Pavel Izmailov
21 papers · 2018–2024 · 7 conferences · across top CS/AI conferences
Achievements
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π§ Keyword Pioneer π£ Hot Topic Early Bird π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (11) π Conference Polyglot (7)
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Interdisciplinary Bridge
π§
Keyword Pioneer
π
Keyword Champion
(2)
ποΈ
Keyword Collector
(83)
β‘
Prolific Year
(6)
π
Conference Pioneer
π
Trend Setter
π
Century Club
(21)
π₯
Unstoppable
(7)
β
The Questioner
(2)
Conferences
NIPS (9)
ICML (6)
ICLR (2)
AISTATS (1)
CVPR (1)
JMLR (1)
UAI (1)
Top co-authors
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