default search action
Maxim Panov
Maxim E. Panov
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
- [c40]Ekaterina Fadeeva, Aleksandr Rubashevskii, Artem Shelmanov, Sergey Petrakov, Haonan Li, Hamdy Mubarak, Evgenii Tsymbalov, Gleb Kuzmin, Alexander Panchenko, Timothy Baldwin, Preslav Nakov, Maxim Panov:
Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification. ACL (Findings) 2024: 9367-9385 - [c39]Vincent Plassier, Nikita Kotelevskii, Aleksandr Rubashevskii, Fedor Noskov, Maksim Velikanov, Alexander Fishkov, Samuel Horváth, Martin Takác, Eric Moulines, Maxim Panov:
Efficient Conformal Prediction under Data Heterogeneity. AISTATS 2024: 4879-4887 - [c38]Qing Li, Jiahui Geng, Chenyang Lyu, Derui Zhu, Maxim Panov, Fakhri Karray:
Reference-free Hallucination Detection for Large Vision-Language Models. EMNLP (Findings) 2024: 4542-4551 - [c37]Maksim Velikanov, Maxim Panov, Dmitry Yarotsky:
Generalization error of spectral algorithms. ICLR 2024 - [c36]Nikita Kotelevskii, Samuel Horváth, Karthik Nandakumar, Martin Takác, Maxim Panov:
Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks. IJCAI 2024: 7127-7135 - [e2]Dmitry I. Ignatov
, Michael Yu. Khachay
Recent Trends in Analysis of Images, Social Networks and Texts - 11th International Conference, AIST 2023, Yerevan, Armenia, September 28-30, Revised Selected Papers. Communications in Computer and Information Science 1905, Springer 2024, ISBN 978-3-031-67007-7 [contents] - [e1]Dmitry I. Ignatov
Analysis of Images, Social Networks and Texts - 11th International Conference, AIST 2023, Yerevan, Armenia, September 28-30, 2023, Revised Selected Papers. Lecture Notes in Computer Science 14486, Springer 2024, ISBN 978-3-031-54533-7 [contents] - [i34]Nikita Kotelevskii, Maxim Panov:
Predictive Uncertainty Quantification via Risk Decompositions for Strictly Proper Scoring Rules. CoRR abs/2402.10727 (2024) - [i33]Ekaterina Fadeeva, Aleksandr Rubashevskii, Artem Shelmanov, Sergey Petrakov, Haonan Li, Hamdy Mubarak, Evgenii Tsymbalov, Gleb Kuzmin, Alexander Panchenko, Timothy Baldwin, Preslav Nakov, Maxim Panov:
Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification. CoRR abs/2403.04696 (2024) - [i32]Maksim Velikanov, Maxim Panov, Dmitry Yarotsky:
Generalization error of spectral algorithms. CoRR abs/2403.11696 (2024) - [i31]Roman Vashurin, Ekaterina Fadeeva, Artem Vazhentsev, Akim Tsvigun, Daniil Vasilev, Rui Xing, Abdelrahman Boda Sadallah, Lyudmila Rvanova, Sergey Petrakov, Alexander Panchenko, Timothy Baldwin, Preslav Nakov, Maxim Panov, Artem Shelmanov:
Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph. CoRR abs/2406.15627 (2024) - [i30]Vincent Plassier, Alexander Fishkov, Maxim Panov, Eric Moulines:
Conditionally valid Probabilistic Conformal Prediction. CoRR abs/2407.01794 (2024) - [i29]Qing Li, Chenyang Lyu, Jiahui Geng, Derui Zhu, Maxim Panov, Fakhri Karray:
Reference-free Hallucination Detection for Large Vision-Language Models. CoRR abs/2408.05767 (2024) - [i28]Artem Vazhentsev, Ekaterina Fadeeva, Rui Xing, Alexander Panchenko, Preslav Nakov, Timothy Baldwin, Maxim Panov, Artem Shelmanov:
Unconditional Truthfulness: Learning Conditional Dependency for Uncertainty Quantification of Large Language Models. CoRR abs/2408.10692 (2024) - 2023
- [c35]Artem Vazhentsev, Akim Tsvigun, Roman Vashurin, Sergey Petrakov, Daniil Vasilev, Maxim Panov, Alexander Panchenko, Artem Shelmanov:
Efficient Out-of-Domain Detection for Sequence to Sequence Models. ACL (Findings) 2023: 1430-1454 - [c34]Artem Vazhentsev, Gleb Kuzmin, Akim Tsvigun, Alexander Panchenko, Maxim Panov, Mikhail Burtsev, Artem Shelmanov:
