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Featured Co-authors

Evgeny Burnaev
135 publications
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106 publications
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63 publications
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62 publications
Alain Durmus
51 publications
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49 publications
Alexey Zaytsev
34 publications
Mikhail Belyaev
26 publications
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24 publications
Vladimir Spokoiny
23 publications
Paul M. Thompson
22 publications

∙ 07/26/2023

Optimal Estimation in Mixed-Membership Stochastic Block Models

Community detection is one of the most critical problems in modern netwo...

0 Fedor Noskov, et al. ∙

∙ 06/08/2023

Conformal Prediction for Federated Uncertainty Quantification Under Label Shift

Federated Learning (FL) is a machine learning framework where many clien...

0 Vincent Plassier, et al. ∙

∙ 01/13/2023

Scalable Batch Acquisition for Deep Bayesian Active Learning

In deep active learning, it is especially important to choose multiple e...

0 Aleksandr Rubashevskii, et al. ∙

∙ 01/09/2023

Active Learning for Abstractive Text Summarization

Construction of human-curated annotated datasets for abstractive text su...

0 Akim Tsvigun, et al. ∙

∙ 09/05/2022

ScaleFace: Uncertainty-aware Deep Metric Learning

The performance of modern deep learning-based systems dramatically depen...

0 Roman Kail, et al. ∙

∙ 05/06/2022

Scalable computation of prediction intervals for neural networks via matrix sketching

Accounting for the uncertainty in the predictions of modern neural netwo...

0 Alexander Fishkov, et al. ∙

∙ 02/24/2022

Embedded Ensembles: Infinite Width Limit and Operating Regimes

A memory efficient approach to ensembling neural networks is to share mo...

0 Maksim Velikanov, et al. ∙

∙ 02/07/2022

NUQ: Nonparametric Uncertainty Quantification for Deterministic Neural Networks

This paper proposes a fast and scalable method for uncertainty quantific...

0 Nikita Kotelevskii, et al. ∙

∙ 11/04/2021

Ex^2MCMC: Sampling through Exploration Exploitation

We develop an Explore-Exploit Markov chain Monte Carlo algorithm (Ex^2MC...

0 Evgeny Lagutin, et al. ∙

∙ 07/08/2021

Assigning Topics to Documents by Successive Projections

Topic models provide a useful tool to organize and understand the struct...

0 Olga Klopp, et al. ∙

∙ 06/30/2021

Monte Carlo Variational Auto-Encoders

Variational auto-encoders (VAE) are popular deep latent variable models ...

0 Achille Thin, et al. ∙

∙ 12/31/2020

Nonreversible MCMC from conditional invertible transforms: a complete recipe with convergence guarantees

Markov Chain Monte Carlo (MCMC) is a class of algorithms to sample compl...

0 Achille Thin, et al. ∙

∙ 09/30/2020

EWS-GCN: Edge Weight-Shared Graph Convolutional Network for Transactional Banking Data

In this paper, we discuss how modern deep learning approaches can be app...

0 Ivan Sukharev, et al. ∙

∙ 03/06/2020

Dropout Strikes Back: Improved Uncertainty Estimation via Diversity Sampled Implicit Ensembles

Modern machine learning models usually do not extrapolate well, i.e., th...

0 Evgenii Tsymbalov, et al. ∙

∙ 02/27/2020

MetFlow: A New Efficient Method for Bridging the Gap between Markov Chain Monte Carlo and Variational Inference

In this contribution, we propose a new computationally efficient method ...

76 Achille Thin, et al. ∙

∙ 01/30/2020

NCVis: Noise Contrastive Approach for Scalable Visualization

Modern methods for data visualization via dimensionality reduction, such...

14 Aleksandr Artemenkov, et al. ∙

∙ 01/23/2020

Linking Bank Clients using Graph Neural Networks Powered by Rich Transactional Data

Financial institutions obtain enormous amounts of data about user transa...

0 Valentina Shumovskaia, et al. ∙

∙ 10/14/2019

Accuracy of Gaussian approximation in nonparametric Bernstein – von Mises Theorem

The prominent Bernstein – von Mises (BvM) result claims that the posteri...

0 Vladimir Spokoiny, et al. ∙

∙ 04/12/2019

Geometry-Aware Maximum Likelihood Estimation of Intrinsic Dimension

The existing approaches to intrinsic dimension estimation usually are no...

0 Marina Gomtsyan, et al. ∙

∙ 02/27/2019

Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active Learning

Active learning methods for neural networks are usually based on greedy ...

0 Evgenii Tsymbalov, et al. ∙

∙ 10/06/2018

Constructing Graph Node Embeddings via Discrimination of Similarity Distributions

The problem of unsupervised learning node embeddings in graphs is one of...

0 Stanislav Tsepa, et al. ∙

∙ 06/26/2018

Dropout-based Active Learning for Regression

Active learning is relevant and challenging for high-dimensional regress...

0 Evgenii Tsymbalov, et al. ∙

∙ 04/27/2018

Sparse Group Inductive Matrix Completion

We consider the problem of inductive matrix completion under the assumpt...

0 Ivan Nazarov, et al. ∙

∙ 10/14/2017

Simultaneous Matrix Diagonalization for Structural Brain Networks Classification

This paper considers the problem of brain disease classification based o...

0 Nikita Mokrov, et al. ∙

∙ 09/05/2016

GTApprox: surrogate modeling for industrial design

We describe GTApprox - a new tool for medium-scale surrogate modeling in...

0 Mikhail Belyaev, et al. ∙

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