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Nikita Kotelevskii
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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
372021
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
232022
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
16*2022
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
152020
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
62020
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
32020
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
22023
Predictive Uncertainty Quantification via Risk Decompositions for Strictly Proper Scoring Rules
N Kotelevskii, M Panov
arXiv preprint arXiv:2402.10727, 2024
12024
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
2024
Learning Confident Classifiers in the Presence of Label Noise
AA Hashmi, A Zhumabayeva, N Kotelevskii, A Agafonov, M Yaqub, ...
arXiv preprint arXiv:2301.00524, 2023
2023
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