Pavel Izmailov
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I’m a PhD student in Computer Science at NYU, working with Andrew Gordon Wilson. I am primarily interested in better understanding Deep Neural Networks and finding more efficient ways of training them. In particular my interests include loss surface analysis, optimization and regularization in deep learning. I am also excited about deep semi-supervised learning, Bayesian deep learning, generative models, Gassian processes and other topics.
In years 2017-2019 I was a PhD student in Operations Research and Information Engineering at Cornell University, after which I received an MSc degree and transferred to NYU. I received a BSc in applied math and computer science from the faculty of Computational Mathematics and Cybernetics of Lomonosov Moscow State University, where I was working in the Bayesian Methods Research Group under supervision of Dmitry Vetrov and Dmitry Kropotov.
In the summer of 2019 I completed a research internship in Amazon AWS in Palo Alto, working with Bernie Wang, Alex Smola.
You can contact me at pi390@nyu.edu and follow me on Twitter.
Links
Publications
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Subspace Inference for Bayesian Deep Learning
Uncertainty in Artificial Intelligence (UAI), 2019
[PDF, ArXiv, Code, Poster] -
A Simple Baseline for Bayesian Uncertainty in Deep Learning
Neural Information Processing Systems (NeurIPS), 2019
[PDF, ArXiv, Code] -
There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average
International Conference on Learning Representations (ICLR), 2019
[PDF, ArXiv, Code, Poster] -
Averaging Weights Leads to Wider Optima and Better Generalization
Uncertainty in Artificial Intelligence (UAI), 2018
Oral Presentation
[PDF, ArXiv, Code, Poster, Slides, PyTorch Blogpost, Towards Data Science Blogpost, fast.ai Blogpost] -
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs
Neural Information Processing Systems (NeurIPS), 2018
Spotlight Presentation
[PDF, ArXiv, Code, Poster, Slides, Video] -
Tensor Train decomposition on TensorFlow (T3F)
arXiv preprint, 2018
[PDF, ArXiv, Code] -
Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition
Artificial Intelligence and Statistics (AISTATS), 2018
Oral Presentation
[PDF, ArXiv, Code, Poster, Slides] -
Faster variational inducing input Gaussian process classification
Journal of Machine Learning and Data Analysis, 2017
[PDF, ArXiv]
Workshop Papers
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Semi-Supervised Learning with Normalizing Flows
ICML Workshop on Invertible Neural Nets and Normalizing Flows, 2019
[PDF, Poster] -
Invertible Convolutional Networks
ICML Workshop on Invertible Neural Nets and Normalizing Flows, 2019
Spotlight Presentation
[PDF, Poster, Slides] -
Subspace Inference for Bayesian Deep Learning
ICML Workshop on Uncertainty & Robustness in Deep Learning, 2019
Oral Presentation
[PDF, ArXiv, Code, Poster, Slides, Polina's Talk] -
Fast Uncertainty Estimates and Bayesian Model Averaging of DNNs
UAI Workshop: Uncertainty in Deep Learning, 2018
Oral Presentation
[PDF, Code, Poster, Slides] -
Improving Stability in Deep Reinforcement Learning with Weight Averaging
UAI Workshop: Uncertainty in Deep Learning, 2018
[PDF, Poster]