arXiv Machine Learning
Jun 18
Smoothness-Based Derandomization of PAC-Bayes Bounds
arXiv:2606. 19105v1 Announce Type: new Abstract: We study PAC-Bayes derandomization for smooth loss functions.
Smoothness-Based Derandomization of PAC-Bayes Bounds
We study PAC-Bayes derandomization for smooth loss functions. Our goal is to obtain generalization bounds that hold with high probability for deterministic predictors by exploiting smoothness properties of both the loss and the predictor class.
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Minimax-Optimal Generalization Bounds for Smooth Deep Neural Networks Trained by (Stochastic) Gradient Descent
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arXiv Machine Learning
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arXiv Machine Learning
Jul 7
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arXiv Machine Learning
Jun 19
A Solver-Free Training Method for Predict-then-Optimize
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arXiv Machine Learning
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arXiv Machine Learning
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arXiv Machine Learning
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