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Previous research on PAC-Bayes learning theory has focused extensively on establishing tight upper bounds for test errors.
A pac analysis of a bayesian estimator
John Shawe-Taylor and Robert C Williamson · 1997
Earlier work this paper cites.
Some pac-bayesian theorems
David A McAllester · 1998
Earlier work this paper cites.
Pac-bayesian model averaging
David A McAllester · 1999
Earlier work this paper cites.
A pac-bayesian margin bound for linear classifiers: Why svms work
Ralf Herbrich and Thore Graepel · 2000
Earlier work this paper cites.
A note on the pac bayesian theorem
Andreas Maurer · 2004
Earlier work this paper cites.
Robust linear least squares regression
Jean-Yves Audibert and Olivier Catoni · 2011
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Earlier work this paper cites.
Weight uncertainty in neural network
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
Earlier work this paper cites.
Adding gradient noise improves learning for very deep networks
Arvind Neelakantan, Luke Vilnis, Quoc V Le, Ilya Sutskever, Lukasz Kaiser, Karol Kurach, and James Martens · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
Earlier work this paper cites.
Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking
Aleksandar Bojchevski and Stephan Günnemann · 2017
Earlier work this paper cites.
Computing nonvacuous generalization bounds for deep (stochastic) neural networks with many more parameters than training data
Gintare Karolina Dziugaite and Daniel M. Roy · 2017
Earlier work this paper cites.
Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
Earlier work this paper cites.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
Earlier work this paper cites.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
Earlier work this paper cites.
A strongly quasiconvex pac-bayesian bound
Niklas Thiemann, Christian Igel, Olivier Wintenberger, and Yevgeny Seldin · 2017
Earlier work this paper cites.
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
Earlier work this paper cites.
Simpler pac-bayesian bounds for hostile data
Pierre Alquier and Benjamin Guedj · 2018
Cited alongside, same era.
BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Cited alongside, same era.
Data-dependent pac-bayes priors via differential privacy
Gintare Karolina Dziugaite and Daniel M Roy · 2018
Cited alongside, same era.
Predict then propagate: Graph neural networks meet personalized pagerank
Johannes Gasteiger, Aleksandar Bojchevski, and Stephan Günnemann · 2018
Cited alongside, same era.
Towards understanding regularization in batch normalization
Ping Luo, Xinjiang Wang, Wenqi Shao, and Zhanglin Peng · 2018
Cited alongside, same era.
The implicit and explicit regularization effects of dropout
Colin Wei, Sham Kakade, and Tengyu Ma · 2020
Later among the works it cites.
User-friendly introduction to pac-bayes bounds
Pierre Alquier · 2021
Later among the works it cites.
Differentiable pac–bayes objectives with partially aggregated neural networks
Felix Biggs and Benjamin Guedj · 2021
Later among the works it cites.
Gradient descent on neural networks typically occurs at the edge of stability
Jeremy M Cohen, Simran Kaur, Yuanzhi Li, J Zico Kolter, and Ameet Talwalkar · 2021
Later among the works it cites.
Label noise sgd provably prefers flat global minimizers
Alex Damian, Tengyu Ma, and Jason D Lee · 2021
Later among the works it cites.
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Non-vacuous generalization bounds at the imagenet scale: a pac-bayesian compression approach
Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P Adams, and Peter Orbanz · 2018
Cited alongside, same era.
Pac-bayes under potentially heavy tails
Matthew Holland · 2019
Cited alongside, same era.
Fantastic generalization measures and where to find them
Yiding Jiang, Behnam Neyshabur, Hossein Mobahi, Dilip Krishnan, and Samy Bengio · 2019
Cited alongside, same era.
Efron-stein pac-bayesian inequalities
Ilja Kuzborskij and Csaba Szepesvári · 2019
Cited alongside, same era.
Dichotomize and generalize: Pac-bayesian binary activated deep neural networks
Gaël Letarte, Pascal Germain, Benjamin Guedj, and François Laviolette · 2019
Cited alongside, same era.
Uniform convergence may be unable to explain generalization in deep learning
Vaishnavh Nagarajan and J Zico Kolter · 2019
Cited alongside, same era.
Omar Rivasplata, Vikram M Tankasali, and Csaba Szepesvári · 2019
Cited alongside, same era.
Gintare Karolina Dziugaite, Kyle Hsu, Waseem Gharbieh, Gabriel Arpino, and Daniel Roy · 2021
Later among the works it cites.
Pac-bayes unleashed: Generalisation bounds with unbounded losses
Maxime Haddouche, Benjamin Guedj, Omar Rivasplata, and John Shawe-Taylor · 2021
Later among the works it cites.
Learning pac-bayes priors for probabilistic neural networks
Maria Perez-Ortiz, Omar Rivasplata, Benjamin Guedj, Matthew Gleeson, Jingyu Zhang, John Shawe-Taylor, Miroslaw Bober, and Josef Kittler · 2021
Later among the works it cites.
Tighter risk certificates for neural networks
María Pérez-Ortiz, Omar Rivasplata, John Shawe-Taylor, and Csaba Szepesvári · 2021
Later among the works it cites.
Implicit regularization in heavy-ball momentum accelerated stochastic gradient descent
Avrajit Ghosh, He Lyu, Xitong Zhang, and Rongrong Wang · 2022
Later among the works it cites.
A new characterization of the edge of stability based on a sharpness measure aware of batch gradient distribution
Sungyoon Lee and Cheongjae Jang · 2022
Later among the works it cites.
Anticorrelated noise injection for improved generalization
Antonio Orvieto, Hans Kersting, Frank Proske, Francis Bach, and Aurelien Lucchi · 2022
Later among the works it cites.
Matias D Cattaneo, Jason M Klusowski, and Boris Shigida · 2023
Closest in time.
Fantastic generalization measures are nowhere to be found, 2023
Michael Gastpar, Ido Nachum, Jonathan Shafer, and Thomas Weinberger · 2023
Closest in time.
Borja Rodríguez-Gálvez, Ragnar Thobaben, and Mikael Skoglund · 2023
Closest in time.
Learning via wasserstein-based high probability generalisation bounds
Paul Viallard, Maxime Haddouche, Umut Simsekli, and Benjamin Guedj · 2023
Closest in time.
Pac-bayes-chernoff bounds for unbounded losses
Ioar Casado, Luis A Ortega, Andrés R Masegosa, and Aritz Pérez · 2024
Closest in time.