Collecting research materials on neural samplers with diffusion/flow models
59
40 commits
updated Jul 4, 2025
README
Awesome Diffusion/Flow Based Samplers
Collecting research materials on neural samplers with diffusion/flow models
Table of Contents
Workshops & Symposiums
Papers
Diffusion & Flow Based
RNE: a plug-and-play framework for diffusion density estimation and inference-time control
Jiajun He, José Miguel Hernández-Lobato, Yuanqi Du, Francisco Vargas
arXiv:2506.05668 [Paper]
27 June 2025
On scalable and efficient training of diffusion samplers
Minkyu Kim, Kiyoung Seong, Dongyeop Woo, Sungsoo Ahn, Minsu Kim
arXiv:2505.19552 [Paper]
6 June 2025
Test-Time Alignment of Discrete Diffusion Models with Sequential Monte Carlo
Chinmay Pani, Zijing Ou, Yingzhen Li
arXiv:2505.22524 [Paper]
28 May 2025
Energy-based generator matching: A neural sampler for general state space
Dongyeop Woo, Minsu Kim, Minkyu Kim, Kiyoung Seong, Sungsoo Ahn
arXiv:2505.19646 [Paper]
27 May 2025
Importance Weighted Score Matching for Diffusion Samplers with Enhanced Mode Coverage
Chenguang Wang, Xiaoyu Zhang, Kaiyuan Cui, Weichen Zhao, Yongtao Guan, Tianshu Yu
arXiv:2505.19431 [Paper]
26 May 2025
Discrete Neural Flow Samplers with Locally Equivariant Transformer
Zijing Ou, Ruixiang Zhang, Yingzhen Li
arXiv:2505.17741 [Paper]
23 May 2025
Path Gradients after Flow Matching
Lorenz Vaitl, Leon Klein
arXiv:2505.10139 [Paper]
15 May 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, Kirill Neklyudov
arXiv:2503.02819 [Paper]
4 Mar 2025
Scalable Equilibrium Sampling with Sequential Boltzmann Generators
Charlie B. Tan, Avishek Joey Bose, Chen Lin, Leon Klein, Michael M. Bronstein, Alexander Tong
arXiv:2502.18462 [Paper]
25 Feb 2025
Generalised Parallel Tempering: Flexible Replica Exchange via Flows and Diffusions
Leo Zhang, Peter Potaptchik, Arnaud Doucet, Hai-Dang Dau, Saifuddin Syed
arXiv:2502.10328 [Paper]
14 Feb 2025
LEAPS: A discrete neural sampler via locally equivariant networks
Peter Holderrieth, Michael S. Albergo, Tommi Jaakkola
arXiv:2502.10843 [Paper]
15 Feb 2025
Single-Step Consistent Diffusion Samplers
Pascal Jutras-Dubé, Patrick Pynadath, Ruqi Zhang
arXiv:2502.07579 [Paper]
11 Feb 2025
Neural Flow Samplers with Shortcut Models
Wuhao Chen, Zijing Ou, Yingzhen Li
arXiv:2502.07337 [Paper]
11 Feb 2025
No Trick, No Treat: Pursuits and Challenges Towards Simulation-free Training of Neural Samplers
Jiajun He, Yuanqi Du, Francisco Vargas, Dinghuai Zhang, Shreyas Padhy, RuiKang OuYang, Carla Gomes, José Miguel Hernández-Lobato
arXiv:2502.06685 [Paper]
10 Feb 2025
End-to-end Learning of Gaussian Mixture Priors for Diffusion Sampler
Denis Blessing, Xiaogang Jia, Gerhard Neumann
ICLR 2025. [Paper]
11 Feb 2025
Complexity Analysis of Normalizing Constant Estimation: from Jarzynski Equality to Annealed Importance Sampling and beyond
Wei Guo, Molei Tao, Yongxin Chen
ICLR 2025. [Paper]
7 Feb 2025
Underdamped Diffusion Bridges with Applications to Sampling
Denis Blessing, Julius Berner, Lorenz Richter, Gerhard Neumann
ICLR 2025. [Paper]
23 Jan 2025
Neural Sampling from Boltzmann Densities: Fisher-Rao Curves in the Wasserstein Geometry
Jannis Chemseddine, Christian Wald, Richard Duong, Gabriele Steidl
ICLR 2025. [Paper]
