Faster variational inducing input Gaussian process classification. - Pavel Izmailov; Dmitry Kropotov (CoRR, 2016)

Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition. - Pavel Izmailov; Alexander Novikov 001; Dmitry Kropotov (AISTATS, 2018)

Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs. - Timur Garipov; Pavel Izmailov; Dmitrii Podoprikhin; Dmitry P. Vetrov; Andrew Gordon Wilson (NeurIPS, 2018)

Averaging Weights Leads to Wider Optima and Better Generalization. - Pavel Izmailov; Dmitrii Podoprikhin; Timur Garipov; Dmitry P. Vetrov; Andrew Gordon Wilson (UAI, 2018)

Improving Consistency-Based Semi-Supervised Learning with Weight Averaging. - Ben Athiwaratkun; Marc Finzi; Pavel Izmailov; Andrew Gordon Wilson (CoRR, 2018)

There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average. - Ben Athiwaratkun; Marc Finzi; Pavel Izmailov; Andrew Gordon Wilson (ICLR (Poster), 2019)

A Simple Baseline for Bayesian Uncertainty in Deep Learning. - Wesley J. Maddox; Pavel Izmailov; Timur Garipov; Dmitry P. Vetrov; Andrew Gordon Wilson (NeurIPS, 2019)

Subspace Inference for Bayesian Deep Learning. - Pavel Izmailov; Wesley J. Maddox; Polina Kirichenko; Timur Garipov; Dmitry P. Vetrov; Andrew Gordon Wilson (UAI, 2019)

Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data. - Marc Finzi; Samuel Stanton; Pavel Izmailov; Andrew Gordon Wilson (ICML, 2020)

Semi-Supervised Learning with Normalizing Flows. - Pavel Izmailov; Polina Kirichenko; Marc Finzi; Andrew Gordon Wilson (ICML, 2020)

Learning Invariances in Neural Networks from Training Data. - Gregory W. Benton; Marc Finzi; Pavel Izmailov; Andrew Gordon Wilson (NeurIPS, 2020)

Why Normalizing Flows Fail to Detect Out-of-Distribution Data. - Polina Kirichenko; Pavel Izmailov; Andrew Gordon Wilson (NeurIPS, 2020)

Bayesian Deep Learning and a Probabilistic Perspective of Generalization. - Andrew Gordon Wilson; Pavel Izmailov (NeurIPS, 2020)

Learning Invariances in Neural Networks. - Gregory W. Benton; Marc Finzi; Pavel Izmailov; Andrew Gordon Wilson (CoRR, 2020)

Tensor Train Decomposition on TensorFlow (T3F). - Alexander Novikov 001; Pavel Izmailov; Valentin Khrulkov; Michael Figurnov; Ivan V. Oseledets (J. Mach. Learn. Res., 2020)

What Are Bayesian Neural Network Posteriors Really Like? - Pavel Izmailov; Sharad Vikram; Matthew D. Hoffman; Andrew Gordon Wilson (ICML, 2021)

Dangers of Bayesian Model Averaging under Covariate Shift. - Pavel Izmailov; Patrick Nicholson; Sanae Lotfi; Andrew Gordon Wilson (NeurIPS, 2021)

Does Knowledge Distillation Really Work? - Samuel Stanton; Pavel Izmailov; Polina Kirichenko; Alexander A. Alemi; Andrew Gordon Wilson (NeurIPS, 2021)

Evaluating Approximate Inference in Bayesian Deep Learning. - Andrew Gordon Wilson; Pavel Izmailov; Matthew D. Hoffman; Yarin Gal; Yingzhen Li; Melanie F. Pradier; Sharad Vikram; Andrew Y. K. Foong; Sanae Lotfi; Sebastian Farquhar (NeurIPS (Competition and Demos), 2021)

Bayesian Model Selection, the Marginal Likelihood, and Generalization. - Sanae Lotfi; Pavel Izmailov; Gregory W. Benton; Micah Goldblum; Andrew Gordon Wilson (ICML, 2022)

On Feature Learning in the Presence of Spurious Correlations. - Pavel Izmailov; Polina Kirichenko; Nate Gruver; Andrew Gordon Wilson (NeurIPS, 2022)

On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification. - Sanyam Kapoor; Wesley J. Maddox; Pavel Izmailov; Andrew Gordon Wilson (NeurIPS, 2022)

FlexiViT: One Model for All Patch Sizes. - Lucas Beyer; Pavel Izmailov; Alexander Kolesnikov 003; Mathilde Caron; Simon Kornblith; Xiaohua Zhai; Matthias Minderer; Michael Tschannen; Ibrahim Alabdulmohsin; Filip Pavetic (CVPR, 2023)

Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations. - Polina Kirichenko; Pavel Izmailov; Andrew Gordon Wilson (ICLR, 2023)

Simple and Fast Group Robustness by Automatic Feature Reweighting. - Shikai Qiu; Andres Potapczynski; Pavel Izmailov; Andrew Gordon Wilson (ICML, 2023)

Deconstructing Models and Methods in Deep Learning - Pavel Izmailov (2023)

Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision. - Collin Burns; Pavel Izmailov; Jan Hendrik Kirchner; Bowen Baker; Leo Gao; Leopold Aschenbrenner; Yining Chen; Adrien Ecoffet; Manas Joglekar; Jan Leike; Ilya Sutskever; Jeffrey Wu 003 (ICML, 2024)

Can a Confident Prior Replace a Cold Posterior? - Martin Marek; Brooks Paige; Pavel Izmailov (CoRR, 2024)