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 (MCS, 2011)

Adaptive Design of Experiments Based on Gaussian Processes. - Evgeny Burnaev; Maxim Panov (SLDS, 2015)

Overlapping Community Detection in Weighted Graphs: Matrix Factorization Approach. - Konstantin Slavnov; Maxim Panov (IDP, 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)

Simultaneous Matrix Diagonalization for Structural Brain Networks Classification. - Nikita Mokrov; Maxim Panov; Boris A. Gutman; Joshua I. Faskowitz; Neda Jahanshad; Paul M. Thompson (COMPLEX NETWORKS, 2017)

Consistent Estimation of Mixed Memberships with Successive Projections. - Maxim Panov; Konstantin Slavnov; Roman Ushakov (COMPLEX NETWORKS, 2017)

Automatic Bitcoin Address Clustering. - Dmitry Ermilov; Maxim Panov; Yury Yanovich (ICMLA, 2017)

Dropout-Based Active Learning for Regression. - Evgenii Tsymbalov; Maxim Panov; Alexander Shapeev (AIST, 2018)

Constructing Graph Node Embeddings via Discrimination of Similarity Distributions. - Maxim Panov; Stanislav Tsepa (ICDM Workshops, 2018)

Sparse Group Inductive Matrix Completion. - Ivan Nazarov; Boris Shirokikh; Maria Burkina; Gennady Fedonin; Maxim Panov (CoRR, 2018)

Geometry-Aware Maximum Likelihood Estimation of Intrinsic Dimension. - Marina Gomtsyan; Nikita Mokrov; Maxim Panov; Yury Yanovich (ACML, 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 (IECON, 2019)

Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active Learning. - Evgenii Tsymbalov; Sergei Makarychev; Alexander Shapeev; Maxim Panov (IJCAI, 2019)

Linking Bank Clients using Graph Neural Networks Powered by Rich Transactional Data: Extended Abstract. - Valentina Shumovskaia; Kirill Fedyanin; Ivan Sukharev; Dmitry Berestnev; Maxim Panov (DSAA, 2020)

EWS-GCN: Edge Weight-Shared Graph Convolutional Network for Transactional Banking Data. - Ivan Sukharev; Valentina Shumovskaia; Kirill Fedyanin; Maxim Panov; Dmitry Berestnev (ICDM, 2020)

NCVis: Noise Contrastive Approach for Scalable Visualization. - Aleksandr Artemenkov; Maxim Panov (WWW, 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)

Dropout Strikes Back: Improved Uncertainty Estimation via Diversity Sampled Implicit Ensembles. - Evgenii Tsymbalov; Kirill Fedyanin; Maxim Panov (CoRR, 2020)

Dropout Strikes Back: Improved Uncertainty Estimation via Diversity Sampling. - Kirill Fedyanin; Evgenii Tsymbalov; Maxim Panov (AIST (Supplement), 2021)

Scalable Computation of Prediction Intervals for Neural Networks via Matrix Sketching. - Alexander Fishkov; Maxim Panov (AIST, 2021)

How Certain is Your Transformer? - Artem Shelmanov; Evgenii Tsymbalov; Dmitry Puzyrev; Kirill Fedyanin; Alexander Panchenko; Maxim Panov (EACL, 2021)

Monte Carlo Variational Auto-Encoders. - Achille Thin; Nikita Kotelevskii; Arnaud Doucet; Alain Durmus; Eric Moulines; Maxim Panov (ICML, 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; et al. (NeurIPS (Competition and Demos), 2021)

Tensor-train density estimation. - Georgii S. Novikov; Maxim E. Panov; Ivan V. Oseledets (UAI, 2021)

Ex2MCMC: Sampling through Exploration Exploitation. - Evgeny Lagutin; Daniil Selikhanovych; Achille Thin; Sergey Samsonov; Alexey Naumov; Denis Belomestny; Maxim Panov; Eric Moulines (CoRR, 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)

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 001; Manvel Avetisian; Leonid Zhukov (ACL (1), 2022)