22 Jan 2025
A General Framework for Inference-time Scaling and Steering of Diffusion Models
Raghav Singhal, Zachary Horvitz, Ryan Teehan, Mengye Ren, Zhou Yu, Kathleen McKeown, Rajesh Ranganath
arXiv:2501.06848 [Paper]
16 Jan 2025
Learned Reference-based Diffusion Sampling for multi-modal distributions
Maxence Noble, Louis Grenioux, Marylou Gabrié, Alain Oliviero Durmus
ICLR 2025. [Paper]
25 Dec 2024
Sequential Controlled Langevin Diffusions
Junhua Chen, Lorenz Richter, Julius Berner, Denis Blessing, Gerhard Neumann, Anima Anandkumar
ICLR 2025. [Paper]
10 Dec 2024
Diffusion-PINN Sampler
Zhekun Shi, Longlin Yu, Tianyu Xie, Cheng Zhang
arXiv:2410.15336 [Paper]
20 Oct 2024
NETS: A Non-Equilibrium Transport Sampler
Michael S. Albergo, Eric Vanden-Eijnden
arXiv:2410.02711 [Paper]
3 Oct 2024
BNEM: A Boltzmann Sampler Based on Bootstrapped Noised Energy Matching
RuiKang OuYang, Bo Qiang, José Miguel Hernández-Lobato
arXiv:2409.09787 [Paper]
15 Sep 2024
Efficient and Unbiased Sampling of Boltzmann Distributions via Consistency Models
Fengzhe Zhang, Jiajun He, Laurence I. Midgley, Javier Antorán, José Miguel Hernández-Lobato
arXiv:2409.07323 [Paper]
11 Sep 2024
Iterated Energy-based Flow Matching for Sampling from Boltzmann Densities
Dongyeop Woo, Sungsoo Ahn
arXiv:2408.16249 [Paper]
29 Aug 2024
Dynamical Measure Transport and Neural PDE Solvers for Sampling
Jingtong Sun, Julius Berner, Lorenz Richter, Marius Zeinhofer, Johannes Müller, Kamyar Azizzadenesheli, Anima Anandkumar
NeurIPS 2024. [Paper]
10 Jul 2024
Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flows
Alberto Cabezas, Louis Sharrock, Christopher Nemeth
NeurIPS 2024. [Paper] [Code]
23 May 2024
Liouville Flow Importance Sampler
Yifeng Tian, Nishant Panda, Yen Ting Lin
ICML 2024. [Paper] [Code]
3 May 2024
Faster Sampling via Stochastic Gradient Proximal Sampler
Xunpeng Huang, Difan Zou, Yi-An Ma, Hanze Dong, Tong Zhang
ICML 2024. [Paper]
27 May 2024
Path-Guided Particle-based Sampling
Mingzhou_Fan, Ruida Zhou, Chao Tian, Xiaoning Qian
ICLR 2024. [Paper]
2 May 2024
Stochastic Localization via Iterative Posterior Sampling
Louis Grenioux, Maxence Noble, Marylou Gabrié, Alain Oliviero Durmus
ICML 2024. [Paper]
16 Feb 2024
Iterated Denoising Energy Matching for Sampling from Boltzmann Densities
Tara Akhound-Sadegh, Jarrid Rector-Brooks, Avishek Joey Bose, Sarthak Mittal, Pablo Lemos, Cheng-Hao Liu, Marcin Sendera, Siamak Ravanbakhsh, Gauthier Gidel, Yoshua Bengio, Nikolay Malkin, Alexander Tong
ICML 2024. [Paper] [Code]
9 Feb 2024
Particle Denoising Diffusion Sampler
TAngus Phillips, Hai-Dang Dau, Michael John Hutchinson, Valentin De Bortoli, George Deligiannidis, Arnaud Doucet
ICML 2024. [Paper] [Code]
9 Feb 2024
Diffusive Gibbs Sampling
Wenlin Chen, Mingtian Zhang, Brooks Paige, José Miguel Hernández-Lobato, David Barber
ICML 2024. [Paper] [Code]
5 Feb 2024
Reverse Diffusion Monte Carlo
Xunpeng Huang, Hanze Dong, Yifan Hao, Yi-An Ma, Tong Zhang
ICLR 2024. [Paper]
2 Oct 2023
Transport meets Variational Inference: Controlled Monte Carlo Diffusions
Francisco Vargas, Shreyas Padhy, Denis Blessing, Nikolas Nüsken
ICLR 2024. [Paper]
3 Jul 2023
Improved sampling via learned diffusions
Lorenz Richter, Julius Berner
ICLR 2024. [Paper] [Code]
