Maxim Panov

According to our database, Maxim Panov authored at least 48 papers between 2011 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Generalization error of spectral algorithms.

Maksim Velikanov, Maxim Panov, Dmitry Yarotsky

CoRR, 2024

Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification.

Ekaterina Fadeeva, Aleksandr Rubashevskii, Artem Shelmanov, Sergey Petrakov, Haonan Li, Hamdy Mubarak, Evgenii Tsymbalov, Gleb Kuzmin, Alexander Panchenko, Timothy Baldwin, Preslav Nakov, Maxim Panov

CoRR, 2024

Predictive Uncertainty Quantification via Risk Decompositions for Strictly Proper Scoring Rules.

Nikita Kotelevskii, Maxim Panov

CoRR, 2024

2023

Efficient Conformal Prediction under Data Heterogeneity.

Vincent Plassier, Nikita Kotelevskii, Aleksandr Rubashevskii, Fedor Noskov, Maksim Velikanov, Alexander Fishkov, Samuel Horváth, Martin Takác, Eric Moulines, Maxim Panov

CoRR, 2023

Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks.

Nikita Kotelevskii, Samuel Horváth, Karthik Nandakumar, Martin Takác, Maxim Panov

CoRR, 2023

Selective Nonparametric Regression via Testing.

Fedor Noskov, Alexander Fishkov, Maxim Panov

CoRR, 2023

Optimal Estimation in Mixed-Membership Stochastic Block Models.

Fedor Noskov, Maxim Panov

CoRR, 2023

Learning from Low Rank Tensor Data: A Random Tensor Theory Perspective.

Mohamed El Amine Seddik, Malik Tiomoko, Alexis Decurninge, Maxim Panov, Maxime Guillaud

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Scalable Batch Acquisition for Deep Bayesian Active Learning.

Aleksandr Rubashevskii, Daria Kotova, Maxim Panov

Proceedings of the 2023 SIAM International Conference on Data Mining, 2023

Uncertainty Estimation for Debiased Models: Does Fairness Hurt Reliability?

Gleb Kuzmin, Artem Vazhentsev, Artem Shelmanov, Xudong Han, Simon Suster, Maxim Panov, Alexander Panchenko, Timothy Baldwin

Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, 2023

Conformal Prediction for Federated Uncertainty Quantification Under Label Shift.

Vincent Plassier, Mehdi Makni, Aleksandr Rubashevskii, Eric Moulines, Maxim Panov

Proceedings of the International Conference on Machine Learning, 2023

Real-Time Reconstruction of Complex Flow in Nanoporous Media: Linear vs Non-linear Decoding.

Emmanuel Akeweje, Andrey Olhin, Vsevolod Avilkin, Aleksey Vishnyakov, Maxim Panov

Proceedings of the Computational Science - ICCS 2023, 2023

LM-Polygraph: Uncertainty Estimation for Language Models.

Ekaterina Fadeeva, Roman Vashurin, Akim Tsvigun, Artem Vazhentsev, Sergey Petrakov, Kirill Fedyanin, Daniil Vasilev, Elizaveta Goncharova, Alexander Panchenko, Maxim Panov, Timothy Baldwin, Artem Shelmanov

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

ScaleFace: Uncertainty-aware Deep Metric Learning.

Roman Kail, Kirill Fedyanin, Nikita Muravev, Alexey Zaytsev, Maxim Panov

Proceedings of the 10th IEEE International Conference on Data Science and Advanced Analytics, 2023

Efficient Out-of-Domain Detection for Sequence to Sequence Models.

Artem Vazhentsev, Akim Tsvigun, Roman Vashurin, Sergey Petrakov, Daniil Vasilev, Maxim Panov, Alexander Panchenko, Artem Shelmanov

Proceedings of the Findings of the Association for Computational Linguistics: ACL 2023, 2023

Hybrid Uncertainty Quantification for Selective Text Classification in Ambiguous Tasks.

Artem Vazhentsev, Gleb Kuzmin, Akim Tsvigun, Alexander Panchenko, Maxim Panov, Mikhail Burtsev, Artem Shelmanov

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023

2022

Towards OOD Detection in Graph Classification from Uncertainty Estimation Perspective.

