Sampling as optimization in the space of measures: The Langevin dynamics as a composite optimization problem

22 February 2018

Papers citing "Sampling as optimization in the space of measures: The Langevin dynamics as a composite optimization problem"

50 / 53 papers shown

Title
Operator-Level Quantum Acceleration of Non-Logconcave Sampling Jiaqi Leng Zhiyan Ding Zherui Chen Lin Lin 62 0 0 08 May 2025
Mirror Mean-Field Langevin Dynamics Anming Gu Juno Kim 31 0 0 05 May 2025
DDEQs: Distributional Deep Equilibrium Models through Wasserstein Gradient Flows Jonathan Geuter Clément Bonet Anna Korba David Alvarez-Melis 61 0 0 03 Mar 2025
Langevin Multiplicative Weights Update with Applications in Polynomial Portfolio Management Yi-Hu Feng Tianlin Li Tian Xie 62 0 0 26 Feb 2025
Mixing Time of the Proximal Sampler in Relative Fisher Information via Strong Data Processing Inequality Andre Wibisono 53 1 0 08 Feb 2025
Convergence Analysis of the Wasserstein Proximal Algorithm beyond Geodesic Convexity Shuailong Zhu Xiaohui Chen 77 0 0 28 Jan 2025
Computational and Statistical Asymptotic Analysis of the JKO Scheme for Iterative Algorithms to update distributions Shang Wu Yazhen Wang 43 0 0 11 Jan 2025
Non-geodesically-convex optimization in the Wasserstein space Hoang Phuc Hau Luu Hanlin Yu Bernardo Williams Petrus Mikkola Marcelo Hartmann Kai Puolamaki Arto Klami 53 2 0 08 Jan 2025
Provable Convergence and Limitations of Geometric Tempering for Langevin Dynamics Omar Chehab Anna Korba Austin Stromme Adrien Vacher 35 2 0 13 Oct 2024
Provable Accuracy Bounds for Hybrid Dynamical Optimization and Sampling Matthew Burns Qingyuan Hou Michael Huang 137 1 0 08 Oct 2024
Stochastic variance-reduced Gaussian variational inference on the Bures-Wasserstein manifold Hoang Phuc Hau Luu Hanlin Yu Bernardo Williams Marcelo Hartmann Arto Klami DRL 41 0 0 03 Oct 2024
Proximal Oracles for Optimization and Sampling Jiaming Liang Yongxin Chen 36 3 0 02 Apr 2024
Implicit Diffusion: Efficient Optimization through Stochastic Sampling Pierre Marion Anna Korba Peter Bartlett Mathieu Blondel Valentin De Bortoli Arnaud Doucet Felipe Llinares-López Courtney Paquette Quentin Berthet 76 12 0 08 Feb 2024
Algorithms for mean-field variational inference via polyhedral optimization in the Wasserstein space Yiheng Jiang Sinho Chewi Aram-Alexandre Pooladian 29 7 0 05 Dec 2023
Provably Fast Finite Particle Variants of SVGD via Virtual Particle Stochastic Approximation Aniket Das Dheeraj M. Nagaraj 35 7 0 27 May 2023
Forward-backward Gaussian variational inference via JKO in the Bures-Wasserstein Space Michael Diao Krishnakumar Balasubramanian Sinho Chewi Adil Salim BDL 29 21 0 10 Apr 2023
Query lower bounds for log-concave sampling Sinho Chewi Jaume de Dios Pont Jerry Li Chen Lu Shyam Narayanan 32 8 0 05 Apr 2023
ELF: Federated Langevin Algorithms with Primal, Dual and Bidirectional Compression Avetik G. Karagulyan Peter Richtárik FedML 31 6 0 08 Mar 2023
An Explicit Expansion of the Kullback-Leibler Divergence along its Fisher-Rao Gradient Flow Carles Domingo-Enrich Aram-Alexandre Pooladian MDE 26 11 0 23 Feb 2023
Geometry of Score Based Generative Models S. Ghimire Jinyang Liu Armand Comas Massague Davin Hill A. Masoomi Mario Sznaier Jennifer Dy DiffM 33 8 0 09 Feb 2023
Particle-based Variational Inference with Preconditioned Functional Gradient Flow Hanze Dong Xi Wang Yong Lin Tong Zhang 27 19 0 25 Nov 2022
Birth-death dynamics for sampling: Global convergence, approximations and their asymptotics Yulong Lu D. Slepčev Lihan Wang 37 22 0 01 Nov 2022
A Dynamical System View of Langevin-Based Non-Convex Sampling Mohammad Reza Karimi Ya-Ping Hsieh Andreas Krause 40 4 0 25 Oct 2022