Embedded Ensembles: infinite width limit and operating regimes. - Maksim Velikanov; Roman V. Kail; Ivan Anokhin; Roman Vashurin; Maxim Panov; Alexey Zaytsev 002; Dmitry Yarotsky (AISTATS, 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 001; Artem Shelmanov (EMNLP (Findings), 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 (NeurIPS, 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)

Towards OOD Detection in Graph Classification from Uncertainty Estimation Perspective. - Gleb Bazhenov; Sergei Ivanov 002; Maxim Panov; Alexey Zaytsev 002; Evgeny Burnaev (CoRR, 2022)

Hybrid Uncertainty Quantification for Selective Text Classification in Ambiguous Tasks. - Artem Vazhentsev; Gleb Kuzmin; Akim Tsvigun; Alexander Panchenko; Maxim Panov; Mikhail Burtsev 001; Artem Shelmanov (ACL (1), 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 (ACL (Findings), 2023)

Selective Nonparametric Regression via Testing. - Fedor Noskov; Alexander Fishkov; Maxim Panov (ACML, 2023)

Distributed Bayesian Coresets. - Vladimir Omelyusik; Maxim Panov (AIST (Supplement), 2023)

ScaleFace: Uncertainty-aware Deep Metric Learning. - Roman Kail; Kirill Fedyanin; Nikita Muravev; Alexey Zaytsev 002; Maxim Panov (DSAA, 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 (EMNLP (Demos), 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 (ICCS (3), 2023)

Conformal Prediction for Federated Uncertainty Quantification Under Label Shift. - Vincent Plassier; Mehdi Makni; Aleksandr Rubashevskii; Eric Moulines; Maxim Panov (ICML, 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 (IJCNLP (1), 2023)

Scalable Batch Acquisition for Deep Bayesian Active Learning. - Aleksandr Rubashevskii; Daria Kotova; Maxim Panov (SDM, 2023)

Learning from Low Rank Tensor Data: A Random Tensor Theory Perspective. - Mohamed El Amine Seddik; Malik Tiomoko; Alexis Decurninge; Maxim Panov; Maxime Guillaud (UAI, 2023)

Optimal Estimation in Mixed-Membership Stochastic Block Models. - Fedor Noskov; Maxim Panov (CoRR, 2023)

Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification. - Ekaterina Fadeeva; Aleksandr Rubashevskii; Artem Shelmanov; Sergey Petrakov; Haonan Li 002; Hamdy Mubarak; Evgenii Tsymbalov; Gleb Kuzmin; Alexander Panchenko; Timothy Baldwin; Preslav Nakov; Maxim Panov (ACL (Findings), 2024)

Automatic Adaptive Conformal Inference for Time Series Forecasting. - Artem Makhin; Maxim Panov (AIST, 2024)

Efficient Conformal Prediction under Data Heterogeneity. - Vincent Plassier; Nikita Kotelevskii; Aleksandr Rubashevskii; Fedor Noskov; Maksim Velikanov; Alexander Fishkov; Samuel Horváth; Martin Takác 001; Eric Moulines; Maxim Panov (AISTATS, 2024)

Reference-free Hallucination Detection for Large Vision-Language Models. - Qing Li 038; Jiahui Geng; Chenyang Lyu; Derui Zhu; Maxim Panov; Fakhri Karray (EMNLP (Findings), 2024)

Generalization error of spectral algorithms. - Maksim Velikanov; Maxim Panov; Dmitry Yarotsky (ICLR, 2024)

Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks. - Nikita Kotelevskii; Samuel Horváth; Karthik Nandakumar; Martin Takác 001; Maxim Panov (IJCAI, 2024)

Predictive Uncertainty Quantification via Risk Decompositions for Strictly Proper Scoring Rules. - Nikita Kotelevskii; Maxim Panov (CoRR, 2024)

Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph. - 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; et al. (CoRR, 2024)

Conditionally valid Probabilistic Conformal Prediction. - Vincent Plassier; Alexander Fishkov; Maxim Panov; Eric Moulines (CoRR, 2024)

