Kirill Neklyudov

46 papers A* 11A 2Misc 1Journal 26Unranked 6

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Discrete Feynman-Kac Correctors. CoRR Mohsin Hasan, Viktor Ohanesian, Artem Gazizov, Yoshua Bengio, Alán Aspuru-Guzik, Roberto Bondesan, Marta Skreta, Kirill Neklyudov
2026	J	`jnl`	Riemannian MeanFlow. CoRR Dongyeop Woo, Marta Skreta, Seonghyun Park, Kirill Neklyudov, Sungsoo Ahn
2025	J	`jnl`	Amortized Sampling with Transferable Normalizing Flows. CoRR Charlie B. Tan, Majdi Hassan, Leon Klein, Saifuddin Syed, Dominique Beaini, Michael M. Bronstein, Alexander Tong, Kirill Neklyudov
2025	A	`conf`	Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints. AISTATS 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
2025	A*	`conf`	Efficient Evolutionary Search Over Chemical Space with Large Language Models. ICLR Haorui Wang, Marta Skreta, Cher Tian Ser, Wenhao Gao, Lingkai Kong, Felix Strieth-Kalthoff, Chenru Duan, Yuchen Zhuang, Yue Yu, Yanqiao Zhu, Yuanqi Du, Alán Aspuru-Guzik, Kirill Neklyudov, Chao Zhang
2025	A*	`conf`	Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of Experts. ICML Marta Skreta, Tara Akhound-Sadegh, Viktor Ohanesian, Roberto Bondesan, Alán Aspuru-Guzik, Arnaud Doucet, Rob Brekelmans, Alexander Tong, Kirill Neklyudov
2025	J	`jnl`	Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of Experts. CoRR Marta Skreta, Tara Akhound-Sadegh, Viktor Ohanesian, Roberto Bondesan, Alán Aspuru-Guzik, Arnaud Doucet, Rob Brekelmans, Alexander Tong, Kirill Neklyudov
2025	J	`jnl`	Foundations of Diffusion Models in General State Spaces: A Self-Contained Introduction. CoRR Vincent Pauline, Tobias Höppe, Kirill Neklyudov, Alexander Tong, Stefan Bauer, Andrea Dittadi
2025	A*	`conf`	Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold. ICLR Lazar Atanackovic, Xi Zhang, Brandon Amos, Mathieu Blanchette, Leo J. Lee, Yoshua Bengio, Alexander Tong, Kirill Neklyudov
2025	J	`jnl`	Progressive Inference-Time Annealing of Diffusion Models for Sampling from Boltzmann Densities. CoRR Tara Akhound-Sadegh, Jungyoon Lee, Avishek Joey Bose, Valentin De Bortoli, Arnaud Doucet, Michael M. Bronstein, Dominique Beaini, Siamak Ravanbakhsh, Kirill Neklyudov, Alexander Tong
2025	J	`jnl`	Self-Refining Training for Amortized Density Functional Theory. CoRR Majdi Hassan, Cristian Gabellini, Hatem Helal, Dominique Beaini, Kirill Neklyudov
2025	A*	`conf`	The Superposition of Diffusion Models Using the Itô Density Estimator. ICLR Marta Skreta, Lazar Atanackovic, Joey Bose, Alexander Tong, Kirill Neklyudov
2025	J	`jnl`	Wavefunction Flows: Efficient Quantum Simulation of Continuous Flow Models. CoRR David Layden, Ryan Sweke, Vojtech Havlícek, Anirban Chowdhury, Kirill Neklyudov
2024	A*	`conf`	A Computational Framework for Solving Wasserstein Lagrangian Flows. ICML Kirill Neklyudov, Rob Brekelmans, Alexander Tong, Lazar Atanackovic, Qiang Liu, Alireza Makhzani
2024	J	`jnl`	Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints. CoRR Lingkai Kong, Yuanqi Du, Wenhao Mu, Kirill Neklyudov, Valentin De Bortoli, Haorui Wang, Dongxia Wu, Aaron M. Ferber, Yi-An Ma, Carla P. Gomes, Chao Zhang
2024	A*	`conf`	Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling. NeurIPS Yuanqi Du, Michael Plainer, Rob Brekelmans, Chenru Duan, Frank Noé, Carla P. Gomes, Alán Aspuru-Guzik, Kirill Neklyudov
2024	J	`jnl`	Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling. CoRR Yuanqi Du, Michael Plainer, Rob Brekelmans, Chenru Duan, Frank Noé, Carla P. Gomes, Alán Aspuru-Guzik, Kirill Neklyudov
2024	J	`jnl`	Efficient Evolutionary Search Over Chemical Space with Large Language Models. CoRR 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
2024	J	`jnl`	Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold. CoRR Lazar Atanackovic, Xi Zhang, Brandon Amos, Mathieu Blanchette, Leo J. Lee, Yoshua Bengio, Alexander Tong, Kirill Neklyudov
2024	A*	`conf`	Structured Inverse-Free Natural Gradient Descent: Memory-Efficient & Numerically-Stable KFAC. ICML Wu Lin, Felix Dangel, Runa Eschenhagen, Kirill Neklyudov, Agustinus Kristiadi, Richard E. Turner, Alireza Makhzani