3 Jul 2023
Learning Interpolations between Boltzmann Densities
Bálint Máté, François Fleuret
TMLR 2023. [Paper] [Code]
30 May 2023
Action Matching: Learning Stochastic Dynamics from Samples
Kirill Neklyudov, Rob Brekelmans, Daniel Severo, Alireza Makhzani
ICML 2023. [Paper] [Code]
6 Feb 2023
Denoising Diffusion Samplers
Francisco Vargas, Will Sussman Grathwohl, Arnaud Doucet
ICLR 2023. [Paper]
2 Feb 2023
Optimal Control & GFlowNet & Others
Adjoint Schrödinger Bridge Sampler
Guan-Horng Liu, Jaemoo Choi, Yongxin Chen, Benjamin Kurt Miller, Ricky T. Q. Chen
arXiv:2506.22565 [Paper]
27 June 2025
Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching
Aaron Havens, Benjamin Kurt Miller, Bing Yan, Carles Domingo-Enrich, Anuroop Sriram, Brandon Wood, Daniel Levine, Bin Hu, Brandon Amos, Brian Karrer, Xiang Fu, Guan-Horng Liu, Ricky T. Q. Chen
arXiv:2504.11713 [Paper]
16 Apr 2025
Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control
Carles Domingo-Enrich, Michal Drozdzal, Brian Karrer, Ricky T. Q. Chen
ICLR 2025 [Paper]
7 Jan 2025
Value Gradient Sampler: Sampling as Sequential Decision Making
Sangwoong Yoon, Himchan Hwang, Hyeokju Jeong, Dong Kyu Shin, Che-Sang Park, Sehee Kwon, Frank Chongwoo Park
arXiv:2502.13280 [Paper]
18 Feb 2025
Denoising Fisher Training For Neural Implicit Samplers
Weijian Luo, Wei Deng
arXiv:2411.01453 [Paper]
3 Nov 2024
Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction
Jarrid Rector-Brooks, Mohsin Hasan, Zhangzhi Peng, Zachary Quinn, Chenghao Liu, Sarthak Mittal, Nouha Dziri, Michael Bronstein, Yoshua Bengio, Pranam Chatterjee, Alexander Tong, Avishek Joey Bose
ICLR 2025. [Paper]
10 Otc 2024
Training Neural Samplers with Reverse Diffusive KL Divergence
Jiajun He, Wenlin Chen, Mingtian Zhang, David Barber, José Miguel Hernández-Lobato
AISTAT 2025 [Paper] [Code]
16 Oct 2024
Flow Annealed Importance Sampling Bootstrap meets Differentiable Particle Physics
Annalena Kofler, Vincent Stimper, Mikhail Mikhasenko, Michael Kagan, Lukas Heinrich
arXiv:2411.16234 [Paper]
25 Nov 2024
Functional Gradient Flows for Constrained Sampling
Shiyue Zhang, Longlin Yu, Ziheng Cheng, Cheng Zhang
NeurIPS 2024. [Paper]
30 Oct 2024
Transferable Boltzmann Generators
Leon Klein, Frank Noé
NeurIPS 2024. [Paper]
20 Jun 2024
Improved off-policy training of diffusion samplers
Marcin Sendera, Minsu Kim, Sarthak Mittal, Pablo Lemos, Luca Scimeca, Jarrid Rector-Brooks, Alexandre Adam, Yoshua Bengio, Nikolay Malkin
NeurIPS 2024. [Paper]
26 May 2024
An optimal control perspective on diffusion-based generative modeling
Julius Berner, Lorenz Richter, Karen Ullrich
TMLR 2024. [Paper] [Code]
26 Mar 2024
Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization
Dinghuai Zhang, Ricky T. Q. Chen, Cheng-Hao Liu, Aaron Courville, Yoshua Bengio
ICLR 2024. [Paper]
4 Oct 2023
Entropy-based Training Methods for Scalable Neural Implicit Sampler
Weijian Luo, Boya Zhang, Zhihua Zhang
NeurIPS 2023. [Paper]
8 Jun 2023
GFlowNets and variational inference
Nikolay Malkin, Salem Lahlou, Tristan Deleu, Xu Ji, Edward Hu, Katie Everett, Dinghuai Zhang, Yoshua Bengio
ICLR 2023. [Paper]
2 Oct 2022
Flow Annealed Importance Sampling Bootstrap
Laurence Illing Midgley, Vincent Stimper, Gregor N. C. Simm, Bernhard Schölkopf, José Miguel Hernández-Lobato
