Neklyudov, Kirill

31 publications

NeurIPS 2025 Amortized Sampling with Transferable Normalizing Flows Charlie B. Tan, Majdi Hassan, Leon Klein, Saifuddin Syed, Dominique Beaini, Michael M. Bronstein, Alexander Tong, Kirill Neklyudov

AISTATS 2025 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

ICLR 2025 Efficient Evolutionary Search over Chemical Space with Large Language Models Haorui Wang, Marta Skreta, Cher Tian Ser, Wenhao Gao, Lingkai Kong, Felix Strieth-Kalthoff, Chenru Duan, Yuchen Zhuang, Yue Yu, Yanqiao Zhu, Yuanqi Du, Alan Aspuru-Guzik, Kirill Neklyudov, Chao Zhang

ICML 2025 Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of Experts Marta Skreta, Tara Akhound-Sadegh, Viktor Ohanesian, Roberto Bondesan, Alan Aspuru-Guzik, Arnaud Doucet, Rob Brekelmans, Alexander Tong, Kirill Neklyudov

ICLRW 2025 Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of Experts Marta Skreta, Tara Akhound-Sadegh, Viktor Ohanesian, Roberto Bondesan, Alan Aspuru-Guzik, Arnaud Doucet, Rob Brekelmans, Alexander Tong, Kirill Neklyudov

ICLR 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, Kirill Neklyudov

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

ICLRW 2025 Scaling Deep Learning Solutions for Transition Path Sampling Jungyoon Lee, Michael Plainer, Yuanqi Du, Lars Holdijk, Rob Brekelmans, Dominique Beaini, Kirill Neklyudov

ICLRW 2025 Scaling Deep Learning Solutions for Transition Path Sampling Jungyoon Lee, Michael Plainer, Yuanqi Du, Lars Holdijk, Rob Brekelmans, Carla P Gomes, Dominique Beaini, Kirill Neklyudov

ICLR 2025 The Superposition of Diffusion Models Using the Itô Density Estimator Marta Skreta, Lazar Atanackovic, Joey Bose, Alexander Tong, Kirill Neklyudov

ICML 2024 A Computational Framework for Solving Wasserstein Lagrangian Flows Kirill Neklyudov, Rob Brekelmans, Alexander Tong, Lazar Atanackovic, Qiang Liu, Alireza Makhzani

NeurIPS 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

ICMLW 2024 Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling Yuanqi Du, Michael Plainer, Rob Brekelmans, Chenru Duan, Frank Noe, Carla P Gomes, Alan Aspuru-Guzik, Kirill Neklyudov

ICMLW 2024 Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling Yuanqi Du, Michael Plainer, Rob Brekelmans, Chenru Duan, Frank Noe, Carla P Gomes, Alan Aspuru-Guzik, Kirill Neklyudov

ICMLW 2024 Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling Yuanqi Du, Michael Plainer, Rob Brekelmans, Chenru Duan, Frank Noe, Carla P Gomes, Alan Aspuru-Guzik, Kirill Neklyudov

ICMLW 2024 Efficient Evolutionary Search over Chemical Space with Large Language Models Haorui Wang, Marta Skreta, Yuanqi Du, Wenhao Gao, Lingkai Kong, Cher Tian Ser, Felix Strieth-Kalthoff, Chenru Duan, Yuchen Zhuang, Yue Yu, Yanqiao Zhu, Alan Aspuru-Guzik, Kirill Neklyudov, Chao Zhang

ICMLW 2024 Efficient Evolutionary Search over Chemical Space with Large Language Models Haorui Wang, Marta Skreta, Yuanqi Du, Wenhao Gao, Lingkai Kong, Cher Tian Ser, Felix Strieth-Kalthoff, Chenru Duan, Yuchen Zhuang, Yue Yu, Yanqiao Zhu, Alan Aspuru-Guzik, Kirill Neklyudov, Chao Zhang

ICMLW 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

ICML 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

NeurIPSW 2023 A Computational Framework for Solving Wasserstein Lagrangian Flows Kirill Neklyudov, Rob Brekelmans, Alexander Tong, Lazar Atanackovic, Qiang Liu, Alireza Makhzani

ICML 2023 Action Matching: Learning Stochastic Dynamics from Samples Kirill Neklyudov, Rob Brekelmans, Daniel Severo, Alireza Makhzani

NeurIPSW 2023 On Schrödinger Bridge Matching and Expectation Maximization Rob Brekelmans, Kirill Neklyudov

NeurIPSW 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

NeurIPS 2023 Wasserstein Quantum Monte Carlo: A Novel Approach for Solving the Quantum Many-Body Schrödinger Equation Kirill Neklyudov, Jannes Nys, Luca Thiede, Juan Carrasquilla, Qiang Liu, Max Welling, Alireza Makhzani

AISTATS 2022 Orbital MCMC Kirill Neklyudov, Max Welling

NeurIPSW 2022 Action Matching: A Variational Method for Learning Stochastic Dynamics from Samples Kirill Neklyudov, Daniel Severo, Alireza Makhzani

NeurIPSW 2021 Particle Dynamics for Learning EBMs Kirill Neklyudov, Priyank Jaini, Max Welling

ICML 2020 Involutive MCMC: A Unifying Framework Kirill Neklyudov, Max Welling, Evgenii Egorov, Dmitry Vetrov

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

ICLR 2019 Variance Networks: When Expectation Does Not Meet Your Expectations Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry Vetrov

NeurIPS 2017 Structured Bayesian Pruning via Log-Normal Multiplicative Noise Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry P Vetrov