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Bayesian Model Selection, the Marginal Likelihood, and Generalization

23 February 2022

Papers citing "Bayesian Model Selection, the Marginal Likelihood, and Generalization"

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Title
Learning the Regularization Strength for Deep Fine-Tuning via a Data-Emphasized Variational Objective Ethan Harvey Mikhail Petrov Michael C. Hughes 114 0 0 28 Jan 2025
Identifying Information from Observations with Uncertainty and Novelty D. Prijatelj Timothy J. Ireland Walter J. Scheirer 104 0 0 16 Jan 2025
PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization Sanae Lotfi Marc Finzi Sanyam Kapoor Andres Potapczynski Micah Goldblum A. Wilson BDL MLT AI4CE 64 62 0 24 Nov 2022
A PAC-Bayesian Generalization Bound for Equivariant Networks Arash Behboodi Gabriele Cesa Taco S. Cohen 79 18 0 24 Oct 2022
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning Javier Antorán David Janz J. Allingham Erik A. Daxberger Riccardo Barbano Eric T. Nalisnick José Miguel Hernández-Lobato UQCV BDL 75 31 0 17 Jun 2022
On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification Sanyam Kapoor Wesley J. Maddox Pavel Izmailov A. Wilson BDL UD 76 51 0 30 Mar 2022
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations Alexander Immer Tycho F. A. van der Ouderaa Gunnar Rätsch Vincent Fortuin Mark van der Wilk BDL 87 48 0 22 Feb 2022
User-friendly introduction to PAC-Bayes bounds Pierre Alquier FedML 154 205 0 21 Oct 2021
The Neural Testbed: Evaluating Joint Predictions Ian Osband Zheng Wen S. Asghari Vikranth Dwaracherla Botao Hao M. Ibrahimi Dieterich Lawson Xiuyuan Lu Brendan O'Donoghue Benjamin Van Roy UQCV 68 22 0 09 Oct 2021
Differentiable Annealed Importance Sampling and the Perils of Gradient Noise Guodong Zhang Kyle Hsu Jianing Li Chelsea Finn Roger C. Grosse 64 40 0 21 Jul 2021
From Predictions to Decisions: The Importance of Joint Predictive Distributions Zheng Wen Ian Osband Chao Qin Xiuyuan Lu M. Ibrahimi Vikranth Dwaracherla Mohammad Asghari Benjamin Van Roy UQCV 49 22 0 20 Jul 2021
Laplace Redux -- Effortless Bayesian Deep Learning Erik A. Daxberger Agustinus Kristiadi Alexander Immer Runa Eschenhagen Matthias Bauer Philipp Hennig BDL UQCV 221 315 0 28 Jun 2021
Last Layer Marginal Likelihood for Invariance Learning Pola Schwobel Martin Jørgensen Sebastian W. Ober Mark van der Wilk BDL UQCV 75 30 0 14 Jun 2021
What Are Bayesian Neural Network Posteriors Really Like? Pavel Izmailov Sharad Vikram Matthew D. Hoffman A. Wilson UQCV BDL 72 387 0 29 Apr 2021
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning Alexander Immer Matthias Bauer Vincent Fortuin Gunnar Rätsch Mohammad Emtiyaz Khan BDL UQCV 124 109 0 11 Apr 2021
The Promises and Pitfalls of Deep Kernel Learning Sebastian W. Ober C. Rasmussen Mark van der Wilk UQCV BDL 65 109 0 24 Feb 2021
Bayesian Neural Network Priors Revisited Vincent Fortuin Adrià Garriga-Alonso Sebastian W. Ober F. Wenzel Gunnar Rätsch Richard Turner Mark van der Wilk Laurence Aitchison BDL UQCV 106 140 0 12 Feb 2021
A Bayesian Perspective on Training Speed and Model Selection Clare Lyle Lisa Schut Binxin Ru Y. Gal Mark van der Wilk 90 24 0 27 Oct 2020
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale Alexey Dosovitskiy Lucas Beyer Alexander Kolesnikov Dirk Weissenborn Xiaohua Zhai ... Matthias Minderer G. Heigold Sylvain Gelly Jakob Uszkoreit N. Houlsby ViT 667 41,369 0 22 Oct 2020
PAC $^m$ -Bayes: Narrowing the Empirical Risk Gap in the Misspecified Bayesian Regime Warren Morningstar Alexander A. Alemi Joshua V. Dillon 119 16 0 19 Oct 2020
On the role of data in PAC-Bayes bounds Gintare Karolina Dziugaite Kyle Hsu W. Gharbieh Gabriel Arpino Daniel M. Roy 72 78 0 19 Jun 2020
Marginal likelihood computation for model selection and hypothesis testing: an extensive review F. Llorente Luca Martino D. Delgado J. Lopez-Santiago 80 85 0 17 May 2020
