| Monte Carlo variational auto-encoders A Thin, N Kotelevskii, A Doucet, A Durmus, E Moulines, M Panov International Conference on Machine Learning, 10247-10257, 2021 | 61 | 2021 |
| Fedpop: A bayesian approach for personalised federated learning N Kotelevskii, M Vono, A Durmus, E Moulines Advances in Neural Information Processing Systems 35, 8687-8701, 2022 | 58 | 2022 |
| Nonparametric uncertainty quantification for single deterministic neural network N Kotelevskii, A Artemenkov, K Fedyanin, F Noskov, A Fishkov, ... Advances in Neural Information Processing Systems 35, 36308-36323, 2022 | 49* | 2022 |
| Predictive uncertainty quantification via risk decompositions for strictly proper scoring rules N Kotelevskii, M Panov CoRR, 2024 | 23* | 2024 |
| MetFlow: a new efficient method for bridging the gap between Markov chain Monte Carlo and variational inference A Thin, N Kotelevskii, JS Denain, L Grinsztajn, A Durmus, M Panov, ... arXiv preprint arXiv:2002.12253, 2020 | 20 | 2020 |
| Efficient conformal prediction under data heterogeneity V Plassier, N Kotelevskii, A Rubashevskii, F Noskov, M Velikanov, ... International Conference on Artificial Intelligence and Statistics, 4879-4887, 2024 | 13 | 2024 |
| Dirichlet-based uncertainty quantification for personalized federated learning with improved posterior networks N Kotelevskii, S Horváth, K Nandakumar, M Takáč, M Panov arXiv preprint arXiv:2312.11230, 2023 | 12 | 2023 |
| Nonreversible MCMC from conditional invertible transforms: a complete recipe with convergence guarantees A Thin, N Kotelevskii, C Andrieu, A Durmus, E Moulines, M Panov arXiv preprint arXiv:2012.15550, 2020 | 10 | 2020 |
| Learning confident classifiers in the presence of label noise AA Hashmi, A Zhumabayeva, N Kotelevskii, A Agafonov, M Yaqub, ... Proceedings of the 2025 SIAM International Conference on Data Mining (SDM …, 2025 | 4 | 2025 |
| Metropolized flow: from invertible flow to mcmc A Thin, N Kotelevskii, A Durmus, M Panov, E Moulines Proceedings of the ICML Workshop on Invertible Neural Networks, Normalizing …, 2020 | 3 | 2020 |
| Adaptive Temperature Scaling with Conformal Prediction N Kotelevskii, M Guizani, E Moulines, M Panov arXiv preprint arXiv:2505.15437, 2025 | 2 | 2025 |
| Method and system for determining uncertainty in personalized federated learning M PANOV, N KOTELEVSKII, M Takac, S HORVATH US Patent App. 18/738,622, 2025 | | 2025 |
| Uncertainty Quantification for Regression using Proper Scoring Rules A Fishkov, K Schweighofer, M Ielanskyi, N Kotelevskii, M Guizani, ... arXiv preprint arXiv:2509.26610, 2025 | | 2025 |
| Neural Optimal Transport Meets Multivariate Conformal Prediction V Kondratyev, A Fishkov, N Kotelevskii, M Hegazy, R Flamary, M Panov, ... arXiv preprint arXiv:2509.25444, 2025 | | 2025 |
| Multidimensional Uncertainty Quantification via Optimal Transport N Kotelevskii, M Goloburda, V Kondratyev, A Fishkov, M Guizani, ... arXiv preprint arXiv:2509.22380, 2025 | | 2025 |
| Who to Trust? Aggregating Client Knowledge in Logit-Based Federated Learning V Kovalchuk, N Kotelevskii, M Panov, S Horváth, M Takáč arXiv preprint arXiv:2509.15147, 2025 | | 2025 |
Maxim PanovAssistant Professor at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)Verified email at mbzuai.ac.ae