Gleb Bazhenov, Sergei Ivanov, Maxim Panov, Alexey Zaytsev, Evgeny Burnaev

CoRR, 2022

NUQ: Nonparametric Uncertainty Quantification for Deterministic Neural Networks.

Nikita Kotelevskii, Aleksandr Artemenkov, Kirill Fedyanin, Fedor Noskov, Alexander Fishkov, Aleksandr Petiushko, Maxim Panov

CoRR, 2022

Nonparametric Uncertainty Quantification for Single Deterministic Neural Network.

Nikita Kotelevskii, Aleksandr Artemenkov, Kirill Fedyanin, Fedor Noskov, Alexander Fishkov, Artem Shelmanov, Artem Vazhentsev, Aleksandr Petiushko, Maxim Panov

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Active Learning for Abstractive Text Summarization.

Akim Tsvigun, Ivan Lysenko, Danila Sedashov, Ivan Lazichny, Eldar Damirov, Vladimir Karlov, Artemy Belousov, Leonid Sanochkin, Maxim Panov, Alexander Panchenko, Mikhail Burtsev, Artem Shelmanov

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2022, 2022

Embedded Ensembles: infinite width limit and operating regimes.

Maksim Velikanov, Roman V. Kail, Ivan Anokhin, Roman Vashurin, Maxim Panov, Alexey Zaytsev, Dmitry Yarotsky

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Uncertainty Estimation of Transformer Predictions for Misclassification Detection.

Artem Vazhentsev, Gleb Kuzmin, Artem Shelmanov, Akim Tsvigun, Evgenii Tsymbalov, Kirill Fedyanin, Maxim Panov, Alexander Panchenko, Gleb Gusev, Mikhail Burtsev, Manvel Avetisian, Leonid Zhukov

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022

2021

Linking bank clients using graph neural networks powered by rich transactional data.

Valentina Shumovskaia, Kirill Fedyanin, Ivan Sukharev, Dmitry Berestnev, Maxim Panov

Int. J. Data Sci. Anal., 2021

Ex²MCMC: Sampling through Exploration Exploitation.

Evgeny Lagutin, Daniil Selikhanovych, Achille Thin, Sergey Samsonov, Alexey Naumov, Denis Belomestny, Maxim Panov, Eric Moulines

CoRR, 2021

Tensor-train density estimation.

Georgii S. Novikov, Maxim E. Panov, Ivan V. Oseledets

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Retrospective on the SENSORIUM 2022 competition.

Konstantin F. Willeke, Paul G. Fahey, Mohammad Bashiri, Laura Hansel, Christoph Blessing, Konstantin-Klemens Lurz, Max F. Burg, Santiago A. Cadena, Zhiwei Ding, Kayla Ponder, Taliah Muhammad, Saumil S. Patel, Kaiwen Deng, Yuanfang Guan, Yiqin Zhu, Kaiwen Xiao, Xiao Han, Simone Azeglio, Ulisse Ferrari, Peter Neri, Olivier Marre, Adrian Hoffmann, Kirill Fedyanin, Kirill Vishniakov, Maxim Panov, Subash Prakash, Kishan Naik, Kantharaju Narayanappa, Alexander S. Ecker, Andreas S. Tolias, Fabian H. Sinz

Proceedings of the NeurIPS 2022 Competition Track, 2021

Monte Carlo Variational Auto-Encoders.

Achille Thin, Nikita Kotelevskii, Arnaud Doucet, Alain Durmus, Eric Moulines, Maxim Panov

Proceedings of the 38th International Conference on Machine Learning, 2021

How Certain is Your Transformer?

Artem Shelmanov, Evgenii Tsymbalov, Dmitry Puzyrev, Kirill Fedyanin, Alexander Panchenko, Maxim Panov

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, 2021

Scalable Computation of Prediction Intervals for Neural Networks via Matrix Sketching.

Alexander Fishkov, Maxim Panov

Proceedings of the Analysis of Images, Social Networks and Texts, 2021

Dropout Strikes Back: Improved Uncertainty Estimation via Diversity Sampling.