Hybrid Uncertainty Quantification for Selective Text Classification in Ambiguous Tasks. ACL (1) 2023: 11659-11681 - [c33]Fedor Noskov, Alexander Fishkov, Maxim Panov:
Selective Nonparametric Regression via Testing. ACML 2023: 1023-1038 - [c32]Vladimir Omelyusik
Distributed Bayesian Coresets. AIST (Supplement) 2023: 287-298 - [c31]Roman Kail, Kirill Fedyanin, Nikita Muravev, Alexey Zaytsev, Maxim Panov:
ScaleFace: Uncertainty-aware Deep Metric Learning. DSAA 2023: 1-10 - [c30]Ekaterina Fadeeva, Roman Vashurin, Akim Tsvigun, Artem Vazhentsev, Sergey Petrakov, Kirill Fedyanin, Daniil Vasilev, Elizaveta Goncharova, Alexander Panchenko, Maxim Panov, Timothy Baldwin, Artem Shelmanov:
LM-Polygraph: Uncertainty Estimation for Language Models. EMNLP (Demos) 2023: 446-461 - [c29]Emmanuel Akeweje
Real-Time Reconstruction of Complex Flow in Nanoporous Media: Linear vs Non-linear Decoding. ICCS (3) 2023: 580-594 - [c28]Vincent Plassier, Mehdi Makni, Aleksandr Rubashevskii, Eric Moulines, Maxim Panov:
Conformal Prediction for Federated Uncertainty Quantification Under Label Shift. ICML 2023: 27907-27947 - [c27]Gleb Kuzmin, Artem Vazhentsev, Artem Shelmanov, Xudong Han, Simon Suster, Maxim Panov, Alexander Panchenko, Timothy Baldwin:
Uncertainty Estimation for Debiased Models: Does Fairness Hurt Reliability? IJCNLP (1) 2023: 744-770 - [c26]Aleksandr Rubashevskii, Daria Kotova, Maxim Panov:
Scalable Batch Acquisition for Deep Bayesian Active Learning. SDM 2023: 739-747 - [c25]Mohamed El Amine Seddik, Malik Tiomoko, Alexis Decurninge, Maxim Panov, Maxime Guillaud:
Learning from Low Rank Tensor Data: A Random Tensor Theory Perspective. UAI 2023: 1858-1867 - [i27]Akim Tsvigun, Ivan Lysenko, Danila Sedashov, Ivan Lazichny, Eldar Damirov, Vladimir Karlov, Artemy Belousov, Leonid Sanochkin
Active Learning for Abstractive Text Summarization. CoRR abs/2301.03252 (2023) - [i26]Aleksandr Rubashevskii, Daria Kotova, Maxim Panov:
Scalable Batch Acquisition for Deep Bayesian Active Learning. CoRR abs/2301.05490 (2023) - [i25]Vincent Plassier, Mehdi Makni, Aleksandr Rubashevskii, Eric Moulines, Maxim Panov:
Conformal Prediction for Federated Uncertainty Quantification Under Label Shift. CoRR abs/2306.05131 (2023) - [i24]Fedor Noskov, Maxim Panov:
Optimal Estimation in Mixed-Membership Stochastic Block Models. CoRR abs/2307.14530 (2023) - [i23]Fedor Noskov
Selective Nonparametric Regression via Testing. CoRR abs/2309.16412 (2023) - [i22]Ekaterina Fadeeva, Roman Vashurin, Akim Tsvigun, Artem Vazhentsev, Sergey Petrakov, Kirill Fedyanin, Daniil Vasilev, Elizaveta Goncharova, Alexander Panchenko, Maxim Panov, Timothy Baldwin, Artem Shelmanov:
LM-Polygraph: Uncertainty Estimation for Language Models. CoRR abs/2311.07383 (2023) - [i21]Nikita Kotelevskii, Samuel Horváth, Karthik Nandakumar, Martin Takác, Maxim Panov:
Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks. CoRR abs/2312.11230 (2023) - [i20]Vincent Plassier, Nikita Kotelevskii, Aleksandr Rubashevskii, Fedor Noskov
Efficient Conformal Prediction under Data Heterogeneity. CoRR abs/2312.15799 (2023) - 2022
- [c24]Artem Vazhentsev, Gleb Kuzmin, Artem Shelmanov, Akim Tsvigun, Evgenii Tsymbalov, Kirill Fedyanin, Maxim Panov, Alexander Panchenko, Gleb Gusev, Mikhail Burtsev, Manvel Avetisian, Leonid Zhukov:
Uncertainty Estimation of Transformer Predictions for Misclassification Detection. ACL (1) 2022: 8237-8252 - [c23]Maksim Velikanov, Roman V. Kail, Ivan Anokhin, Roman Vashurin, Maxim Panov, Alexey Zaytsev, Dmitry Yarotsky:
Embedded Ensembles: infinite width limit and operating regimes. AISTATS 2022: 3138-3163 - [c22]Akim Tsvigun, Ivan Lysenko, Danila Sedashov, Ivan Lazichny, Eldar Damirov, Vladimir Karlov, Artemy Belousov, Leonid Sanochkin, Maxim Panov, Alexander Panchenko, Mikhail Burtsev, Artem Shelmanov:
