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Found 9 papers, 5 papers with code
PAC-Bayes-Chernoff bounds for unbounded losses
This result can be understood as a PAC-Bayesian version of the Cram\'er-Chernoff bound.
If there is no underfitting, there is no Cold Posterior Effect
The cold posterior effect (CPE) (Wenzel et al., 2020) in Bayesian deep learning shows that, for posteriors with a temperature $T<1$, the resulting posterior predictive could have better performances than the Bayesian posterior ($T=1$).
PAC-Chernoff Bounds: Understanding Generalization in the Interpolation Regime
In this paper, we present a distribution-dependent PAC-Chernoff bound that is perfectly tight for interpolators even under overparametrized model classes.
Diversity and Generalization in Neural Network Ensembles
In this work, we combine and expand previously published results in a theoretically sound framework that describes the relationship between diversity and ensemble performance for a wide range of ensemble methods.
Chebyshev-Cantelli PAC-Bayes-Bennett Inequality for the Weighted Majority Vote
The bound is based on a novel parametric form of the Chebyshev- Cantelli inequality (a. k. a.
Second Order PAC-Bayesian Bounds for the Weighted Majority Vote
We present a novel analysis of the expected risk of weighted majority vote in multiclass classification.
InferPy: Probabilistic Modeling with Deep Neural Networks Made Easy
InferPy is a Python package for probabilistic modeling with deep neural networks.
Probabilistic Models with Deep Neural Networks
Recent advances in statistical inference have significantly expanded the toolbox of probabilistic modeling.
AMIDST: a Java Toolbox for Scalable Probabilistic Machine Learning
The AMIDST Toolbox is a software for scalable probabilistic machine learning with a spe- cial focus on (massive) streaming data.
