Pavel Izmailov

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 46

Top Topics

Top Venues

Publications

Martin Marek, Brooks Paige, Pavel Izmailov
Can a Confident Prior Replace a Cold Posterior? CoRR (2024)
Lucas Beyer, Pavel Izmailov, Alexander Kolesnikov, Mathilde Caron, Simon Kornblith, Xiaohua Zhai, Matthias Minderer, Michael Tschannen, Ibrahim Alabdulmohsin, Filip Pavetic
FlexiViT: One Model for All Patch Sizes. CVPR (2023)
Shikai Qiu, Andres Potapczynski, Pavel Izmailov, Andrew Gordon Wilson
Simple and Fast Group Robustness by Automatic Feature Reweighting. ICML (2023)
Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, Bowen Baker, Leo Gao, Leopold Aschenbrenner, Yining Chen, Adrien Ecoffet, Manas Joglekar, Jan Leike, Ilya Sutskever, Jeff Wu
Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision. CoRR (2023)
Polina Kirichenko, Pavel Izmailov, Andrew Gordon Wilson
Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations. ICLR (2023)
Shikai Qiu, Andres Potapczynski, Pavel Izmailov, Andrew Gordon Wilson
Simple and Fast Group Robustness by Automatic Feature Reweighting. CoRR (2023)
Sanae Lotfi, Pavel Izmailov, Gregory W. Benton, Micah Goldblum, Andrew Gordon Wilson
Bayesian Model Selection, the Marginal Likelihood, and Generalization. CoRR (2022)
Sanae Lotfi, Pavel Izmailov, Gregory W. Benton, Micah Goldblum, Andrew Gordon Wilson
Bayesian Model Selection, the Marginal Likelihood, and Generalization. ICML (2022)
Pavel Izmailov, Polina Kirichenko, Nate Gruver, Andrew Gordon Wilson
On Feature Learning in the Presence of Spurious Correlations. CoRR (2022)
Lucas Beyer, Pavel Izmailov, Alexander Kolesnikov, Mathilde Caron, Simon Kornblith, Xiaohua Zhai, Matthias Minderer, Michael Tschannen, Ibrahim Alabdulmohsin, Filip Pavetic
FlexiViT: One Model for All Patch Sizes. CoRR (2022)
Polina Kirichenko, Pavel Izmailov, Andrew Gordon Wilson
Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations. CoRR (2022)
Pavel Izmailov, Polina Kirichenko, Nate Gruver, Andrew Gordon Wilson
On Feature Learning in the Presence of Spurious Correlations. NeurIPS (2022)
Sanyam Kapoor, Wesley J. Maddox, Pavel Izmailov, Andrew Gordon Wilson
On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification. NeurIPS (2022)
Sanyam Kapoor, Wesley J. Maddox, Pavel Izmailov, Andrew Gordon Wilson
On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification. CoRR (2022)
Pavel Izmailov, Sharad Vikram, Matthew D. Hoffman, Andrew Gordon Wilson
What Are Bayesian Neural Network Posteriors Really Like? CoRR (2021)
Pavel Izmailov, Patrick Nicholson, Sanae Lotfi, Andrew Gordon Wilson
Dangers of Bayesian Model Averaging under Covariate Shift. NeurIPS (2021)
Samuel Stanton, Pavel Izmailov, Polina Kirichenko, Alexander A. Alemi, Andrew Gordon Wilson
Does Knowledge Distillation Really Work? CoRR (2021)
Pavel Izmailov, Sharad Vikram, Matthew D. Hoffman, Andrew Gordon Wilson
What Are Bayesian Neural Network Posteriors Really Like? ICML (2021)
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
Evaluating Approximate Inference in Bayesian Deep Learning. NeurIPS (Competition and Demos) (2021)
Pavel Izmailov, Patrick Nicholson, Sanae Lotfi, Andrew Gordon Wilson
Dangers of Bayesian Model Averaging under Covariate Shift. CoRR (2021)
Samuel Stanton, Pavel Izmailov, Polina Kirichenko, Alexander A. Alemi, Andrew Gordon Wilson
Does Knowledge Distillation Really Work? NeurIPS (2021)
Gregory W. Benton, Marc Finzi, Pavel Izmailov, Andrew Gordon Wilson
