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PAC-Bayes Learning Bounds for Sample-Dependent Priors
Pranjal Awasthi · Satyen Kale · Stefani Karp · Mehryar Mohri

Tue Dec 08 09:00 AM -- 11:00 AM (PST) @ Poster Session 1 #436

We present a series of new PAC-Bayes learning guarantees for randomized algorithms with sample-dependent priors. Our most general bounds make no assumption on the priors and are given in terms of certain covering numbers under the infinite-Renyi divergence and the L1 distance. We show how to use these general bounds to derive leaning bounds in the setting where the sample-dependent priors obey an infinite-Renyi divergence or L1-distance sensitivity condition. We also provide a flexible framework for computing PAC-Bayes bounds, under certain stability assumptions on the sample-dependent priors, and show how to use this framework to give more refined bounds when the priors satisfy an infinite-Renyi divergence sensitivity condition.

Author Information

Pranjal Awasthi (Google/Rutgers University)
Satyen Kale (Google)
Stefani Karp (Google/CMU)
Mehryar Mohri (Google Research & Courant Institute of Mathematical Sciences)

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