Kirill Fedyanin, Evgenii Tsymbalov, Maxim Panov

Proceedings of the Recent Trends in Analysis of Images, Social Networks and Texts, 2021

2020

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

Evgenii Tsymbalov, Kirill Fedyanin, Maxim Panov

CoRR, 2020

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

Achille Thin, Nikita Kotelevskii, Jean-Stanislas Denain, Léo Grinsztajn, Alain Durmus, Maxim Panov, Eric Moulines

CoRR, 2020

NCVis: Noise Contrastive Approach for Scalable Visualization.

Aleksandr Artemenkov, Maxim Panov

Proceedings of the WWW '20: The Web Conference 2020, Taipei, Taiwan, April 20-24, 2020, 2020

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

Ivan Sukharev, Valentina Shumovskaia, Kirill Fedyanin, Maxim Panov, Dmitry Berestnev

Proceedings of the 20th IEEE International Conference on Data Mining, 2020

Linking Bank Clients using Graph Neural Networks Powered by Rich Transactional Data: Extended Abstract.

Valentina Shumovskaia, Kirill Fedyanin, Ivan Sukharev, Dmitry Berestnev, Maxim Panov

Proceedings of the 7th IEEE International Conference on Data Science and Advanced Analytics, 2020

2019

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

Evgenii Tsymbalov, Sergei Makarychev, Alexander Shapeev, Maxim Panov

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Data-Driven Body-Machine Interface for Drone Intuitive Control through Voice and Gestures.

Alexander Menshchikov, Dmitry Ermilov, I. Dranitsky, L. Kupchenko, Maxim Panov, Maxim V. Fedorov, Andrey Somov

Proceedings of the IECON 2019, 2019

Geometry-Aware Maximum Likelihood Estimation of Intrinsic Dimension.

Marina Gomtsyan, Nikita Mokrov, Maxim Panov, Yury Yanovich

Proceedings of The 11th Asian Conference on Machine Learning, 2019

2018

Sparse Group Inductive Matrix Completion.

Ivan Nazarov, Boris Shirokikh, Maria Burkina, Gennady Fedonin, Maxim Panov

CoRR, 2018

Constructing Graph Node Embeddings via Discrimination of Similarity Distributions.

Maxim Panov, Stanislav Tsepa

Proceedings of the 2018 IEEE International Conference on Data Mining Workshops, 2018

Dropout-Based Active Learning for Regression.

Evgenii Tsymbalov, Maxim Panov, Alexander Shapeev

Proceedings of the Analysis of Images, Social Networks and Texts, 2018

2017

Automatic Bitcoin Address Clustering.

Dmitry Ermilov, Maxim Panov, Yury Yanovich

Proceedings of the 16th IEEE International Conference on Machine Learning and Applications, 2017

Consistent Estimation of Mixed Memberships with Successive Projections.

Maxim Panov, Konstantin Slavnov, Roman Ushakov

Proceedings of the Complex Networks & Their Applications VI, 2017

Simultaneous Matrix Diagonalization for Structural Brain Networks Classification.

Nikita Mokrov, Maxim Panov, Boris A. Gutman, Joshua I. Faskowitz, Neda Jahanshad, Paul M. Thompson

Proceedings of the Complex Networks & Their Applications VI, 2017

2016

GTApprox: Surrogate modeling for industrial design.

Mikhail Belyaev, Evgeny Burnaev, Ermek Kapushev, Maxim Panov, Pavel V. Prikhodko, Dmitry P. Vetrov, Dmitry Yarotsky

Adv. Eng. Softw., 2016

Overlapping Community Detection in Weighted Graphs: Matrix Factorization Approach.

Konstantin Slavnov, Maxim Panov

Proceedings of the Intelligent Data Processing, 11th International Conference, 2016

2015

Adaptive Design of Experiments Based on Gaussian Processes.

Evgeny Burnaev, Maxim Panov

Proceedings of the Statistical Learning and Data Sciences - Third International Symposium, 2015

2011

A Modified Neutral Point Method for Kernel-Based Fusion of Pattern-Recognition Modalities with Incomplete Data Sets.

Maxim Panov, Alexander Tatarchuk, Vadim Mottl, David Windridge

Proceedings of the Multiple Classifier Systems - 10th International Workshop, 2011