ICLR 2023. [Paper] [Code]
3 Aug 2022
Path Integral Sampler: a stochastic control approach for sampling
Qinsheng Zhang, Yongxin Chen
ICLR 2022. [Paper] [Code]
30 Nov 2021
Solving high-dimensional Hamilton-Jacobi-Bellman PDEs using neural networks: perspectives from the theory of controlled diffusions and measures on path space
Nikolas Nüsken, Lorenz Richter
arXiv:2005.05409 [Paper]
11 May 2020
Applications
Energy-Weighted Flow Matching for Offline Reinforcement Learning
Shiyuan Zhang, Weitong Zhang, Quanquan Gu
ICLR 2025 [Paper]
6 Mar 2025
Scalable Discrete Diffusion Samplers: Combinatorial Optimization and Statistical Physics
Sebastian Sanokowski, Wilhelm Berghammer, Martin Ennemoser, Haoyu Peter Wang, Sepp Hochreiter, Sebastian Lehner
ICLR 2025 [Paper]
12 Feb 2025
Debiasing Guidance for Discrete Diffusion with Sequential Monte Carlo
Cheuk Kit Lee, Paul Jeha, Jes Frellsen, Pietro Lio, Michael Samuel Albergo, Francisco Vargas
arXiv:2502.06079 [Paper]
10 Feb 2025
Syntactic and Semantic Control of Large Language Models via Sequential Monte Carlo
João Loula, Benjamin LeBrun, Li Du, Ben Lipkin, Clemente Pasti, Gabriel Grand, Tianyu Liu, Yahya Emara, Marjorie Freedman, Jason Eisner, Ryan Cotterell, Vikash Mansinghka, Alexander K. Lew, Tim Vieira, Timothy J. O'Donnell
ICLR 2025. [Paper]
23 Jan 2025
Step-by-Step Reasoning for Math Problems via Twisted Sequential Monte Carlo
Shengyu Feng, Xiang Kong, Shuang Ma, Aonan Zhang, Dong Yin, Chong Wang, Ruoming Pang, Yiming Yang
ICLR 2025. [Paper]
23 Jan 2025
Alignment without Over-optimization: Training-Free Solution for Diffusion Models
Sunwoo Kim, Minkyu Kim, Dongmin Park
ICLR 2025. [Paper]
23 Jan 2025
Posterior Inference in Sequential Models with Soft Value Guidance
Nikolas Nüsken, Lorenz Richter
Blog Post. [Blog]
6 Jan 2025
On Diffusion Posterior Sampling via Sequential Monte Carlo for Zero-Shot Scaffolding of Protein Motifs
James Matthew Young, O. Deniz Akyildiz
arXiv:2412.05788 [Paper]
8 Dec 2024
Efficient Diversity-Preserving Diffusion Alignment via Gradient-Informed GFlowNets
Zhen Liu, Tim Z. Xiao, Weiyang Liu, Yoshua Bengio, Dinghuai Zhang
ICLR 2025. [Paper]
10 Dec 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 Ferber, Yi-An Ma, Carla P. Gomes, Chao Zhang
AISTATS 2025. [Paper]
21 Oct 2024
Plug-and-Play Controllable Generation for Discrete Masked Models
Wei Guo, Yuchen Zhu, Molei Tao, Yongxin Chen
arXiv:2410.02143 [Paper]
3 Oct 2024
Improving GFlowNets for Text-to-Image Diffusion Alignment
Dinghuai Zhang, Yizhe Zhang, Jiatao Gu, Ruixiang Zhang, Josh Susskind, Navdeep Jaitly, Shuangfei Zhai
TMLR 2025. [Paper]
2 Jun 2024
Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo
Stephen Zhao, Rob Brekelmans, Alireza Makhzani, Roger Grosse
ICML 2024 Best Paper Award. [Paper]
26 Apr 2024
Monte Carlo guided Diffusion for Bayesian linear inverse problems
Gabriel Cardoso, Yazid Janati El Idrissi, Sylvain Le Corff, Eric Moulines
ICLR 2024. [Paper]
15 Aug 2023
Practical and Asymptotically Exact Conditional Sampling in Diffusion Models
Luhuan Wu, Brian L. Trippe, Christian A. Naesseth, David M. Blei, John P. Cunningham
NeurIPS 2023. [Paper]
30 Jun 2023
