PAC-Bayesian Bounds based on the Rényi Divergence

By Luc Bégin, Pascal Germain, François Laviolette, Jean-Francis Roy. 2016

96 citations recorded by the source corpus.

Subfields: Probabilistic & Bayesian Methods

How researchers see this paper

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In Probabilistic & Bayesian Methods

1. Jiaming Song
2. Stefano Ermon
3. Pierre Alquier
4. Yang Song
5. Chenlin Meng
6. Jonathan Ho
7. Diederik P. Kingma
8. Prafulla Dhariwal

Citations in the corpus

• PAC-Bayesian Theory Meets Bayesian Inference (2016, neutral): “We present below a different proof that follows the key steps of the very general PAC-Bayesian theorem presented in Bégin et al. [5, Theorem 4].”
• Bridging the Gap Between $f$-GANs and Wasserstein GANs (2019, background): “Proposition 2 also directly implies the “change of measure” inequality of Rényi α-divergences (Atar et al., 2015; Bégin et al., 2016).”
• User-friendly introduction to PAC-Bayes bounds (2021, methodology): “2 Bounds based on moment inequalities Based on techniques developped in [100, 29], [5] proved inequalities similar to PAC-Bayes bounds, that hold for heavy-tailed losses (they can also hold for non i.”