Learning Invariances in Neural Networks from Training Data. NeurIPS (2020)
Pavel Izmailov, Polina Kirichenko, Marc Finzi, Andrew Gordon Wilson
Semi-Supervised Learning with Normalizing Flows. ICML (2020)
Andrew Gordon Wilson, Pavel Izmailov
Bayesian Deep Learning and a Probabilistic Perspective of Generalization. NeurIPS (2020)
Alexander Novikov, Pavel Izmailov, Valentin Khrulkov, Michael Figurnov, Ivan V. Oseledets
Tensor Train Decomposition on TensorFlow (T3F). J. Mach. Learn. Res. 21 (2020)
Andrew Gordon Wilson, Pavel Izmailov
Bayesian Deep Learning and a Probabilistic Perspective of Generalization. CoRR (2020)
Polina Kirichenko, Pavel Izmailov, Andrew Gordon Wilson
Why Normalizing Flows Fail to Detect Out-of-Distribution Data. CoRR (2020)
Marc Finzi, Samuel Stanton, Pavel Izmailov, Andrew Gordon Wilson
Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data. ICML (2020)
Marc Finzi, Samuel Stanton, Pavel Izmailov, Andrew Gordon Wilson
Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data. CoRR (2020)
Polina Kirichenko, Pavel Izmailov, Andrew Gordon Wilson
Why Normalizing Flows Fail to Detect Out-of-Distribution Data. NeurIPS (2020)
Gregory W. Benton, Marc Finzi, Pavel Izmailov, Andrew Gordon Wilson
Learning Invariances in Neural Networks. CoRR (2020)
Pavel Izmailov, Polina Kirichenko, Marc Finzi, Andrew Gordon Wilson
Semi-Supervised Learning with Normalizing Flows. CoRR (2019)
Pavel Izmailov, Wesley Maddox, Polina Kirichenko, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson
Subspace Inference for Bayesian Deep Learning. UAI (2019)
Wesley J. Maddox, Pavel Izmailov, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson
A Simple Baseline for Bayesian Uncertainty in Deep Learning. NeurIPS (2019)
Ben Athiwaratkun, Marc Finzi, Pavel Izmailov, Andrew Gordon Wilson
There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average. ICLR (Poster) (2019)
Wesley Maddox, Timur Garipov, Pavel Izmailov, Dmitry P. Vetrov, Andrew Gordon Wilson
A Simple Baseline for Bayesian Uncertainty in Deep Learning. CoRR (2019)
Pavel Izmailov, Wesley J. Maddox, Polina Kirichenko, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson
Subspace Inference for Bayesian Deep Learning. CoRR (2019)
Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry P. Vetrov, Andrew Gordon Wilson
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs. CoRR (2018)
Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry P. Vetrov, Andrew Gordon Wilson
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs. NeurIPS (2018)
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson
Averaging Weights Leads to Wider Optima and Better Generalization. CoRR (2018)
Alexander Novikov, Pavel Izmailov, Valentin Khrulkov, Michael Figurnov, Ivan V. Oseledets
Tensor Train decomposition on TensorFlow (T3F). CoRR (2018)
Pavel Izmailov, Alexander Novikov, Dmitry Kropotov
Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition. AISTATS (2018)
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson
Averaging Weights Leads to Wider Optima and Better Generalization. UAI (2018)
Ben Athiwaratkun, Marc Finzi, Pavel Izmailov, Andrew Gordon Wilson
Improving Consistency-Based Semi-Supervised Learning with Weight Averaging. CoRR (2018)
Pavel Izmailov, Alexander Novikov, Dmitry Kropotov
Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition. CoRR (2017)
Pavel Izmailov, Dmitry Kropotov
Faster variational inducing input Gaussian process classification. CoRR (2016)