantifying epistemic uncertainty. However, standard weight-space priors yield complex, high-dimensional neural net…
stat.ML2026
Dimension-Free PAC-Bayes Generalization for Deep Neural Networks via Dirichlet Processes
Sumit Vashishtha
Classical PAC-Bayesian generalization theory places prior distributions over Euclidean parameter space , causing relative entropy to scale with network dim…
stat.ML2025
Leveraging priors on distribution functions for multi-arm bandits
Sumit Vashishtha, Odalric-Ambrym Maillard
We introduce Dirichlet Process Posterior Sampling (DPPS), a Bayesian non-parametric algorithm for multi-arm bandits based on Dirichlet Process (DP) priors. Like Thompson-sampling,…