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Collecting research materials on neural samplers with diffusion/flow models
59
40 commits
updated Jul 4, 2025
README
Awesome Diffusion/Flow Based Samplers
Collecting research materials on neural samplers with diffusion/flow models
Table of Contents
Workshops & Symposiums
Papers
Diffusion & Flow Based
RNE: a plug-and-play framework for diffusion density estimation and inference-time control
Jiajun He, José Miguel Hernández-Lobato, Yuanqi Du, Francisco Vargas
arXiv:2506.05668 [Paper]
27 June 2025
On scalable and efficient training of diffusion samplers
Minkyu Kim, Kiyoung Seong, Dongyeop Woo, Sungsoo Ahn, Minsu Kim
arXiv:2505.19552 [Paper]
6 June 2025
Test-Time Alignment of Discrete Diffusion Models with Sequential Monte Carlo
Chinmay Pani, Zijing Ou, Yingzhen Li
arXiv:2505.22524 [Paper]
28 May 2025
Energy-based generator matching: A neural sampler for general state space
Dongyeop Woo, Minsu Kim, Minkyu Kim, Kiyoung Seong, Sungsoo Ahn
arXiv:2505.19646 [Paper]
27 May 2025
Importance Weighted Score Matching for Diffusion Samplers with Enhanced Mode Coverage
Chenguang Wang, Xiaoyu Zhang, Kaiyuan Cui, Weichen Zhao, Yongtao Guan, Tianshu Yu
arXiv:2505.19431 [Paper]
26 May 2025
Discrete Neural Flow Samplers with Locally Equivariant Transformer
Zijing Ou, Ruixiang Zhang, Yingzhen Li
arXiv:2505.17741 [Paper]
23 May 2025
Path Gradients after Flow Matching
Lorenz Vaitl, Leon Klein
arXiv:2505.10139 [Paper]
15 May 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, Kirill Neklyudov
arXiv:2503.02819 [Paper]
4 Mar 2025
Scalable Equilibrium Sampling with Sequential Boltzmann Generators
Charlie B. Tan, Avishek Joey Bose, Chen Lin, Leon Klein, Michael M. Bronstein, Alexander Tong
arXiv:2502.18462 [Paper]
25 Feb 2025
Generalised Parallel Tempering: Flexible Replica Exchange via Flows and Diffusions
Leo Zhang, Peter Potaptchik, Arnaud Doucet, Hai-Dang Dau, Saifuddin Syed
arXiv:2502.10328 [Paper]
14 Feb 2025
LEAPS: A discrete neural sampler via locally equivariant networks
Peter Holderrieth, Michael S. Albergo, Tommi Jaakkola
arXiv:2502.10843 [Paper]
15 Feb 2025
Single-Step Consistent Diffusion Samplers
Pascal Jutras-Dubé, Patrick Pynadath, Ruqi Zhang
arXiv:2502.07579 [Paper]
11 Feb 2025
Neural Flow Samplers with Shortcut Models
Wuhao Chen, Zijing Ou, Yingzhen Li
arXiv:2502.07337 [Paper]
11 Feb 2025
No Trick, No Treat: Pursuits and Challenges Towards Simulation-free Training of Neural Samplers
Jiajun He, Yuanqi Du, Francisco Vargas, Dinghuai Zhang, Shreyas Padhy, RuiKang OuYang, Carla Gomes, José Miguel Hernández-Lobato
arXiv:2502.06685 [Paper]
10 Feb 2025
End-to-end Learning of Gaussian Mixture Priors for Diffusion Sampler
Denis Blessing, Xiaogang Jia, Gerhard Neumann
ICLR 2025. [Paper]
11 Feb 2025
Complexity Analysis of Normalizing Constant Estimation: from Jarzynski Equality to Annealed Importance Sampling and beyond
Wei Guo, Molei Tao, Yongxin Chen
ICLR 2025. [Paper]
7 Feb 2025
Underdamped Diffusion Bridges with Applications to Sampling
Denis Blessing, Julius Berner, Lorenz Richter, Gerhard Neumann
ICLR 2025. [Paper]
23 Jan 2025
Neural Sampling from Boltzmann Densities: Fisher-Rao Curves in the Wasserstein Geometry
Jannis Chemseddine, Christian Wald, Richard Duong, Gabriele Steidl
ICLR 2025. [Paper]