On the Benefits of Invariance in Neural Networks Clare Lyle Mark van der Wilk Marta Z. Kwiatkowska Y. Gal Benjamin Bloem-Reddy OOD BDL 76 95 0 01 May 2020
Rethinking Parameter Counting in Deep Models: Effective Dimensionality Revisited Wesley J. Maddox Gregory W. Benton A. Wilson 116 61 0 04 Mar 2020
Bayesian Deep Learning and a Probabilistic Perspective of Generalization A. Wilson Pavel Izmailov UQCV BDL OOD 109 653 0 20 Feb 2020
Decision-Making with Auto-Encoding Variational Bayes Romain Lopez Pierre Boyeau Nir Yosef Michael I. Jordan Jeffrey Regier BDL 467 10,591 0 17 Feb 2020
Fantastic Generalization Measures and Where to Find Them Yiding Jiang Behnam Neyshabur H. Mobahi Dilip Krishnan Samy Bengio AI4CE 139 610 0 04 Dec 2019
On the marginal likelihood and cross-validation Edwin Fong Chris Holmes UQCV 102 111 0 21 May 2019
Fixup Initialization: Residual Learning Without Normalization Hongyi Zhang Yann N. Dauphin Tengyu Ma ODL AI4CE 94 351 0 27 Jan 2019
Learning Invariances using the Marginal Likelihood Mark van der Wilk Matthias Bauer S. T. John J. Hensman 75 86 0 16 Aug 2018
Non-Vacuous Generalization Bounds at the ImageNet Scale: A PAC-Bayesian Compression Approach Wenda Zhou Victor Veitch Morgane Austern Ryan P. Adams Peter Orbanz 75 215 0 16 Apr 2018
Improving Output Uncertainty Estimation and Generalization in Deep Learning via Neural Network Gaussian Processes Tomoharu Iwata Zoubin Ghahramani UQCV BDL 50 42 0 19 Jul 2017
On Calibration of Modern Neural Networks Chuan Guo Geoff Pleiss Yu Sun Kilian Q. Weinberger UQCV 299 5,855 0 14 Jun 2017
Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data Gintare Karolina Dziugaite Daniel M. Roy 106 819 0 31 Mar 2017
Variational Dropout Sparsifies Deep Neural Networks Dmitry Molchanov Arsenii Ashukha Dmitry Vetrov BDL 144 831 0 19 Jan 2017
Overcoming catastrophic forgetting in neural networks J. Kirkpatrick Razvan Pascanu Neil C. Rabinowitz J. Veness Guillaume Desjardins ... A. Grabska-Barwinska Demis Hassabis Claudia Clopath D. Kumaran R. Hadsell CLL 372 7,547 0 02 Dec 2016
Densely Connected Convolutional Networks Gao Huang Zhuang Liu Laurens van der Maaten Kilian Q. Weinberger PINN 3DV 775 36,861 0 25 Aug 2016
A Strongly Quasiconvex PAC-Bayesian Bound Niklas Thiemann Christian Igel Olivier Wintenberger Yevgeny Seldin 54 84 0 19 Aug 2016
PAC-Bayesian Theory Meets Bayesian Inference Pascal Germain Francis R. Bach Alexandre Lacoste Simon Lacoste-Julien 68 184 0 27 May 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 2.2K 194,322 0 10 Dec 2015
Sandwiching the marginal likelihood using bidirectional Monte Carlo Roger C. Grosse Zoubin Ghahramani Ryan P. Adams 56 62 0 08 Nov 2015
Deep Kernel Learning A. Wilson Zhiting Hu Ruslan Salakhutdinov Eric Xing BDL 248 888 0 06 Nov 2015
The Human Kernel A. Wilson Christoph Dann Christopher G. Lucas Eric Xing GP SyDa 42 68 0 26 Oct 2015
Variational Dropout and the Local Reparameterization Trick Diederik P. Kingma Tim Salimans Max Welling BDL 226 1,518 0 08 Jun 2015
Variational Inference with Normalizing Flows Danilo Jimenez Rezende S. Mohamed DRL BDL 318 4,197 0 21 May 2015
Optimizing Neural Networks with Kronecker-factored Approximate Curvature James Martens Roger C. Grosse ODL 104 1,023 0 19 Mar 2015
Going Deeper with Convolutions Christian Szegedy Wei Liu Yangqing Jia P. Sermanet Scott E. Reed Dragomir Anguelov D. Erhan Vincent Vanhoucke Andrew Rabinovich 483 43,685 0 17 Sep 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition Karen Simonyan Andrew Zisserman FAtt MDE 1.7K 100,479 0 04 Sep 2014
Automatic Construction and Natural-Language Description of Nonparametric Regression Models J. Lloyd David Duvenaud Roger C. Grosse J. Tenenbaum Zoubin Ghahramani 78 242 0 18 Feb 2014
Gaussian Processes for Big Data J. Hensman Nicolò Fusi Neil D. Lawrence GP 107 1,235 0 26 Sep 2013