Fast Rates with Unbounded Losses
By Peter D. Grünwald, Nishant A. Mehta. 2016
26 citations recorded by the source corpus.
Subfields: Statistical Learning Theory
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In Statistical Learning Theory
Citations in the corpus
- PAC-Bayesian Theory Meets Bayesian Inference (2016, neutral): “However, despite the amount of work dedicated to this statistical learning theory—many authors improved the initial results [9, 29, 33, 43, 48] and/or generalized them for various machine learning setups [4, 14, 20, 28, 38, 44, 45, 46]—it is mostly used as a frequentist method.”
- Inconsistency of Bayesian Inference for Misspecified Linear Models, and a Proposal for Repairing It (2014, methodology): “It is very likely that the requirement of bounded density ratios can be dropped, cf. the developments of Grünwald and Mehta (2016); it is not so easy to drop the Cèsaro-replacement, but we suspect that this is an artifact of the proof technique.”
- Entropy-SGD optimizes the prior of a PAC-Bayes bound: Generalization properties of Entropy-SGD and data-dependent priors (2017, background): “(See Grünwald & Mehta (2016) for related work on excess risk bounds and further references).”
- Entropy-SGD optimizes the prior of a PAC-Bayes bound: Data-dependent PAC-Bayes priors via differential privacy (2017, background): “Similar connections have been made by Zhang (2006a), Zhang (2006b), Grünwald (2012), and Grünwald and Mehta (2016).”