Unconditional Truthfulness: Learning Conditional Dependency for Uncertainty Quantification of Large Language Models. - Artem Vazhentsev; Ekaterina Fadeeva; Rui Xing; Alexander Panchenko; Preslav Nakov; Timothy Baldwin; Maxim Panov; Artem Shelmanov (CoRR, 2024)

From Risk to Uncertainty: Generating Predictive Uncertainty Measures via Bayesian Estimation. - Nikita Kotelevskii; Vladimir Kondratyev; Martin Takác 001; Eric Moulines; Maxim Panov (ICLR, 2025)

Probabilistic Conformal Prediction with Approximate Conditional Validity. - Vincent Plassier; Alexander Fishkov; Mohsen Guizani; Maxim Panov; Eric Moulines (ICLR, 2025)

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models. - Artem Vazhentsev; Lyudmila Rvanova; Ivan Lazichny; Alexander Panchenko; Maxim Panov; Timothy Baldwin; Artem Shelmanov (NAACL (Long Papers), 2025)

Learning Confident Classifiers in the Presence of Label Noise. - Asma Ahmed Hashmi; Aigerim Zhumabayeva; Nikita Kotelevskii; Artem Agafonov; Mohammad Yaqub; Maxim Panov; Martin Takác 001 (SDM, 2025)

Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph. - Roman Vashurin; Ekaterina Fadeeva; Artem Vazhentsev; Lyudmila Rvanova; Daniil Vasilev; Akim Tsvigun; Sergey Petrakov; Rui Xing; Abdelrahman Boda Sadallah; Kirill Grishchenkov; Alexander Panchenko; Timothy Baldwin; et al. (Trans. Assoc. Comput. Linguistics, 2025)

CoCoA: A Generalized Approach to Uncertainty Quantification by Integrating Confidence and Consistency of LLM Outputs. - Roman Vashurin; Maiya Goloburda; Preslav Nakov; Artem Shelmanov; Maxim Panov (CoRR, 2025)

Screener: Self-supervised Pathology Segmentation Model for 3D Medical Images. - Mikhail Goncharov; Eugenia Soboleva; Maria Donskova; Ivan V. Oseledets; Marina Munkhoeva; Maxim Panov (CoRR, 2025)

Rectifying Conformity Scores for Better Conditional Coverage. - Vincent Plassier; Alexander Fishkov; Victor Dheur; Mohsen Guizani; Souhaib Ben Taieb; Maxim Panov; Eric Moulines (CoRR, 2025)

Uncertainty-aware abstention in medical diagnosis based on medical texts. - Artem Vazhentsev; Ivan Sviridov; Alvard Barseghyan; Gleb Kuzmin; Alexander Panchenko; Aleksandr Nesterov; Artem Shelmanov; Maxim Panov (CoRR, 2025)

Adaptive Temperature Scaling with Conformal Prediction. - Nikita Kotelevskii; Mohsen Guizani; Eric Moulines; Maxim Panov (CoRR, 2025)

UNCERTAINTY-LINE: Length-Invariant Estimation of Uncertainty for Large Language Models. - Roman Vashurin; Maiya Goloburda; Preslav Nakov; Maxim Panov (CoRR, 2025)

Uncertainty-Aware Attention Heads: Efficient Unsupervised Uncertainty Quantification for LLMs. - Artem Vazhentsev; Lyudmila Rvanova; Gleb Kuzmin; Ekaterina Fadeeva; Ivan Lazichny; Alexander Panchenko; Maxim Panov; Timothy Baldwin; Mrinmaya Sachan; Preslav Nakov; Artem Shelmanov (CoRR, 2025)

Faithfulness-Aware Uncertainty Quantification for Fact-Checking the Output of Retrieval Augmented Generation. - Ekaterina Fadeeva; Aleksandr Rubashevskii; Roman Vashurin; Shehzaad Dhuliawala; Artem Shelmanov; Timothy Baldwin; Preslav Nakov; Mrinmaya Sachan; Maxim Panov (CoRR, 2025)