30 citations · 125 across the 26 of their papers we have counts for
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stat.ML2021
Self-Bounding Majority Vote Learning Algorithms by the Direct Minimization of a Tight PAC-Bayesian C-Bound
Paul Viallard, Pascal Germain, Amaury Habrard +1
In the PAC-Bayesian literature, the C-Bound refers to an insightful relation between the risk of a majority vote classifier (under the zero-one loss) and the first two moments of i…
stat.ML2021★ 1 cited
A General Framework for the Practical Disintegration of PAC-Bayesian Bounds
Paul Viallard, Pascal Germain, Amaury Habrard +1
PAC-Bayesian bounds are known to be tight and informative when studying the generalization ability of randomized classifiers. However, they require a loose and costly derandomizati…