Nikita Kotelevskii

19 papers A* 5A 2Journal 12

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Adaptive Temperature Scaling with Conformal Prediction. CoRR Nikita Kotelevskii, Mohsen Guizani, Eric Moulines, Maxim Panov
2025	A*	`conf`	From Risk to Uncertainty: Generating Predictive Uncertainty Measures via Bayesian Estimation. ICLR Nikita Kotelevskii, Vladimir Kondratyev, Martin Takác, Eric Moulines, Maxim Panov
2025	A	`conf`	Learning Confident Classifiers in the Presence of Label Noise. SDM Asma Ahmed Hashmi, Aigerim Zhumabayeva, Nikita Kotelevskii, Artem Agafonov, Mohammad Yaqub, Maxim Panov, Martin Takác
2025	J	`jnl`	Multidimensional Uncertainty Quantification via Optimal Transport. CoRR Nikita Kotelevskii, Maiya Goloburda, Vladimir Kondratyev, Alexander Fishkov, Mohsen Guizani, Eric Moulines, Maxim Panov
2025	J	`jnl`	Neural Optimal Transport Meets Multivariate Conformal Prediction. CoRR Vladimir Kondratyev, Alexander Fishkov, Nikita Kotelevskii, Mahmoud Hegazy, Rémi Flamary, Maxim Panov, Eric Moulines
2025	J	`jnl`	Uncertainty Quantification for Regression using Proper Scoring Rules. CoRR Alexander Fishkov, Kajetan Schweighofer, Mykyta Ielanskyi, Nikita Kotelevskii, Mohsen Guizani, Maxim Panov
2025	J	`jnl`	Who to Trust? Aggregating Client Knowledge in Logit-Based Federated Learning. CoRR Viktor Kovalchuk, Nikita Kotelevskii, Maxim Panov, Samuel Horváth, Martin Takác
2024	A*	`conf`	Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks. IJCAI Nikita Kotelevskii, Samuel Horváth, Karthik Nandakumar, Martin Takác, Maxim Panov
2024	A	`conf`	Efficient Conformal Prediction under Data Heterogeneity. AISTATS Vincent Plassier, Nikita Kotelevskii, Aleksandr Rubashevskii, Fedor Noskov, Maksim Velikanov, Alexander Fishkov, Samuel Horváth, Martin Takác, Eric Moulines, Maxim Panov
2024	J	`jnl`	Predictive Uncertainty Quantification via Risk Decompositions for Strictly Proper Scoring Rules. CoRR Nikita Kotelevskii, Maxim Panov
2023	J	`jnl`	Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks. CoRR Nikita Kotelevskii, Samuel Horváth, Karthik Nandakumar, Martin Takác, Maxim Panov
2023	J	`jnl`	Efficient Conformal Prediction under Data Heterogeneity. CoRR Vincent Plassier, Nikita Kotelevskii, Aleksandr Rubashevskii, Fedor Noskov, Maksim Velikanov, Alexander Fishkov, Samuel Horváth, Martin Takác, Eric Moulines, Maxim Panov
2022	A*	`conf`	FedPop: A Bayesian Approach for Personalised Federated Learning. NeurIPS Nikita Kotelevskii, Maxime Vono, Alain Durmus, Eric Moulines
2022	J	`jnl`	FedPop: A Bayesian Approach for Personalised Federated Learning. CoRR Nikita Kotelevskii, Maxime Vono, Eric Moulines, Alain Durmus
2022	J	`jnl`	NUQ: Nonparametric Uncertainty Quantification for Deterministic Neural Networks. CoRR Nikita Kotelevskii, Aleksandr Artemenkov, Kirill Fedyanin, Fedor Noskov, Alexander Fishkov, Aleksandr Petiushko, Maxim Panov
2022	A*	`conf`	Nonparametric Uncertainty Quantification for Single Deterministic Neural Network. NeurIPS Nikita Kotelevskii, Aleksandr Artemenkov, Kirill Fedyanin, Fedor Noskov, Alexander Fishkov, Artem Shelmanov, Artem Vazhentsev, Aleksandr Petiushko, Maxim Panov
2021	A*	`conf`	Monte Carlo Variational Auto-Encoders. ICML Achille Thin, Nikita Kotelevskii, Arnaud Doucet, Alain Durmus, Eric Moulines, Maxim Panov
2021	J	`jnl`	Monte Carlo Variational Auto-Encoders. CoRR Achille Thin, Nikita Kotelevskii, Arnaud Doucet, Alain Durmus, Eric Moulines, Maxim Panov
2020	J	`jnl`	MetFlow: A New Efficient Method for Bridging the Gap between Markov Chain Monte Carlo and Variational Inference. CoRR Achille Thin, Nikita Kotelevskii, Jean-Stanislas Denain, Léo Grinsztajn, Alain Durmus, Maxim Panov, Eric Moulines