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ORCIDPavel Izmailov
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2020 – today
- 2024
[c22]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:
Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision. ICML 2024- [i24]Martin Marek, Brooks Paige, Pavel Izmailov:
Can a Confident Prior Replace a Cold Posterior? CoRR abs/2403.01272 (2024) - 2023
- [c21]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: 14496-14506 - [c20]Polina Kirichenko, Pavel Izmailov, Andrew Gordon Wilson:
Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations. ICLR 2023 - [c19]Shikai Qiu, Andres Potapczynski, Pavel Izmailov, Andrew Gordon Wilson:
Simple and Fast Group Robustness by Automatic Feature Reweighting. ICML 2023: 28448-28467 - [i23]Shikai Qiu, Andres Potapczynski, Pavel Izmailov, Andrew Gordon Wilson:
Simple and Fast Group Robustness by Automatic Feature Reweighting. CoRR abs/2306.11074 (2023) - [i22]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 abs/2312.09390 (2023) - 2022
- [c18]Sanae Lotfi, Pavel Izmailov, Gregory W. Benton, Micah Goldblum, Andrew Gordon Wilson:
Bayesian Model Selection, the Marginal Likelihood, and Generalization. ICML 2022: 14223-14247 - [c17]Pavel Izmailov, Polina Kirichenko, Nate Gruver, Andrew Gordon Wilson:
On Feature Learning in the Presence of Spurious Correlations. NeurIPS 2022 - [c16]Sanyam Kapoor, Wesley J. Maddox, Pavel Izmailov, Andrew Gordon Wilson:
On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification. NeurIPS 2022 - [i21]Sanae Lotfi, Pavel Izmailov, Gregory W. Benton, Micah Goldblum, Andrew Gordon Wilson:
Bayesian Model Selection, the Marginal Likelihood, and Generalization. CoRR abs/2202.11678 (2022) - [i20]Sanyam Kapoor, Wesley J. Maddox, Pavel Izmailov, Andrew Gordon Wilson:
On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification. CoRR abs/2203.16481 (2022) - [i19]Polina Kirichenko, Pavel Izmailov, Andrew Gordon Wilson:
Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations. CoRR abs/2204.02937 (2022) - [i18]Pavel Izmailov, Polina Kirichenko, Nate Gruver, Andrew Gordon Wilson:
On Feature Learning in the Presence of Spurious Correlations. CoRR abs/2210.11369 (2022) - [i17]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 abs/2212.08013 (2022) - 2021
- [c15]Pavel Izmailov, Sharad Vikram, Matthew D. Hoffman, Andrew Gordon Wilson:
What Are Bayesian Neural Network Posteriors Really Like? ICML 2021: 4629-4640 - [c14]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: 113-124 - [c13]Pavel Izmailov, Patrick Nicholson, Sanae Lotfi, Andrew Gordon Wilson:
Dangers of Bayesian Model Averaging under Covariate Shift. NeurIPS 2021: 3309-3322 - [c12]Samuel Stanton, Pavel Izmailov, Polina Kirichenko, Alexander A. Alemi, Andrew Gordon Wilson:
Does Knowledge Distillation Really Work? NeurIPS 2021: 6906-6919 - [i16]Pavel Izmailov, Sharad Vikram, Matthew D. Hoffman, Andrew Gordon Wilson:
What Are Bayesian Neural Network Posteriors Really Like? CoRR abs/2104.14421 (2021) - [i15]Samuel Stanton, Pavel Izmailov, Polina Kirichenko, Alexander A. Alemi, Andrew Gordon Wilson:
Does Knowledge Distillation Really Work? CoRR abs/2106.05945 (2021) - [i14]Pavel Izmailov, Patrick Nicholson, Sanae Lotfi, Andrew Gordon Wilson:
Dangers of Bayesian Model Averaging under Covariate Shift. CoRR abs/2106.11905 (2021) - 2020
- [j1]Alexander Novikov, Pavel Izmailov, Valentin Khrulkov, Michael Figurnov, Ivan V. Oseledets:
Tensor Train Decomposition on TensorFlow (T3F). J. Mach. Learn. Res. 21: 30:1-30:7 (2020) - [c11]Marc Finzi, Samuel Stanton, Pavel Izmailov, Andrew Gordon Wilson:
Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data. ICML 2020: 3165-3176 - [c10]Pavel Izmailov, Polina Kirichenko, Marc Finzi, Andrew Gordon Wilson:
