10 citations · 24 across the 6 of their papers we have counts for
4 papers · 1 filter
Chebyshev-Cantelli PAC-Bayes-Bennett Inequality for the Weighted Majority Vote
Yi-Shan Wu, Andrés R. Masegosa, Stephan S. Lorenzen +2
We present a new second-order oracle bound for the expected risk of a weighted majority vote. The bound is based on a novel parametric form of the Chebyshev- Cantelli inequality (a…
Information Bottleneck: Exact Analysis of (Quantized) Neural Networks
Stephan Sloth Lorenzen, Christian Igel, Mads Nielsen
The information bottleneck (IB) principle has been suggested as a way to analyze deep neural networks. The learning dynamics are studied by inspecting the mutual information (MI) b…
Second Order PAC-Bayesian Bounds for the Weighted Majority Vote
Andrés R. Masegosa, Stephan S. Lorenzen, Christian Igel +1
We present a novel analysis of the expected risk of weighted majority vote in multiclass classification. The analysis takes correlation of predictions by ensemble members into acco…
On PAC-Bayesian Bounds for Random Forests
Stephan Sloth Lorenzen, Christian Igel, Yevgeny Seldin
Existing guarantees in terms of rigorous upper bounds on the generalization error for the original random forest algorithm, one of the most frequently used machine learning methods…