22 Jan 2025
A General Framework for Inference-time Scaling and Steering of Diffusion Models
Raghav Singhal, Zachary Horvitz, Ryan Teehan, Mengye Ren, Zhou Yu, Kathleen McKeown, Rajesh Ranganath
arXiv:2501.06848 [Paper]
16 Jan 2025
Learned Reference-based Diffusion Sampling for multi-modal distributions
Maxence Noble, Louis Grenioux, Marylou Gabrié, Alain Oliviero Durmus
ICLR 2025. [Paper]
25 Dec 2024
Sequential Controlled Langevin Diffusions
Junhua Chen, Lorenz Richter, Julius Berner, Denis Blessing, Gerhard Neumann, Anima Anandkumar
ICLR 2025. [Paper]
10 Dec 2024
Diffusion-PINN Sampler
Zhekun Shi, Longlin Yu, Tianyu Xie, Cheng Zhang
arXiv:2410.15336 [Paper]
20 Oct 2024
NETS: A Non-Equilibrium Transport Sampler
Michael S. Albergo, Eric Vanden-Eijnden
arXiv:2410.02711 [Paper]
3 Oct 2024
BNEM: A Boltzmann Sampler Based on Bootstrapped Noised Energy Matching
RuiKang OuYang, Bo Qiang, José Miguel Hernández-Lobato
arXiv:2409.09787 [Paper]
15 Sep 2024
Efficient and Unbiased Sampling of Boltzmann Distributions via Consistency Models
Fengzhe Zhang, Jiajun He, Laurence I. Midgley, Javier Antorán, José Miguel Hernández-Lobato
arXiv:2409.07323 [Paper]
11 Sep 2024
Iterated Energy-based Flow Matching for Sampling from Boltzmann Densities
Dongyeop Woo, Sungsoo Ahn
arXiv:2408.16249 [Paper]
29 Aug 2024
Dynamical Measure Transport and Neural PDE Solvers for Sampling
Jingtong Sun, Julius Berner, Lorenz Richter, Marius Zeinhofer, Johannes Müller, Kamyar Azizzadenesheli, Anima Anandkumar
NeurIPS 2024. [Paper]
10 Jul 2024
Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flows
Alberto Cabezas, Louis Sharrock, Christopher Nemeth
NeurIPS 2024. [Paper] [Code]
23 May 2024
Liouville Flow Importance Sampler
Yifeng Tian, Nishant Panda, Yen Ting Lin
ICML 2024. [Paper] [Code]
3 May 2024
Faster Sampling via Stochastic Gradient Proximal Sampler
Xunpeng Huang, Difan Zou, Yi-An Ma, Hanze Dong, Tong Zhang
ICML 2024. [Paper]
27 May 2024
Path-Guided Particle-based Sampling
Mingzhou_Fan, Ruida Zhou, Chao Tian, Xiaoning Qian
ICLR 2024. [Paper]
2 May 2024
Stochastic Localization via Iterative Posterior Sampling
Louis Grenioux, Maxence Noble, Marylou Gabrié, Alain Oliviero Durmus
ICML 2024. [Paper]
16 Feb 2024
Iterated Denoising Energy Matching for Sampling from Boltzmann Densities
Tara Akhound-Sadegh, Jarrid Rector-Brooks, Avishek Joey Bose, Sarthak Mittal, Pablo Lemos, Cheng-Hao Liu, Marcin Sendera, Siamak Ravanbakhsh, Gauthier Gidel, Yoshua Bengio, Nikolay Malkin, Alexander Tong
ICML 2024. [Paper] [Code]
9 Feb 2024
Particle Denoising Diffusion Sampler
TAngus Phillips, Hai-Dang Dau, Michael John Hutchinson, Valentin De Bortoli, George Deligiannidis, Arnaud Doucet
ICML 2024. [Paper] [Code]
9 Feb 2024
Diffusive Gibbs Sampling
Wenlin Chen, Mingtian Zhang, Brooks Paige, José Miguel Hernández-Lobato, David Barber
ICML 2024. [Paper] [Code]
5 Feb 2024
Reverse Diffusion Monte Carlo
Xunpeng Huang, Hanze Dong, Yifan Hao, Yi-An Ma, Tong Zhang
ICLR 2024. [Paper]
2 Oct 2023
Transport meets Variational Inference: Controlled Monte Carlo Diffusions
Francisco Vargas, Shreyas Padhy, Denis Blessing, Nikolas Nüsken
ICLR 2024. [Paper]
3 Jul 2023
Improved sampling via learned diffusions
Lorenz Richter, Julius Berner
ICLR 2024. [Paper] [Code]
