Izmailov, Pavel

24 publications

TMLR 2025 Reliable and Responsible Foundation Models Xinyu Yang, Junlin Han, Rishi Bommasani, Jinqi Luo, Wenjie Qu, Wangchunshu Zhou, Adel Bibi, Xiyao Wang, Jaehong Yoon, Elias Stengel-Eskin, Shengbang Tong, Lingfeng Shen, Rafael Rafailov, Runjia Li, Zhaoyang Wang, Yiyang Zhou, Chenhang Cui, Yu Wang, Wenhao Zheng, Huichi Zhou, Jindong Gu, Zhaorun Chen, Peng Xia, Tony Lee, Thomas P Zollo, Vikash Sehwag, Jixuan Leng, Jiuhai Chen, Yuxin Wen, Huan Zhang, Zhun Deng, Linjun Zhang, Pavel Izmailov, Pang Wei Koh, Yulia Tsvetkov, Andrew Gordon Wilson, Jiaheng Zhang, James Zou, Cihang Xie, Hao Wang, Philip Torr, Julian McAuley, David Alvarez-Melis, Florian Tramèr, Kaidi Xu, Suman Jana, Chris Callison-Burch, Rene Vidal, Filippos Kokkinos, Mohit Bansal, Beidi Chen, Huaxiu Yao

ICML 2024 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

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

ICLR 2023 Last Layer Re-Training Is Sufficient for Robustness to Spurious Correlations Polina Kirichenko, Pavel Izmailov, Andrew Gordon Wilson

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

ICML 2022 Bayesian Model Selection, the Marginal Likelihood, and Generalization Sanae Lotfi, Pavel Izmailov, Gregory Benton, Micah Goldblum, Andrew Gordon Wilson

ICMLW 2022 Last Layer Re-Training Is Sufficient for Robustness to Spurious Correlations Polina Kirichenko, Pavel Izmailov, Andrew Gordon Wilson

NeurIPS 2022 On Feature Learning in the Presence of Spurious Correlations Pavel Izmailov, Polina Kirichenko, Nate Gruver, Andrew G Wilson

NeurIPS 2022 On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification Sanyam Kapoor, Wesley J Maddox, Pavel Izmailov, Andrew G Wilson

NeurIPS 2021 Dangers of Bayesian Model Averaging Under Covariate Shift Pavel Izmailov, Patrick Nicholson, Sanae Lotfi, Andrew G Wilson

NeurIPS 2021 Does Knowledge Distillation Really Work? Samuel Stanton, Pavel Izmailov, Polina Kirichenko, Alexander A Alemi, Andrew G Wilson

ICML 2021 What Are Bayesian Neural Network Posteriors Really like? Pavel Izmailov, Sharad Vikram, Matthew D Hoffman, Andrew Gordon Gordon Wilson

NeurIPS 2020 Bayesian Deep Learning and a Probabilistic Perspective of Generalization Andrew G Wilson, Pavel Izmailov

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

NeurIPS 2020 Learning Invariances in Neural Networks from Training Data Gregory Benton, Marc Finzi, Pavel Izmailov, Andrew G Wilson

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

MLOSS 2020 Tensor Train Decomposition on TensorFlow (T3F) Alexander Novikov, Pavel Izmailov, Valentin Khrulkov, Michael Figurnov, Ivan Oseledets

NeurIPS 2020 Why Normalizing Flows Fail to Detect Out-of-Distribution Data Polina Kirichenko, Pavel Izmailov, Andrew G Wilson

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

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

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

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

NeurIPS 2018 Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry P Vetrov, Andrew G Wilson

AISTATS 2018 Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition Pavel Izmailov, Alexander Novikov, Dmitry Kropotov