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)

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

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)

FedPop: A Bayesian Approach for Personalised Federated Learning. - Nikita Kotelevskii; Maxime Vono; Alain Durmus; Eric Moulines (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)

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)

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)

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

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