Active Learning for Abstractive Text Summarization. EMNLP (Findings) 2022: 5128-5152 - [c21]Nikita Kotelevskii, Aleksandr Artemenkov, Kirill Fedyanin, Fedor Noskov, Alexander Fishkov, Artem Shelmanov, Artem Vazhentsev, Aleksandr Petiushko, Maxim Panov:
Nonparametric Uncertainty Quantification for Single Deterministic Neural Network. NeurIPS 2022 - [i19]Nikita Kotelevskii, Aleksandr Artemenkov, Kirill Fedyanin, Fedor Noskov, Alexander Fishkov, Aleksandr Petiushko, Maxim Panov:
NUQ: Nonparametric Uncertainty Quantification for Deterministic Neural Networks. CoRR abs/2202.03101 (2022) - [i18]Maksim Velikanov, Roman Kail, Ivan Anokhin, Roman Vashurin, Maxim Panov, Alexey Zaytsev, Dmitry Yarotsky:
Embedded Ensembles: Infinite Width Limit and Operating Regimes. CoRR abs/2202.12297 (2022) - [i17]Alexander Fishkov, Maxim Panov:
Scalable computation of prediction intervals for neural networks via matrix sketching. CoRR abs/2205.03194 (2022) - [i16]Gleb Bazhenov
Towards OOD Detection in Graph Classification from Uncertainty Estimation Perspective. CoRR abs/2206.10691 (2022) - [i15]Roman Kail, Kirill Fedyanin, Nikita Muravev
ScaleFace: Uncertainty-aware Deep Metric Learning. CoRR abs/2209.01880 (2022) - 2021
- [j2]Valentina Shumovskaia, Kirill Fedyanin
Linking bank clients using graph neural networks powered by rich transactional data. Int. J. Data Sci. Anal. 12(2): 135-145 (2021) - [c20]Kirill Fedyanin, Evgenii Tsymbalov, Maxim Panov:
Dropout Strikes Back: Improved Uncertainty Estimation via Diversity Sampling. AIST (Supplement) 2021: 125-137 - [c19]Alexander Fishkov, Maxim Panov:
Scalable Computation of Prediction Intervals for Neural Networks via Matrix Sketching. AIST 2021: 225-238 - [c18]Artem Shelmanov, Evgenii Tsymbalov, Dmitry Puzyrev, Kirill Fedyanin, Alexander Panchenko, Maxim Panov:
How Certain is Your Transformer? EACL 2021: 1833-1840 - [c17]Achille Thin, Nikita Kotelevskii, Arnaud Doucet, Alain Durmus, Eric Moulines, Maxim Panov:
Monte Carlo Variational Auto-Encoders. ICML 2021: 10247-10257 - [c16]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:
Retrospective on the SENSORIUM 2022 competition. NeurIPS (Competition and Demos) 2021: 314-333 - [c15]Georgii S. Novikov, Maxim E. Panov, Ivan V. Oseledets:
Tensor-train density estimation. UAI 2021: 1321-1331 - [i14]Achille Thin, Nikita Kotelevskii, Arnaud Doucet, Alain Durmus, Eric Moulines, Maxim Panov:
Monte Carlo Variational Auto-Encoders. CoRR abs/2106.15921 (2021) - [i13]Georgii S. Novikov, Maxim E. Panov, Ivan V. Oseledets:
Tensor-Train Density Estimation. CoRR abs/2108.00089 (2021) - [i12]Evgeny Lagutin, Daniil Selikhanovych, Achille Thin, Sergey Samsonov, Alexey Naumov, Denis Belomestny, Maxim Panov, Eric Moulines:
Ex2MCMC: Sampling through Exploration Exploitation. CoRR abs/2111.02702 (2021) - 2020
- [c14]Valentina Shumovskaia, Kirill Fedyanin, Ivan Sukharev
Linking Bank Clients using Graph Neural Networks Powered by Rich Transactional Data: Extended Abstract. DSAA 2020: 787-788 - [c13]Ivan Sukharev
EWS-GCN: Edge Weight-Shared Graph Convolutional Network for Transactional Banking Data. ICDM 2020: 1268-1273 - [c12]Aleksandr Artemenkov, Maxim Panov:
NCVis: Noise Contrastive Approach for Scalable Visualization. WWW 2020: 2941-2947 - [i11]Valentina Shumovskaia, Kirill Fedyanin, Ivan Sukharev, Dmitry Berestnev, Maxim Panov:
Linking Bank Clients using Graph Neural Networks Powered by Rich Transactional Data. CoRR abs/2001.08427 (2020) - [i10]Aleksandr Artemenkov, Maxim Panov:
