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 (AIST, 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 (MLSA@PKDD/ECML, 2016)

Structured Bayesian Pruning via Log-Normal Multiplicative Noise. - Kirill Neklyudov; Dmitry Molchanov; Arsenii Ashukha; Dmitry P. Vetrov (NIPS, 2017)

Uncertainty Estimation via Stochastic Batch Normalization. - Andrei Atanov; Arsenii Ashukha; Dmitry Molchanov; Kirill Neklyudov; Dmitry P. Vetrov (ICLR (Workshop), 2018)

Metropolis-Hastings view on variational inference and adversarial training. - Kirill Neklyudov; Pavel Shvechikov; Dmitry P. Vetrov (CoRR, 2018)

Variance Networks: When Expectation Does Not Meet Your Expectations. - Kirill Neklyudov; Dmitry Molchanov; Arsenii Ashukha; Dmitry P. Vetrov (ICLR (Poster), 2019)

MaxEntropy Pursuit Variational Inference. - Evgenii Egorov; Kirill Neklyudov; Ruslan Kostoev; Evgeny Burnaev (ISNN (1), 2019)

The Implicit Metropolis-Hastings Algorithm. - Kirill Neklyudov; Evgenii Egorov; Dmitry P. Vetrov (NeurIPS, 2019)

Involutive MCMC: a Unifying Framework. - Kirill Neklyudov; Max Welling; Evgenii Egorov; Dmitry P. Vetrov (ICML, 2020)

Deterministic Gibbs Sampling via Ordinary Differential Equations. - Kirill Neklyudov; Roberto Bondesan; Max Welling (CoRR, 2021)

Particle Dynamics for Learning EBMs. - Kirill Neklyudov; Priyank Jaini; Max Welling (CoRR, 2021)

Orbital MCMC. - Kirill Neklyudov; Max Welling (AISTATS, 2022)

Action Matching: A Variational Method for Learning Stochastic Dynamics from Samples. - Kirill Neklyudov; Daniel Severo 001; Alireza Makhzani (CoRR, 2022)

Action Matching: Learning Stochastic Dynamics from Samples. - Kirill Neklyudov; Rob Brekelmans; Daniel Severo 001; Alireza Makhzani (ICML, 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 (NeurIPS, 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)

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)

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 (ICML, 2024)

A Computational Framework for Solving Wasserstein Lagrangian Flows. - Kirill Neklyudov; Rob Brekelmans; Alexander Tong 001; Lazar Atanackovic; Qiang Liu; Alireza Makhzani (ICML, 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 (NeurIPS, 2024)

Efficient Evolutionary Search Over Chemical Space with Large Language Models. - Haorui Wang; Marta Skreta; Cher Tian Ser; Wenhao Gao 001; Lingkai Kong; Felix Streith-Kalthoff; Chenru Duan; Yuchen Zhuang; Yue Yu 001; Yanqiao Zhu 001; Yuanqi Du; Alán Aspuru-Guzik; et al. (CoRR, 2024)

Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints. - Lingkai Kong; Yuanqi Du; Wenhao Mu; Kirill Neklyudov; Valentin De Bortoli; Dongxia Wu; Haorui Wang; Aaron M. Ferber; Yian Ma; Carla P. Gomes; Chao Zhang 014 (AISTATS, 2025)

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 001; Kirill Neklyudov (ICLR, 2025)

The Superposition of Diffusion Models Using the Itô Density Estimator. - Marta Skreta; Lazar Atanackovic; Joey Bose; Alexander Tong 001; Kirill Neklyudov (ICLR, 2025)

Efficient Evolutionary Search Over Chemical Space with Large Language Models. - Haorui Wang; Marta Skreta; Cher Tian Ser; Wenhao Gao 001; Lingkai Kong; Felix Strieth-Kalthoff; Chenru Duan; Yuchen Zhuang; Yue Yu 001; Yanqiao Zhu 001; Yuanqi Du; Alán Aspuru-Guzik; et al. (ICLR, 2025)

Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of Experts. - Marta Skreta; Tara Akhound-Sadegh; Viktor Ohanesian; Roberto Bondesan; Alán Aspuru-Guzik; Arnaud Doucet; Rob Brekelmans; Alexander Tong 001; Kirill Neklyudov (CoRR, 2025)

Self-Refining Training for Amortized Density Functional Theory. - Majdi Hassan; Cristian Gabellini; Hatem Helal; Dominique Beaini; Kirill Neklyudov (CoRR, 2025)

Progressive Inference-Time Annealing of Diffusion Models for Sampling from Boltzmann Densities. - Tara Akhound-Sadegh; Jungyoon Lee; Avishek Joey Bose; Valentin De Bortoli; Arnaud Doucet; Michael M. Bronstein; Dominique Beaini; Siamak Ravanbakhsh; Kirill Neklyudov; Alexander Tong 001 (CoRR, 2025)