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Langevin Diffusion Variational Inference

International Conference on Artificial Intelligence and Statistics (AISTATS), 2022

16 August 2022

Papers citing "Langevin Diffusion Variational Inference"

20 / 20 papers shown

Title
Trust Region Constrained Measure Transport in Path Space for Stochastic Optimal Control and Inference Denis Blessing Julius Berner Lorenz Richter Carles Domingo-Enrich Yuanqi Du Arash Vahdat Gerhard Neumann 100 5 0 17 Aug 2025
MDNS: Masked Diffusion Neural Sampler via Stochastic Optimal Control Yuchen Zhu Wei Guo Jaemoo Choi Guan-Horng Liu Yongxin Chen Molei Tao 139 9 0 14 Aug 2025
Rethinking Losses for Diffusion Bridge Samplers Sebastian Sanokowski Lukas Gruber Christoph Bartmann Sepp Hochreiter Sebastian Lehner DiffM 314 2 0 12 Jun 2025
Variational Formulation of the Particle Flow Particle Filter Yinzhuang Yi Jorge Cortés Nikolay Atanasov 226 0 0 06 May 2025
Underdamped Diffusion Bridges with Applications to SamplingInternational Conference on Learning Representations (ICLR), 2025 Denis Blessing Julius Berner Lorenz Richter Gerhard Neumann DiffM 406 24 0 02 Mar 2025
End-To-End Learning of Gaussian Mixture Priors for Diffusion SamplerInternational Conference on Learning Representations (ICLR), 2025 Denis Blessing Xiaogang Jia Gerhard Neumann DiffM 245 6 0 01 Mar 2025
Single-Step Consistent Diffusion Samplers Pascal Jutras-Dubé Patrick Pynadath Ruqi Zhang DiffM 312 0 0 17 Feb 2025
Sequential Controlled Langevin DiffusionsInternational Conference on Learning Representations (ICLR), 2024 Junhua Chen Lorenz Richter Julius Berner Denis Blessing Gerhard Neumann A. Anandkumar 302 28 0 10 Dec 2024
Learned Reference-based Diffusion Sampling for multi-modal distributions Maxence Noble Louis Grenioux Marylou Gabrié Alain Durmus DiffM 400 11 0 25 Oct 2024
Provable Convergence and Limitations of Geometric Tempering for Langevin DynamicsInternational Conference on Learning Representations (ICLR), 2024 Omar Chehab Anna Korba Austin Stromme Adrien Vacher 398 8 0 13 Oct 2024
Beyond ELBOs: A Large-Scale Evaluation of Variational Methods for Sampling Denis Blessing Xiaogang Jia Johannes Esslinger Francisco Vargas Gerhard Neumann 511 40 0 11 Jun 2024
Differentiable Annealed Importance Sampling Minimizes The Jensen-Shannon Divergence Between Initial and Target Distribution Johannes Zenn Kushagra Pandey 148 0 0 23 May 2024
Liouville Flow Importance SamplerInternational Conference on Machine Learning (ICML), 2024 Yifeng Tian Nishant Panda Yen Ting Lin 232 19 0 03 May 2024
Stochastic Localization via Iterative Posterior Sampling Louis Grenioux Maxence Noble Marylou Gabrié Alain Durmus DiffM 248 20 0 16 Feb 2024
Iterated Denoising Energy Matching for Sampling from Boltzmann Densities Tara Akhound-Sadegh Jarrid Rector-Brooks A. Bose Sarthak Mittal Pablo Lemos ... Siamak Ravanbakhsh Gauthier Gidel Yoshua Bengio Nikolay Malkin Alexander Tong DiffM 216 83 0 09 Feb 2024
Accelerating optimization over the space of probability measures Shi Chen Wenxuan Wu Yuhang Yao Stephen J. Wright 302 8 0 06 Oct 2023
Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimizationInternational Conference on Learning Representations (ICLR), 2023 Dinghuai Zhang Ricky Tian Qi Chen Cheng-Hao Liu Aaron C. Courville Yoshua Bengio 331 59 0 04 Oct 2023
Embracing the chaos: analysis and diagnosis of numerical instability in variational flowsNeural Information Processing Systems (NeurIPS), 2023 Zuheng Xu Trevor Campbell 146 6 0 12 Jul 2023
Transport meets Variational Inference: Controlled Monte Carlo DiffusionsInternational Conference on Learning Representations (ICLR), 2023 Francisco Vargas Shreyas Padhy Denis Blessing Nikolas Nusken DiffM OT 697 12 0 03 Jul 2023
Denoising Diffusion SamplersInternational Conference on Learning Representations (ICLR), 2023 Francisco Vargas Will Grathwohl Arnaud Doucet DiffM 271 117 0 27 Feb 2023