Panov, Maxim

22 publications

ICLR 2026 Don't Throw Away Your Beams: Improving Consistency-Based Uncertainties in LLMs via Beam Search Ekaterina Fadeeva, Maiya Goloburda, Aleksandr Rubashevskii, Roman Vashurin, Artem Shelmanov, Preslav Nakov, Mrinmaya Sachan, Maxim Panov

ICLR 2026 Modeling the Density of Pixel-Level Self-Supervised Embeddings for Unsupervised Pathology Segmentation in Medical CT Mikhail Goncharov, Eugenia Soboleva, Daniil Ignatyev, Mariia Donskova, Mikhail Belyaev, Ivan Oseledets, Marina Munkhoeva, Maxim Panov

ICLR 2026 Neural Optimal Transport Meets Multivariate Conformal Prediction Vladimir Kondratyev, Alexander Fishkov, Mahmoud Hegazy, Nikita Kotelevskii, Rémi Flamary, Maxim Panov, Eric Moulines

NeurIPS 2025 CoCoA: A Minimum Bayes Risk Framework Bridging Confidence and Consistency for Uncertainty Quantification in LLMs Roman Vashurin, Maiya Goloburda, Albina Ilina, Aleksandr Rubashevskii, Preslav Nakov, Artem Shelmanov, Maxim Panov

ICLR 2025 From Risk to Uncertainty: Generating Predictive Uncertainty Measures via Bayesian Estimation Nikita Kotelevskii, Vladimir Kondratyev, Martin Takáč, Eric Moulines, Maxim Panov

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

ICML 2025 Rectifying Conformity Scores for Better Conditional Coverage Vincent Plassier, Alexander Fishkov, Victor Dheur, Mohsen Guizani, Souhaib Ben Taieb, Maxim Panov, Eric Moulines

ICLRW 2025 Sample-Focused Approach for Robust Uncertainty Quantification in LLMs Roman Vashurin, Maiya Goloburda, Preslav Nakov, Artem Shelmanov, Maxim Panov

IJCAI 2024 Dirichlet-Based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks Nikita Kotelevskii, Samuel Horváth, Karthik Nandakumar, Martin Takác, Maxim Panov

AISTATS 2024 Efficient Conformal Prediction Under Data Heterogeneity Vincent Plassier, Nikita Kotelevskii, Aleksandr Rubashevskii, Fedor Noskov, Maksim Velikanov, Alexander Fishkov, Samuel Horvath, Martin Takac, Eric Moulines, Maxim Panov

ICLR 2024 Generalization Error of Spectral Algorithms Maksim Velikanov, Maxim Panov, Dmitry Yarotsky

ICMLW 2024 Predictive Uncertainties Based on Proper Scoring Rules Nikita Kotelevskii, Maxim Panov

ICML 2023 Conformal Prediction for Federated Uncertainty Quantification Under Label Shift Vincent Plassier, Mehdi Makni, Aleksandr Rubashevskii, Eric Moulines, Maxim Panov

UAI 2023 Learning from Low Rank Tensor Data: A Random Tensor Theory Perspective Mohamed El Amine Seddik, Malik Tiomoko, Alexis Decurninge, Maxim Panov, Maxime Gauillaud

ACML 2023 Selective Nonparametric Regression via Testing Fedor Noskov, Alexander Fishkov, Maxim Panov

AISTATS 2022 Embedded Ensembles: Infinite Width Limit and Operating Regimes Maksim Velikanov, Roman V. Kail, Ivan Anokhin, Roman Vashurin, Maxim Panov, Alexey Zaytsev, Dmitry Yarotsky

NeurIPSW 2022 Distributional Deep Q-Learning with CVaR Regression Mastane Achab, Reda Alami, Yasser Abdelaziz Dahou Djilali, Kirill Fedyanin, Eric Moulines, Maxim Panov

NeurIPS 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

ICML 2021 Monte Carlo Variational Auto-Encoders Achille Thin, Nikita Kotelevskii, Arnaud Doucet, Alain Durmus, Eric Moulines, Maxim Panov

NeurIPSW 2021 Nonparametric Approach to Uncertainty Quantification for Deterministic Neural Networks Nikita Yurevich Kotelevskii, Alexander Fishkov, Kirill Fedyanin, Aleksandr Petiushko, Maxim Panov

IJCAI 2019 Deeper Connections Between Neural Networks and Gaussian Processes Speed-up Active Learning Evgenii Tsymbalov, Sergei Makarychev, Alexander Shapeev, Maxim Panov

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