3 Jul 2023
Learning Interpolations between Boltzmann Densities
Bálint Máté, François Fleuret
TMLR 2023. [Paper] [Code]
30 May 2023
Action Matching: Learning Stochastic Dynamics from Samples
Kirill Neklyudov, Rob Brekelmans, Daniel Severo, Alireza Makhzani
ICML 2023. [Paper] [Code]
6 Feb 2023
Denoising Diffusion Samplers
Francisco Vargas, Will Sussman Grathwohl, Arnaud Doucet
ICLR 2023. [Paper]
2 Feb 2023
Optimal Control & GFlowNet & Others
Adjoint Schrödinger Bridge Sampler
Guan-Horng Liu, Jaemoo Choi, Yongxin Chen, Benjamin Kurt Miller, Ricky T. Q. Chen
arXiv:2506.22565 [Paper]
27 June 2025
Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching
Aaron Havens, Benjamin Kurt Miller, Bing Yan, Carles Domingo-Enrich, Anuroop Sriram, Brandon Wood, Daniel Levine, Bin Hu, Brandon Amos, Brian Karrer, Xiang Fu, Guan-Horng Liu, Ricky T. Q. Chen
arXiv:2504.11713 [Paper]
16 Apr 2025
Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control
Carles Domingo-Enrich, Michal Drozdzal, Brian Karrer, Ricky T. Q. Chen
ICLR 2025 [Paper]
7 Jan 2025
Value Gradient Sampler: Sampling as Sequential Decision Making
Sangwoong Yoon, Himchan Hwang, Hyeokju Jeong, Dong Kyu Shin, Che-Sang Park, Sehee Kwon, Frank Chongwoo Park
arXiv:2502.13280 [Paper]
18 Feb 2025
Denoising Fisher Training For Neural Implicit Samplers
Weijian Luo, Wei Deng
arXiv:2411.01453 [Paper]
3 Nov 2024
Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction
Jarrid Rector-Brooks, Mohsin Hasan, Zhangzhi Peng, Zachary Quinn, Chenghao Liu, Sarthak Mittal, Nouha Dziri, Michael Bronstein, Yoshua Bengio, Pranam Chatterjee, Alexander Tong, Avishek Joey Bose
ICLR 2025. [Paper]
10 Otc 2024
Training Neural Samplers with Reverse Diffusive KL Divergence
Jiajun He, Wenlin Chen, Mingtian Zhang, David Barber, José Miguel Hernández-Lobato
AISTAT 2025 [Paper] [Code]
16 Oct 2024
Flow Annealed Importance Sampling Bootstrap meets Differentiable Particle Physics
Annalena Kofler, Vincent Stimper, Mikhail Mikhasenko, Michael Kagan, Lukas Heinrich
arXiv:2411.16234 [Paper]
25 Nov 2024
Functional Gradient Flows for Constrained Sampling
Shiyue Zhang, Longlin Yu, Ziheng Cheng, Cheng Zhang
NeurIPS 2024. [Paper]
30 Oct 2024
Transferable Boltzmann Generators
Leon Klein, Frank Noé
NeurIPS 2024. [Paper]
20 Jun 2024
Improved off-policy training of diffusion samplers
Marcin Sendera, Minsu Kim, Sarthak Mittal, Pablo Lemos, Luca Scimeca, Jarrid Rector-Brooks, Alexandre Adam, Yoshua Bengio, Nikolay Malkin
NeurIPS 2024. [Paper]
26 May 2024
An optimal control perspective on diffusion-based generative modeling
Julius Berner, Lorenz Richter, Karen Ullrich
TMLR 2024. [Paper] [Code]
26 Mar 2024
Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization
Dinghuai Zhang, Ricky T. Q. Chen, Cheng-Hao Liu, Aaron Courville, Yoshua Bengio
ICLR 2024. [Paper]
4 Oct 2023
Entropy-based Training Methods for Scalable Neural Implicit Sampler
Weijian Luo, Boya Zhang, Zhihua Zhang
NeurIPS 2023. [Paper]
8 Jun 2023
GFlowNets and variational inference
Nikolay Malkin, Salem Lahlou, Tristan Deleu, Xu Ji, Edward Hu, Katie Everett, Dinghuai Zhang, Yoshua Bengio
ICLR 2023. [Paper]
2 Oct 2022
Flow Annealed Importance Sampling Bootstrap
Laurence Illing Midgley, Vincent Stimper, Gregor N. C. Simm, Bernhard Schölkopf, José Miguel Hernández-Lobato
