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ORCIDKirill Neklyudov
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2020 – today
- 2024
[c14]Wu Lin, Felix Dangel, Runa Eschenhagen, Kirill Neklyudov, Agustinus Kristiadi, Richard E. Turner, Alireza Makhzani:
Structured Inverse-Free Natural Gradient Descent: Memory-Efficient & Numerically-Stable KFAC. ICML 2024- [c13]Kirill Neklyudov, Rob Brekelmans, Alexander Tong, Lazar Atanackovic, Qiang Liu, Alireza Makhzani:
A Computational Framework for Solving Wasserstein Lagrangian Flows. ICML 2024 - [i17]Lingkai Kong, Yuanqi Du, Wenhao Mu, Kirill Neklyudov, Valentin De Bortol, Haorui Wang, Dongxia Wu, Aaron Ferber, Yi-An Ma, Carla P. Gomes, Chao Zhang:
Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints. CoRR abs/2402.18012 (2024) - [i16]Haorui Wang, Marta Skreta, Cher-Tian Ser, Wenhao Gao, Lingkai Kong, Felix Streith-Kalthoff, Chenru Duan, Yuchen Zhuang, Yue Yu, Yanqiao Zhu, Yuanqi Du, Alán Aspuru-Guzik, Kirill Neklyudov, Chao Zhang:
Efficient Evolutionary Search Over Chemical Space with Large Language Models. CoRR abs/2406.16976 (2024) - [i15]Lazar Atanackovic, Xi Zhang, Brandon Amos, Mathieu Blanchette, Leo J. Lee, Yoshua Bengio, Alexander Tong, Kirill Neklyudov:
Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold. CoRR abs/2408.14608 (2024) - [i14]Yuanqi Du, Michael Plainer, Rob Brekelmans, Chenru Duan, Frank Noé, Carla P. Gomes, Alán Aspuru-Guzik, Kirill Neklyudov:
Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling. CoRR abs/2410.07974 (2024) - 2023
- [c12]Kirill Neklyudov
, Rob Brekelmans, Daniel Severo, Alireza Makhzani:
Action Matching: Learning Stochastic Dynamics from Samples. ICML 2023: 25858-25889 - [c11]Kirill Neklyudov, Jannes Nys, Luca A. Thiede, Juan Carrasquilla, Qiang Liu, Max Welling, Alireza Makhzani:
Wasserstein Quantum Monte Carlo: A Novel Approach for Solving the Quantum Many-Body Schrödinger Equation. NeurIPS 2023 - [i13]Juan Carrasquilla, Mohamed Hibat-Allah, Estelle M. Inack, Alireza Makhzani, Kirill Neklyudov, Graham W. Taylor, Giacomo Torlai:
Quantum HyperNetworks: Training Binary Neural Networks in Quantum Superposition. CoRR abs/2301.08292 (2023) - [i12]Kirill Neklyudov, Jannes Nys, Luca A. Thiede, Juan Carrasquilla, Qiang Liu, Max Welling, Alireza Makhzani:
Wasserstein Quantum Monte Carlo: A Novel Approach for Solving the Quantum Many-Body Schrödinger Equation. CoRR abs/2307.07050 (2023) - [i11]Kirill Neklyudov, Rob Brekelmans, Alexander Tong, Lazar Atanackovic, Qiang Liu, Alireza Makhzani:
A Computational Framework for Solving Wasserstein Lagrangian Flows. CoRR abs/2310.10649 (2023) - [i10]Wu Lin, Felix Dangel, Runa Eschenhagen, Kirill Neklyudov, Agustinus Kristiadi, Richard E. Turner, Alireza Makhzani:
Structured Inverse-Free Natural Gradient: Memory-Efficient & Numerically-Stable KFAC for Large Neural Nets. CoRR abs/2312.05705 (2023) - 2022
- [c10]Kirill Neklyudov, Max Welling:
Orbital MCMC. AISTATS 2022: 5790-5814 - [i9]Kirill Neklyudov, Daniel Severo, Alireza Makhzani:
Action Matching: A Variational Method for Learning Stochastic Dynamics from Samples. CoRR abs/2210.06662 (2022) - 2021
- [i8]Kirill Neklyudov, Roberto Bondesan, Max Welling:
Deterministic Gibbs Sampling via Ordinary Differential Equations. CoRR abs/2106.10188 (2021) - [i7]Kirill Neklyudov, Priyank Jaini, Max Welling:
Particle Dynamics for Learning EBMs. CoRR abs/2111.13772 (2021) - 2020
- [c9]Kirill Neklyudov, Max Welling, Evgenii Egorov, Dmitry P. Vetrov:
Involutive MCMC: a Unifying Framework. ICML 2020: 7273-7282 - [i6]Kirill Neklyudov, Max Welling, Evgenii Egorov, Dmitry P. Vetrov:
Involutive MCMC: a Unifying Framework. CoRR abs/2006.16653 (2020) - [i5]Kirill Neklyudov, Max Welling:
Orbital MCMC. CoRR abs/2010.08047 (2020)
2010 – 2019
- 2019
- [c8]Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov:
Variance Networks: When Expectation Does Not Meet Your Expectations. ICLR (Poster) 2019 - [c7]Andrei Atanov, Arsenii Ashukha, Dmitry Molchanov, Kirill Neklyudov, Dmitry P. Vetrov:
Uncertainty Estimation via Stochastic Batch Normalization. ISNN (1) 2019: 261-269 - [c6]Evgenii Egorov, Kirill Neklyudov, Ruslan Kostoev, Evgeny Burnaev:
MaxEntropy Pursuit Variational Inference. ISNN (1) 2019: 409-417 - [c5]Kirill Neklyudov, Evgenii Egorov, Dmitry P. Vetrov:
The Implicit Metropolis-Hastings Algorithm. NeurIPS 2019: 13932-13942 - [i4]Evgenii Egorov, Kirill Neklyudov, Ruslan Kostoev, Evgeny Burnaev:
MaxEntropy Pursuit Variational Inference. CoRR abs/1905.07855 (2019) - [i3]Kirill Neklyudov, Evgenii Egorov, Dmitry P. Vetrov:
The Implicit Metropolis-Hastings Algorithm. CoRR abs/1906.03644 (2019) - 2018
- [c4]Andrei Atanov, Arsenii Ashukha, Dmitry Molchanov, Kirill Neklyudov, Dmitry P. Vetrov:
Uncertainty Estimation via Stochastic Batch Normalization. ICLR (Workshop) 2018 - [i2]Andrei Atanov, Arsenii Ashukha, Dmitry Molchanov, Kirill Neklyudov, Dmitry P. Vetrov:
Uncertainty Estimation via Stochastic Batch Normalization. CoRR abs/1802.04893 (2018) - [i1]Kirill Neklyudov, Pavel Shvechikov, Dmitry P. Vetrov:
Metropolis-Hastings view on variational inference and adversarial training. CoRR abs/1810.07151 (2018) - 2017
- [c3]Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov:
Structured Bayesian Pruning via Log-Normal Multiplicative Noise. NIPS 2017: 6775-6784 - 2016
- [c2]Aleksandr M. Semenov, Peter Romov, Sergey Korolev, Daniil Yashkov, Kirill Neklyudov:
Performance of Machine Learning Algorithms in Predicting Game Outcome from Drafts in Dota 2. AIST 2016: 26-37 - [c1]Aleksandr M. Semenov, Peter Romov, Kirill Neklyudov, Daniil Yashkov, Daniil Kireev:
Applications of Machine Learning in Dota 2: Literature Review and Practical Knowledge Sharing. MLSA@PKDD/ECML 2016
Coauthor Index
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