Kirill Neklyudov

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 27

Top Topics

Variational Inference

Diffusion Models

Evolutionary Search

Top Venues

Publications

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 (2024)
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 (2024)
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 (2023)
Kirill Neklyudov, Jannes Nys, Luca 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 (2023)
Kirill Neklyudov, Rob Brekelmans, Daniel Severo, Alireza Makhzani
Action Matching: Learning Stochastic Dynamics from Samples. ICML (2023)
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 (2023)
Kirill Neklyudov, Jannes Nys, Luca 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)
Kirill Neklyudov, Rob Brekelmans, Alexander Tong, Lazar Atanackovic, Qiang Liu, Alireza Makhzani
A Computational Framework for Solving Wasserstein Lagrangian Flows. CoRR (2023)
Kirill Neklyudov, Max Welling
Orbital MCMC. AISTATS (2022)
Kirill Neklyudov, Daniel Severo, Alireza Makhzani
Action Matching: A Variational Method for Learning Stochastic Dynamics from Samples. CoRR (2022)
Kirill Neklyudov, Roberto Bondesan, Max Welling
Deterministic Gibbs Sampling via Ordinary Differential Equations. CoRR (2021)
Kirill Neklyudov, Priyank Jaini, Max Welling
Particle Dynamics for Learning EBMs. CoRR (2021)
Kirill Neklyudov, Max Welling, Evgenii Egorov, Dmitry P. Vetrov
Involutive MCMC: a Unifying Framework. CoRR (2020)
Kirill Neklyudov, Max Welling, Evgenii Egorov, Dmitry P. Vetrov
Involutive MCMC: a Unifying Framework. ICML (2020)
Kirill Neklyudov, Max Welling
Orbital MCMC. CoRR (2020)
Kirill Neklyudov, Evgenii Egorov, Dmitry P. Vetrov
The Implicit Metropolis-Hastings Algorithm. NeurIPS (2019)
Andrei Atanov, Arsenii Ashukha, Dmitry Molchanov, Kirill Neklyudov, Dmitry P. Vetrov
Uncertainty Estimation via Stochastic Batch Normalization. ISNN (1) (2019)
Kirill Neklyudov, Evgenii Egorov, Dmitry P. Vetrov
The Implicit Metropolis-Hastings Algorithm. CoRR (2019)
Evgenii Egorov, Kirill Neklyudov, Ruslan Kostoev, Evgeny Burnaev
MaxEntropy Pursuit Variational Inference. ISNN (1) (2019)
Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov
Variance Networks: When Expectation Does Not Meet Your Expectations. ICLR (Poster) (2019)
Evgenii Egorov, Kirill Neklyudov, Ruslan Kostoev, Evgeny Burnaev
MaxEntropy Pursuit Variational Inference. CoRR (2019)
Andrei Atanov, Arsenii Ashukha, Dmitry Molchanov, Kirill Neklyudov, Dmitry P. Vetrov
Uncertainty Estimation via Stochastic Batch Normalization. CoRR (2018)
Kirill Neklyudov, Pavel Shvechikov, Dmitry P. Vetrov
Metropolis-Hastings view on variational inference and adversarial training. CoRR (2018)
Andrei Atanov, Arsenii Ashukha, Dmitry Molchanov, Kirill Neklyudov, Dmitry P. Vetrov
Uncertainty Estimation via Stochastic Batch Normalization. ICLR (Workshop) (2018)
Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov
Structured Bayesian Pruning via Log-Normal Multiplicative Noise. NIPS (2017)
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)
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)