2024	J	`jnl`	The Superposition of Diffusion Models Using the Itô Density Estimator. CoRR Marta Skreta, Lazar Atanackovic, Avishek Joey Bose, Alexander Tong, Kirill Neklyudov
2023	J	`jnl`	A Computational Framework for Solving Wasserstein Lagrangian Flows. CoRR Kirill Neklyudov, Rob Brekelmans, Alexander Tong, Lazar Atanackovic, Qiang Liu, Alireza Makhzani
2023	A*	`conf`	Action Matching: Learning Stochastic Dynamics from Samples. ICML Kirill Neklyudov, Rob Brekelmans, Daniel Severo, Alireza Makhzani
2023	J	`jnl`	Quantum HyperNetworks: Training Binary Neural Networks in Quantum Superposition. CoRR Juan Carrasquilla, Mohamed Hibat-Allah, Estelle M. Inack, Alireza Makhzani, Kirill Neklyudov, Graham W. Taylor, Giacomo Torlai
2023	J	`jnl`	Structured Inverse-Free Natural Gradient: Memory-Efficient & Numerically-Stable KFAC for Large Neural Nets. CoRR Wu Lin, Felix Dangel, Runa Eschenhagen, Kirill Neklyudov, Agustinus Kristiadi, Richard E. Turner, Alireza Makhzani
2023	A*	`conf`	Wasserstein Quantum Monte Carlo: A Novel Approach for Solving the Quantum Many-Body Schrödinger Equation. NeurIPS Kirill Neklyudov, Jannes Nys, Luca A. Thiede, Juan Carrasquilla, Qiang Liu, Max Welling, Alireza Makhzani
2023	J	`jnl`	Wasserstein Quantum Monte Carlo: A Novel Approach for Solving the Quantum Many-Body Schrödinger Equation. CoRR Kirill Neklyudov, Jannes Nys, Luca A. Thiede, Juan Carrasquilla, Qiang Liu, Max Welling, Alireza Makhzani
2022	J	`jnl`	Action Matching: A Variational Method for Learning Stochastic Dynamics from Samples. CoRR Kirill Neklyudov, Daniel Severo, Alireza Makhzani
2022	A	`conf`	Orbital MCMC. AISTATS Kirill Neklyudov, Max Welling
2021	J	`jnl`	Deterministic Gibbs Sampling via Ordinary Differential Equations. CoRR Kirill Neklyudov, Roberto Bondesan, Max Welling
2021	J	`jnl`	Particle Dynamics for Learning EBMs. CoRR Kirill Neklyudov, Priyank Jaini, Max Welling
2020	A*	`conf`	Involutive MCMC: a Unifying Framework. ICML Kirill Neklyudov, Max Welling, Evgenii Egorov, Dmitry P. Vetrov
2020	J	`jnl`	Involutive MCMC: a Unifying Framework. CoRR Kirill Neklyudov, Max Welling, Evgenii Egorov, Dmitry P. Vetrov
2020	J	`jnl`	Orbital MCMC. CoRR Kirill Neklyudov, Max Welling
2019	—	`conf`	MaxEntropy Pursuit Variational Inference. ISNN (1) Evgenii Egorov, Kirill Neklyudov, Ruslan Kostoev, Evgeny Burnaev
2019	J	`jnl`	MaxEntropy Pursuit Variational Inference. CoRR Evgenii Egorov, Kirill Neklyudov, Ruslan Kostoev, Evgeny Burnaev
2019	A*	`conf`	The Implicit Metropolis-Hastings Algorithm. NeurIPS Kirill Neklyudov, Evgenii Egorov, Dmitry P. Vetrov
2019	J	`jnl`	The Implicit Metropolis-Hastings Algorithm. CoRR Kirill Neklyudov, Evgenii Egorov, Dmitry P. Vetrov
2019	—	`conf`	Uncertainty Estimation via Stochastic Batch Normalization. ISNN (1) Andrei Atanov, Arsenii Ashukha, Dmitry Molchanov, Kirill Neklyudov, Dmitry P. Vetrov
2019	—	`conf`	Variance Networks: When Expectation Does Not Meet Your Expectations. ICLR (Poster) Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov
2018	J	`jnl`	Metropolis-Hastings view on variational inference and adversarial training. CoRR Kirill Neklyudov, Pavel Shvechikov, Dmitry P. Vetrov
2018	—	`conf`	Uncertainty Estimation via Stochastic Batch Normalization. ICLR (Workshop) Andrei Atanov, Arsenii Ashukha, Dmitry Molchanov, Kirill Neklyudov, Dmitry P. Vetrov
2018	J	`jnl`	Uncertainty Estimation via Stochastic Batch Normalization. CoRR Andrei Atanov, Arsenii Ashukha, Dmitry Molchanov, Kirill Neklyudov, Dmitry P. Vetrov
2017	—	`conf`	Structured Bayesian Pruning via Log-Normal Multiplicative Noise. NIPS Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov
2016	—	`conf`	Applications of Machine Learning in Dota 2: Literature Review and Practical Knowledge Sharing. MLSA@PKDD/ECML Aleksandr M. Semenov, Peter Romov, Kirill Neklyudov, Daniil Yashkov, Daniil Kireev
2016	Misc	`conf`	Performance of Machine Learning Algorithms in Predicting Game Outcome from Drafts in Dota 2. AIST Aleksandr M. Semenov, Peter Romov, Sergey Korolev, Daniil Yashkov, Kirill Neklyudov