Semi-Supervised Learning with Normalizing Flows. ICML 2020: 4615-4630 - [c9]Gregory W. Benton, Marc Finzi, Pavel Izmailov, Andrew Gordon Wilson:
Learning Invariances in Neural Networks from Training Data. NeurIPS 2020 - [c8]Polina Kirichenko, Pavel Izmailov, Andrew Gordon Wilson:
Why Normalizing Flows Fail to Detect Out-of-Distribution Data. NeurIPS 2020 - [c7]Andrew Gordon Wilson, Pavel Izmailov:
Bayesian Deep Learning and a Probabilistic Perspective of Generalization. NeurIPS 2020 - [i13]Andrew Gordon Wilson, Pavel Izmailov:
Bayesian Deep Learning and a Probabilistic Perspective of Generalization. CoRR abs/2002.08791 (2020) - [i12]Marc Finzi, Samuel Stanton, Pavel Izmailov, Andrew Gordon Wilson:
Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data. CoRR abs/2002.12880 (2020) - [i11]Polina Kirichenko, Pavel Izmailov, Andrew Gordon Wilson:
Why Normalizing Flows Fail to Detect Out-of-Distribution Data. CoRR abs/2006.08545 (2020) - [i10]Gregory W. Benton, Marc Finzi, Pavel Izmailov, Andrew Gordon Wilson:
Learning Invariances in Neural Networks. CoRR abs/2010.11882 (2020)
2010 – 2019
- 2019
- [c6]Ben Athiwaratkun, Marc Finzi, Pavel Izmailov, Andrew Gordon Wilson:
There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average. ICLR (Poster) 2019 - [c5]Wesley J. Maddox, Pavel Izmailov, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson:
A Simple Baseline for Bayesian Uncertainty in Deep Learning. NeurIPS 2019: 13132-13143 - [c4]Pavel Izmailov, Wesley J. Maddox, Polina Kirichenko, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson:
Subspace Inference for Bayesian Deep Learning. UAI 2019: 1169-1179 - [i9]Wesley J. Maddox, Timur Garipov, Pavel Izmailov, Dmitry P. Vetrov, Andrew Gordon Wilson:
A Simple Baseline for Bayesian Uncertainty in Deep Learning. CoRR abs/1902.02476 (2019) - [i8]Pavel Izmailov, Wesley J. Maddox, Polina Kirichenko, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson:
Subspace Inference for Bayesian Deep Learning. CoRR abs/1907.07504 (2019) - [i7]Pavel Izmailov, Polina Kirichenko, Marc Finzi, Andrew Gordon Wilson:
Semi-Supervised Learning with Normalizing Flows. CoRR abs/1912.13025 (2019) - 2018
- [c3]Pavel Izmailov, Alexander Novikov, Dmitry Kropotov:
Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition. AISTATS 2018: 726-735 - [c2]Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry P. Vetrov, Andrew Gordon Wilson:
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs. NeurIPS 2018: 8803-8812 - [c1]Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson:
Averaging Weights Leads to Wider Optima and Better Generalization. UAI 2018: 876-885 - [i6]Alexander Novikov, Pavel Izmailov, Valentin Khrulkov, Michael Figurnov, Ivan V. Oseledets:
Tensor Train decomposition on TensorFlow (T3F). CoRR abs/1801.01928 (2018) - [i5]Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry P. Vetrov, Andrew Gordon Wilson:
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs. CoRR abs/1802.10026 (2018) - [i4]Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson:
Averaging Weights Leads to Wider Optima and Better Generalization. CoRR abs/1803.05407 (2018) - [i3]Ben Athiwaratkun, Marc Finzi, Pavel Izmailov, Andrew Gordon Wilson:
Improving Consistency-Based Semi-Supervised Learning with Weight Averaging. CoRR abs/1806.05594 (2018) - 2017
- [i2]Pavel Izmailov, Alexander Novikov, Dmitry Kropotov:
Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition. CoRR abs/1710.07324 (2017) - 2016
- [i1]Pavel Izmailov, Dmitry Kropotov:
Faster variational inducing input Gaussian process classification. CoRR abs/1611.06132 (2016)
Coauthor Index
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