Kirill Neklyudov

According to our database, Kirill Neklyudov authored at least 23 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling.

Yuanqi Du, Michael Plainer, Rob Brekelmans, Chenru Duan, Frank Noé, Carla P. Gomes, Alán Aspuru-Guzik, Kirill Neklyudov

CoRR, 2024

Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold.

Lazar Atanackovic, Xi Zhang, Brandon Amos, Mathieu Blanchette, Leo J. Lee, Yoshua Bengio, Alexander Tong, Kirill Neklyudov

CoRR, 2024

Efficient Evolutionary Search Over Chemical Space with Large Language Models.

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

CoRR, 2024

Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints.

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

CoRR, 2024

A Computational Framework for Solving Wasserstein Lagrangian Flows.

Kirill Neklyudov, Rob Brekelmans, Alexander Tong, Lazar Atanackovic, Qiang Liu, Alireza Makhzani

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Structured Inverse-Free Natural Gradient Descent: Memory-Efficient & Numerically-Stable KFAC.

Wu Lin, Felix Dangel, Runa Eschenhagen, Kirill Neklyudov, Agustinus Kristiadi, Richard E. Turner, Alireza Makhzani

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Structured Inverse-Free Natural Gradient: Memory-Efficient & Numerically-Stable KFAC for Large Neural Nets.

Wu Lin, Felix Dangel, Runa Eschenhagen, Kirill Neklyudov, Agustinus Kristiadi, Richard E. Turner, Alireza Makhzani

CoRR, 2023

Quantum HyperNetworks: Training Binary Neural Networks in Quantum Superposition.

Juan Carrasquilla, Mohamed Hibat-Allah, Estelle M. Inack, Alireza Makhzani, Kirill Neklyudov, Graham W. Taylor, Giacomo Torlai

CoRR, 2023

Wasserstein Quantum Monte Carlo: A Novel Approach for Solving the Quantum Many-Body Schrödinger Equation.

Kirill Neklyudov, Jannes Nys, Luca A. Thiede, Juan Carrasquilla, Qiang Liu, Max Welling, Alireza Makhzani

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Action Matching: Learning Stochastic Dynamics from Samples.

Kirill Neklyudov, Rob Brekelmans, Daniel Severo, Alireza Makhzani

Proceedings of the International Conference on Machine Learning, 2023

2022

Action Matching: A Variational Method for Learning Stochastic Dynamics from Samples.

Kirill Neklyudov, Daniel Severo, Alireza Makhzani

CoRR, 2022

Orbital MCMC.

Kirill Neklyudov, Max Welling

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Particle Dynamics for Learning EBMs.

Kirill Neklyudov, Priyank Jaini, Max Welling

CoRR, 2021

Deterministic Gibbs Sampling via Ordinary Differential Equations.

Kirill Neklyudov, Roberto Bondesan, Max Welling

CoRR, 2021

2020

Involutive MCMC: a Unifying Framework.

Kirill Neklyudov, Max Welling, Evgenii Egorov, Dmitry P. Vetrov

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

The Implicit Metropolis-Hastings Algorithm.

Kirill Neklyudov, Evgenii Egorov, Dmitry P. Vetrov

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

MaxEntropy Pursuit Variational Inference.

Evgenii Egorov, Kirill Neklyudov, Ruslan Kostoev, Evgeny Burnaev

Proceedings of the Advances in Neural Networks - ISNN 2019, 2019

Variance Networks: When Expectation Does Not Meet Your Expectations.

Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Metropolis-Hastings view on variational inference and adversarial training.

Kirill Neklyudov, Pavel Shvechikov, Dmitry P. Vetrov

CoRR, 2018

Uncertainty Estimation via Stochastic Batch Normalization.

Andrei Atanov, Arsenii Ashukha, Dmitry Molchanov, Kirill Neklyudov, Dmitry P. Vetrov

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Structured Bayesian Pruning via Log-Normal Multiplicative Noise.

Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

Applications of Machine Learning in Dota 2: Literature Review and Practical Knowledge Sharing.

Aleksandr M. Semenov, Peter Romov, Kirill Neklyudov, Daniil Yashkov, Daniil Kireev

Proceedings of the Workshop on Machine Learning and Data Mining for Sports Analytics 2016 co-located with the 2016 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016

Performance of Machine Learning Algorithms in Predicting Game Outcome from Drafts in Dota 2.

Aleksandr M. Semenov, Peter Romov, Sergey Korolev, Daniil Yashkov, Kirill Neklyudov

Proceedings of the Analysis of Images, Social Networks and Texts, 2016