Resolving the Mixing Time of the Langevin Algorithm to its Stationary Distribution for Log-Concave Sampling Jason M. Altschuler Kunal Talwar 38 24 0 16 Oct 2022
Fisher information lower bounds for sampling Sinho Chewi P. Gerber Holden Lee Chen Lu 49 15 0 05 Oct 2022
Nesterov smoothing for sampling without smoothness JiaoJiao Fan Bo Yuan Jiaming Liang Yongxin Chen 37 2 0 15 Aug 2022
Sampling from Log-Concave Distributions over Polytopes via a Soft-Threshold Dikin Walk Oren Mangoubi Nisheeth K. Vishnoi 74 2 0 19 Jun 2022
Utilising the CLT Structure in Stochastic Gradient based Sampling : Improved Analysis and Faster Algorithms Aniket Das Dheeraj M. Nagaraj Anant Raj 54 6 0 08 Jun 2022
Convergence of Stein Variational Gradient Descent under a Weaker Smoothness Condition Lukang Sun Avetik G. Karagulyan Peter Richtárik 26 19 0 01 Jun 2022
Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference R. Grumitt B. Dai U. Seljak BDL 29 12 0 27 May 2022
A Proximal Algorithm for Sampling Jiaming Liang Yongxin Chen 22 17 0 28 Feb 2022
Towards a Theory of Non-Log-Concave Sampling: First-Order Stationarity Guarantees for Langevin Monte Carlo Krishnakumar Balasubramanian Sinho Chewi Murat A. Erdogdu Adil Salim Matthew Shunshi Zhang 42 60 0 10 Feb 2022
HMC and underdamped Langevin united in the unadjusted convex smooth case Nicolai Gouraud Pierre Le Bris Adrien Majka Pierre Monmarché 20 10 0 02 Feb 2022
Convex Analysis of the Mean Field Langevin Dynamics Atsushi Nitanda Denny Wu Taiji Suzuki MLT 61 64 0 25 Jan 2022
Variational Wasserstein gradient flow JiaoJiao Fan Qinsheng Zhang Amirhossein Taghvaei Yongxin Chen 72 54 0 04 Dec 2021
Sampling from Log-Concave Distributions with Infinity-Distance Guarantees Oren Mangoubi Nisheeth K. Vishnoi 30 14 0 07 Nov 2021
Statistical Finite Elements via Langevin Dynamics Ömer Deniz Akyildiz Connor Duffin Sotirios Sabanis Mark Girolami 36 11 0 21 Oct 2021
Efficient Gradient Flows in Sliced-Wasserstein Space Clément Bonet Nicolas Courty Franccois Septier Lucas Drumetz 34 21 0 21 Oct 2021
A Proximal Algorithm for Sampling from Non-smooth Potentials Jiaming Liang Yongxin Chen 42 26 0 09 Oct 2021
When is the Convergence Time of Langevin Algorithms Dimension Independent? A Composite Optimization Viewpoint Y. Freund Yi Ma Tong Zhang 37 16 0 05 Oct 2021
A Convergence Theory for SVGD in the Population Limit under Talagrand's Inequality T1 Adil Salim Lukang Sun Peter Richtárik 23 20 0 06 Jun 2021
Optimizing Functionals on the Space of Probabilities with Input Convex Neural Networks David Alvarez-Melis Yair Schiff Youssef Mroueh 40 53 0 01 Jun 2021
The query complexity of sampling from strongly log-concave distributions in one dimension Sinho Chewi P. Gerber Chen Lu Thibaut Le Gouic Philippe Rigollet 40 21 0 29 May 2021
Kernel Stein Discrepancy Descent Anna Korba Pierre-Cyril Aubin-Frankowski Szymon Majewski Pierre Ablin 19 50 0 20 May 2021
A Non-Asymptotic Analysis for Stein Variational Gradient Descent Anna Korba Adil Salim Michael Arbel Giulia Luise A. Gretton 15 76 0 17 Jun 2020
Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm Adil Salim Peter Richtárik 14 38 0 16 Jun 2020
SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence Sinho Chewi Thibaut Le Gouic Chen Lu Tyler Maunu Philippe Rigollet 36 66 0 03 Jun 2020
On the Convergence of Langevin Monte Carlo: The Interplay between Tail Growth and Smoothness Murat A. Erdogdu Rasa Hosseinzadeh 6 74 0 27 May 2020
Self-Paced Deep Reinforcement Learning Pascal Klink Carlo DÉramo Jan Peters Joni Pajarinen ODL 38 54 0 24 Apr 2020
Sampling Can Be Faster Than Optimization Yian Ma Yuansi Chen Chi Jin Nicolas Flammarion Michael I. Jordan 11 183 0 20 Nov 2018