ICLR 2023. [Paper] [Code]
3 Aug 2022
Path Integral Sampler: a stochastic control approach for sampling
Qinsheng Zhang, Yongxin Chen
ICLR 2022. [Paper] [Code]
30 Nov 2021
Solving high-dimensional Hamilton-Jacobi-Bellman PDEs using neural networks: perspectives from the theory of controlled diffusions and measures on path space
Nikolas Nüsken, Lorenz Richter
arXiv:2005.05409 [Paper]
11 May 2020
Applications
Energy-Weighted Flow Matching for Offline Reinforcement Learning
Shiyuan Zhang, Weitong Zhang, Quanquan Gu
ICLR 2025 [Paper]
6 Mar 2025
Scalable Discrete Diffusion Samplers: Combinatorial Optimization and Statistical Physics
Sebastian Sanokowski, Wilhelm Berghammer, Martin Ennemoser, Haoyu Peter Wang, Sepp Hochreiter, Sebastian Lehner
ICLR 2025 [Paper]
12 Feb 2025
Debiasing Guidance for Discrete Diffusion with Sequential Monte Carlo
Cheuk Kit Lee, Paul Jeha, Jes Frellsen, Pietro Lio, Michael Samuel Albergo, Francisco Vargas
arXiv:2502.06079 [Paper]
10 Feb 2025
Syntactic and Semantic Control of Large Language Models via Sequential Monte Carlo
João Loula, Benjamin LeBrun, Li Du, Ben Lipkin, Clemente Pasti, Gabriel Grand, Tianyu Liu, Yahya Emara, Marjorie Freedman, Jason Eisner, Ryan Cotterell, Vikash Mansinghka, Alexander K. Lew, Tim Vieira, Timothy J. O'Donnell
ICLR 2025. [Paper]
23 Jan 2025
Step-by-Step Reasoning for Math Problems via Twisted Sequential Monte Carlo
Shengyu Feng, Xiang Kong, Shuang Ma, Aonan Zhang, Dong Yin, Chong Wang, Ruoming Pang, Yiming Yang
ICLR 2025. [Paper]
23 Jan 2025
Alignment without Over-optimization: Training-Free Solution for Diffusion Models
Sunwoo Kim, Minkyu Kim, Dongmin Park
ICLR 2025. [Paper]
23 Jan 2025
Posterior Inference in Sequential Models with Soft Value Guidance
Nikolas Nüsken, Lorenz Richter
Blog Post. [Blog]
6 Jan 2025
On Diffusion Posterior Sampling via Sequential Monte Carlo for Zero-Shot Scaffolding of Protein Motifs
James Matthew Young, O. Deniz Akyildiz
arXiv:2412.05788 [Paper]
8 Dec 2024
Efficient Diversity-Preserving Diffusion Alignment via Gradient-Informed GFlowNets
Zhen Liu, Tim Z. Xiao, Weiyang Liu, Yoshua Bengio, Dinghuai Zhang
ICLR 2025. [Paper]
10 Dec 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 Ferber, Yi-An Ma, Carla P. Gomes, Chao Zhang
AISTATS 2025. [Paper]
21 Oct 2024
Plug-and-Play Controllable Generation for Discrete Masked Models
Wei Guo, Yuchen Zhu, Molei Tao, Yongxin Chen
arXiv:2410.02143 [Paper]
3 Oct 2024
Improving GFlowNets for Text-to-Image Diffusion Alignment
Dinghuai Zhang, Yizhe Zhang, Jiatao Gu, Ruixiang Zhang, Josh Susskind, Navdeep Jaitly, Shuangfei Zhai
TMLR 2025. [Paper]
2 Jun 2024
Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo
Stephen Zhao, Rob Brekelmans, Alireza Makhzani, Roger Grosse
ICML 2024 Best Paper Award. [Paper]
26 Apr 2024
Monte Carlo guided Diffusion for Bayesian linear inverse problems
Gabriel Cardoso, Yazid Janati El Idrissi, Sylvain Le Corff, Eric Moulines
ICLR 2024. [Paper]
15 Aug 2023
Practical and Asymptotically Exact Conditional Sampling in Diffusion Models
Luhuan Wu, Brian L. Trippe, Christian A. Naesseth, David M. Blei, John P. Cunningham
NeurIPS 2023. [Paper]
30 Jun 2023
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