Pavel Izmailov
21 papers · 2018–2024 · 7 venues · across top CS/AI venues
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Achievements
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Research topics
Machine Learning
(21)
Deep Learning
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Optimization & Theory
(12)
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Artificial Intelligence
(7)
Learning Types Β· Machine Learning
(6)
Bayesian & Probabilistic Β· Machine Learning
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Bayesian & Probabilistic Β· Artificial Intelligence
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(1)
Keywords
image classification
(4)
variational inference
(3)
neural network
(3)
bayesian neural network
(3)
approximate inference
(2)
semi-supervised learning
(2)
bayesian deep learning
(2)
convolutional neural network
(2)
spurious correlation
(2)
normalizing flow
(2)
model averaging
(2)
domain generalization
(2)
bayesian inference
(2)
transfer learning
(2)
data augmentation
(2)
markov chain monte carlo
(2)
gaussian process
(2)
uncertainty quantification
(2)
posterior approximation
(2)
stochastic weight averaging
(2)
Papers
Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision
ICML 2024
FlexiViT: One Model for All Patch Sizes
CVPR 2023
Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations
ICLR 2023
Simple and Fast Group Robustness by Automatic Feature Reweighting
ICML 2023
Bayesian Model Selection, the Marginal Likelihood, and Generalization
ICML 2022
On Feature Learning in the Presence of Spurious Correlations
NeurIPS 2022
On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification
NeurIPS 2022
Dangers of Bayesian Model Averaging under Covariate Shift
NeurIPS 2021
Does Knowledge Distillation Really Work?
NeurIPS 2021
What Are Bayesian Neural Network Posteriors Really Like?
ICML 2021
Bayesian Deep Learning and a Probabilistic Perspective of Generalization
NeurIPS 2020
Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data
ICML 2020
Learning Invariances in Neural Networks from Training Data
NeurIPS 2020
Semi-Supervised Learning with Normalizing Flows
ICML 2020
Tensor Train Decomposition on TensorFlow (T3F)
JMLR 2020
Why Normalizing Flows Fail to Detect Out-of-Distribution Data
NeurIPS 2020
A Simple Baseline for Bayesian Uncertainty in Deep Learning
NeurIPS 2019
Subspace Inference for Bayesian Deep Learning
UAI 2019
There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average
ICLR 2019
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs
NeurIPS 2018
Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition
AISTATS 2018