← Authors

Pavel Izmailov

21 papers · 2018–2024 · 7 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird 🌍 Conference Polyglot (7) πŸƒ Academic Marathon (6) πŸ—ΊοΈ Taxonomy Completionist (11) 🐝 Cross-Pollinator (13) πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer πŸ† Keyword Champion (2) πŸ—ƒοΈ Keyword Collector (83) ⚑ Prolific Year (6) πŸš€ Conference Pioneer πŸ“ˆ Trend Setter πŸ’Ž Century Club (21) πŸ”₯ Unstoppable (7) ❓ The Questioner (2)

Conferences

NIPS (9) ICML (6) ICLR (2) AISTATS (1) CVPR (1) JMLR (1) UAI (1)

Top co-authors

Andrew Gordon Wilson (8) Andrew G Wilson (8) Polina Kirichenko (6) Marc Finzi (4) Timur Garipov (3) Samuel Stanton (2) Dmitry P Vetrov (2) Sanae Lotfi (2) Gregory Benton (2) Alexander Novikov (2)

Keywords

variational inference (3) bayesian neural network (3) deep ensemble (2) convolutional neural network (2) approximate inference (2) neural network (2) model averaging (2) markov chain monte carlo (2) image classification (2) normalizing flow (2) posterior approximation (2) domain generalization (2) bayesian deep learning (2) group robustness (2) data augmentation (2) bayesian inference (2) gaussian process (2) uncertainty quantification (2) transfer learning (1) semi-supervised learning (1)

Papers

Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision ICML 2024
FlexiViT: One Model for All Patch Sizes CVPR 2023
Simple and Fast Group Robustness by Automatic Feature Reweighting ICML 2023
Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations ICLR 2023
Bayesian Model Selection, the Marginal Likelihood, and Generalization ICML 2022
On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification NIPS 2022
On Feature Learning in the Presence of Spurious Correlations NIPS 2022
What Are Bayesian Neural Network Posteriors Really Like? ICML 2021
Dangers of Bayesian Model Averaging under Covariate Shift NIPS 2021
Does Knowledge Distillation Really Work? NIPS 2021
Semi-Supervised Learning with Normalizing Flows ICML 2020
Learning Invariances in Neural Networks from Training Data NIPS 2020
Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data ICML 2020
Bayesian Deep Learning and a Probabilistic Perspective of Generalization NIPS 2020
Tensor Train Decomposition on TensorFlow (T3F) JMLR 2020
Why Normalizing Flows Fail to Detect Out-of-Distribution Data NIPS 2020
Subspace Inference for Bayesian Deep Learning UAI 2019
A Simple Baseline for Bayesian Uncertainty in Deep Learning NIPS 2019
There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average ICLR 2019
Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition AISTATS 2018
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs NIPS 2018