NCVis: Noise Contrastive Approach for Scalable Visualization. CoRR abs/2001.11411 (2020) - [i9]Achille Thin, Nikita Kotelevskii, Jean-Stanislas Denain, Léo Grinsztajn, Alain Durmus, Maxim Panov, Eric Moulines:
MetFlow: A New Efficient Method for Bridging the Gap between Markov Chain Monte Carlo and Variational Inference. CoRR abs/2002.12253 (2020) - [i8]Evgenii Tsymbalov, Kirill Fedyanin, Maxim Panov:
Dropout Strikes Back: Improved Uncertainty Estimation via Diversity Sampled Implicit Ensembles. CoRR abs/2003.03274 (2020) - [i7]Ivan Sukharev, Valentina Shumovskaia, Kirill Fedyanin, Maxim Panov, Dmitry Berestnev:
EWS-GCN: Edge Weight-Shared Graph Convolutional Network for Transactional Banking Data. CoRR abs/2009.14588 (2020)
2010 – 2019
- 2019
- [c11]Marina Gomtsyan, Nikita Mokrov, Maxim Panov, Yury Yanovich:
Geometry-Aware Maximum Likelihood Estimation of Intrinsic Dimension. ACML 2019: 1126-1141 - [c10]Alexander Menshchikov
Data-Driven Body-Machine Interface for Drone Intuitive Control through Voice and Gestures. IECON 2019: 5602-5609 - [c9]Evgenii Tsymbalov, Sergei Makarychev, Alexander Shapeev, Maxim Panov:
Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active Learning. IJCAI 2019: 3599-3605 - [i6]Evgenii Tsymbalov, Sergei Makarychev, Alexander Shapeev, Maxim Panov:
Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active Learning. CoRR abs/1902.10350 (2019) - [i5]Marina Gomtsyan, Nikita Mokrov, Maxim Panov, Yury Yanovich:
Geometry-Aware Maximum Likelihood Estimation of Intrinsic Dimension. CoRR abs/1904.06151 (2019) - 2018
- [c8]Evgenii Tsymbalov, Maxim Panov, Alexander Shapeev:
Dropout-Based Active Learning for Regression. AIST 2018: 247-258 - [c7]Maxim Panov, Stanislav Tsepa:
Constructing Graph Node Embeddings via Discrimination of Similarity Distributions. ICDM Workshops 2018: 1050-1053 - [i4]Ivan Nazarov, Boris Shirokikh, Maria Burkina, Gennady Fedonin, Maxim Panov:
Sparse Group Inductive Matrix Completion. CoRR abs/1804.10653 (2018) - [i3]Evgenii Tsymbalov, Maxim Panov, Alexander Shapeev:
Dropout-based Active Learning for Regression. CoRR abs/1806.09856 (2018) - [i2]Stanislav Tsepa, Maxim Panov:
Constructing Graph Node Embeddings via Discrimination of Similarity Distributions. CoRR abs/1810.03032 (2018) - 2017
- [c6]Maxim Panov
Consistent Estimation of Mixed Memberships with Successive Projections. COMPLEX NETWORKS 2017: 53-64 - [c5]Nikita Mokrov, Maxim Panov
Simultaneous Matrix Diagonalization for Structural Brain Networks Classification. COMPLEX NETWORKS 2017: 1261-1270 - [c4]Dmitry Ermilov, Maxim Panov
Automatic Bitcoin Address Clustering. ICMLA 2017: 461-466 - 2016
- [j1]Mikhail Belyaev
GTApprox: Surrogate modeling for industrial design. Adv. Eng. Softw. 102: 29-39 (2016) - [c3]Konstantin Slavnov, Maxim Panov:
Overlapping Community Detection in Weighted Graphs: Matrix Factorization Approach. IDP 2016: 3-14 - [i1]Mikhail Belyaev, Evgeny Burnaev, Ermek Kapushev, Maxim Panov, Pavel V. Prikhodko, Dmitry P. Vetrov, Dmitry Yarotsky:
GTApprox: surrogate modeling for industrial design. CoRR abs/1609.01088 (2016) - 2015
- [c2]Evgeny Burnaev
Adaptive Design of Experiments Based on Gaussian Processes. SLDS 2015: 116-125 - 2011
- [c1]Maxim Panov
A Modified Neutral Point Method for Kernel-Based Fusion of Pattern-Recognition Modalities with Incomplete Data Sets. MCS 2011: 126-136
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from ,
, and
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and
to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from .
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-11-19 20:47 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by: