A Convergence Theory for SVGD in the Population Limit under Talagrand's
Inequality T1

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A Convergence Theory for SVGD in the Population Limit under Talagrand's Inequality T1

International Conference on Machine Learning (ICML), 2021

6 June 2021

Peter Richtárik

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Papers citing "A Convergence Theory for SVGD in the Population Limit under Talagrand's Inequality T1"

18 / 18 papers shown

Title
Gradient Flow Sampler-based Distributionally Robust Optimization Zusen Xu Jia Jie Zhu 112 0 0 29 Oct 2025
Adaptive Kernel Selection for Stein Variational Gradient Descent Moritz Melcher Simon Weissmann Ashia C. Wilson Jakob Zech 173 0 0 02 Oct 2025
Improved Finite-Particle Convergence Rates for Stein Variational Gradient DescentInternational Conference on Learning Representations (ICLR), 2024 Sayan Banerjee Krishnakumar Balasubramanian Promit Ghosal 259 7 0 13 Sep 2024
Stein Variational Ergodic Search Darrick Lee Cameron J. Lerch Fabio Ramos Ian Abraham 178 5 0 17 Jun 2024
Long-time asymptotics of noisy SVGD outside the population limit Victor Priser Pascal Bianchi Adil Salim 182 2 0 17 Jun 2024
Zeroth-Order Sampling Methods for Non-Log-Concave Distributions: Alleviating Metastability by Denoising Diffusion Ye He Kevin Rojas Molei Tao DiffM 357 17 0 27 Feb 2024
Bayesian Multi-Task Transfer Learning for Soft Prompt Tuning Haeju Lee Minchan Jeong SeYoung Yun Kee-Eung Kim AAML VPVLM 183 4 0 13 Feb 2024
Towards a Better Theoretical Understanding of Independent Subnetwork TrainingInternational Conference on Machine Learning (ICML), 2023 Egor Shulgin Peter Richtárik AI4CE 324 8 0 28 Jun 2023
Provably Fast Finite Particle Variants of SVGD via Virtual Particle Stochastic ApproximationNeural Information Processing Systems (NeurIPS), 2023 Aniket Das Dheeraj M. Nagaraj 368 8 0 27 May 2023
Learning Rate Free Sampling in Constrained Domains Louis Sharrock Lester W. Mackey Christopher Nemeth 323 3 0 24 May 2023
Tuning-Free Maximum Likelihood Training of Latent Variable Models via Coin BettingInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2023 Louis Sharrock Daniel Dodd Christopher Nemeth 210 10 0 24 May 2023
Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient DescentNeural Information Processing Systems (NeurIPS), 2023 Tianle Liu Promit Ghosal Krishnakumar Balasubramanian Natesh S. Pillai 372 14 0 23 May 2023
Augmented Message Passing Stein Variational Gradient Descent Jiankui Zhou Yue Qiu 154 0 0 18 May 2023
Forward-backward Gaussian variational inference via JKO in the Bures-Wasserstein SpaceInternational Conference on Machine Learning (ICML), 2023 Michael Diao Krishnakumar Balasubramanian Sinho Chewi Adil Salim BDL 127 35 0 10 Apr 2023
Coin Sampling: Gradient-Based Bayesian Inference without Learning RatesInternational Conference on Machine Learning (ICML), 2023 Louis Sharrock Christopher Nemeth BDL 311 9 0 26 Jan 2023
A Finite-Particle Convergence Rate for Stein Variational Gradient DescentNeural Information Processing Systems (NeurIPS), 2022 Jiaxin Shi Lester W. Mackey 222 25 0 17 Nov 2022
Regularized Stein Variational Gradient Flow Ye He Krishnakumar Balasubramanian Bharath K. Sriperumbudur Jianfeng Lu OT 175 13 0 15 Nov 2022
Sampling with Mollified Interaction Energy DescentInternational Conference on Learning Representations (ICLR), 2022 Lingxiao Li Qiang Liu Anna Korba Mikhail Yurochkin Justin Solomon 181 20 0 24 Oct 2022