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09/15/2022
Semi-Counterfactual Risk Minimization Via Neural Networks
Counterfactual risk minimization is a framework for offline policy optim...
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11/15/2021
Progress in Self-Certified Neural Networks
A learning method is self-certified if it uses all available data to sim...
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09/21/2021
Learning PAC-Bayes Priors for Probabilistic Neural Networks
Recent works have investigated deep learning models trained by optimisin...
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07/27/2021
On the Role of Optimization in Double Descent: A Least Squares Study
Empirically it has been observed that the performance of deep neural net...
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01/12/2021
A note on a confidence bound of Kuzborskij and Szepesvári
In an interesting recent work, Kuzborskij and Szepesvári derived a confi...
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12/18/2020
Upper and Lower Bounds on the Performance of Kernel PCA
Principal Component Analysis (PCA) is a popular method for dimension red...
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07/25/2020
Tighter risk certificates for neural networks
This paper presents empirical studies regarding training probabilistic n...
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06/23/2020
PAC-Bayes Analysis Beyond the Usual Bounds
We focus on a stochastic learning model where the learner observes a fin...
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06/22/2020
Logarithmic Pruning is All You Need
The Lottery Ticket Hypothesis is a conjecture that every large neural ne...
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06/12/2020
PAC-Bayes unleashed: generalisation bounds with unbounded losses
We present new PAC-Bayesian generalisation bounds for learning problems ...
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08/19/2019
PAC-Bayes with Backprop
We explore a method to train probabilistic neural networks by minimizing...
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06/18/2018