Fetching the paper…
Reading the bibliography…
Sampling from a high-dimensional distribution is a fundamental task in statistics, engineering, and the sciences.
Criteria for recurrence and existence of invariant measures for multidimensional diffusions
RN Bhattacharya · 1978
Earlier work this paper cites.
Monte Carlo theory and practice
Frederick James · 1980
Earlier work this paper cites.
Correlation functions and computer simulations
Giorgio Parisi · 1981
Earlier work this paper cites.
Hybrid Monte Carlo
Simon Duane, Anthony D Kennedy, Brian J Pendleton, and Duncan Roweth · 1987
Earlier work this paper cites.
The mixing rate of Markov chains, an isoperimetric inequality, and computing the volume
László Lovász and Miklós Simonovits · 1990
Earlier work this paper cites.
A random polynomial-time algorithm for approximating the volume of convex bodies
Martin Dyer, Alan Frieze, and Ravi Kannan · 1991
Earlier work this paper cites.
Probability with martingales
David Williams · 1991
Earlier work this paper cites.
Hit-and-run algorithms for generating multivariate distributions
Claude JP Bélisle, H Edwin Romeijn, and Robert L Smith · 1993
Earlier work this paper cites.
Random walks in a convex body and an improved volume algorithm
László Lovász and Miklós Simonovits · 1993
Earlier work this paper cites.
Isoperimetric problems for convex bodies and a localization lemma
Ravi Kannan, László Lovász, and Miklós Simonovits · 1995
Earlier work this paper cites.
The Markov chain Monte Carlo method: an approach to approximate counting and integration
Mark Jerrum and Alistair Sinclair · 1996
Earlier work this paper cites.
Rates of convergence of the Hastings and Metropolis algorithms
Kerrie L Mengersen and Richard L Tweedie · 1996
Earlier work this paper cites.
The variational formulation of the Fokker–Planck equation
Richard Jordan, David Kinderlehrer, and Felix Otto · 1998
Earlier work this paper cites.
Hit-and-run mixes fast
László Lovász · 1999
Earlier work this paper cites.
Monte Carlo statistical methods , volume 2
Christian P Robert and George Casella · 1999
Earlier work this paper cites.
Monte Carlo strategies in scientific computing , volume 10
Jun S Liu and Jun S Liu · 2001
Earlier work this paper cites.
An introduction to MCMC for machine learning
Christophe Andrieu, Nando De Freitas, Arnaud Doucet, and Michael I Jordan · 2003
Earlier work this paper cites.
Introductory lectures on convex optimization: A basic course , volume 87
Yurii Nesterov · 2003
Earlier work this paper cites.
General state space Markov chains and MCMC algorithms
Gareth O Roberts and Jeffrey S Rosenthal · 2004
Earlier work this paper cites.
Geometric random walks: a survey
Santosh Vempala · 2005
Earlier work this paper cites.
Pattern recognition and machine learning , volume 4
Christopher M Bishop and Nasser M Nasrabadi · 2006
Earlier work this paper cites.
Hit-and-run from a corner
László Lovász and Santosh Vempala · 2006
Earlier work this paper cites.
The geometry of logconcave functions and sampling algorithms
László Lovász and Santosh Vempala · 2007
Earlier work this paper cites.
Riemann manifold Langevin and Hamiltonian Monte Carlo methods
Mark Girolami and Ben Calderhead · 2011
Earlier work this paper cites.
MCMC using Hamiltonian dynamics
Radford M Neal et al · 2011
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
Earlier work this paper cites.
The No-U-Turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo
Matthew D Hoffman, Andrew Gelman, et al · 2014
Earlier work this paper cites.
Convex bodies: the Brunn–Minkowski theory
Rolf Schneider · 2014
Cited alongside, same era.
Rényi divergence and Kullback-Leibler divergence
Tim Van Erven and Peter Harremos · 2014
Cited alongside, same era.
Convex optimization: Algorithms and complexity
Sébastien Bubeck · 2015
Cited alongside, same era.
Sampling from strongly log-concave distributions with the Unadjusted Langevin Algorithm
Alain Durmus and Eric Moulines · 2016
Cited alongside, same era.
f f -divergence inequalities
Igal Sason and Sergio Verdú · 2016
Cited alongside, same era.
Sampling from a log-concave distribution with compact support with proximal Langevin Monte Carlo
Nicolas Brosse, Alain Durmus, Éric Moulines, and Marcelo Pereyra · 2017
Cited alongside, same era.
Langevin Monte Carlo without smoothness
Niladri Chatterji, Jelena Diakonikolas, Michael I Jordan, and Peter Bartlett · 2020
Later among the works it cites.
Private stochastic convex optimization: optimal rates in linear time
Vitaly Feldman, Tomer Koren, and Kunal Talwar · 2020
Later among the works it cites.
Faster differentially private samplers via Rényi divergence analysis of discretized Langevin MCMC
Arun Ganesh and Kunal Talwar · 2020
Later among the works it cites.
Riemannian Langevin algorithm for solving semidefinite programs
Mufan Bill Li and Murat A Erdogdu · 2020
Later among the works it cites.
Primal dual interpretation of the proximal stochastic gradient Langevin algorithm
Adil Salim and Peter Richtárik · 2020
Later among the works it cites.
Wasserstein control of mirror Langevin Monte Carlo
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Nonasymptotic convergence analysis for the Unadjusted Langevin Algorithm
Alain Durmus and Eric Moulines · 2017
Cited alongside, same era.
Markov chains and mixing times , volume 107
David A Levin and Yuval Peres · 2017
Cited alongside, same era.
Non-convex learning via stochastic gradient Langevin dynamics: a nonasymptotic analysis
Maxim Raginsky, Alexander Rakhlin, and Matus Telgarsky · 2017
Cited alongside, same era.
Langevin Monte Carlo and JKO splitting
Espen Bernton · 2018
Cited alongside, same era.
Sampling from a log-concave distribution with Projected Langevin Monte Carlo
Sébastien Bubeck, Ronen Eldan, and Joseph Lehec · 2018
Cited alongside, same era.
Efficient Bayesian computation by Proximal Markov Chain Monte Carlo: when Langevin meets Moreau
Alain Durmus, Eric Moulines, and Marcelo Pereyra · 2018
Cited alongside, same era.
Kelvin Shuangjian Zhang, Gabriel Peyré, Jalal Fadili, and Marcelo Pereyra · 2020
Later among the works it cites.
Efficient constrained sampling via the mirror-Langevin algorithm
Kwangjun Ahn and Sinho Chewi · 2021
Later among the works it cites.
Differential privacy dynamics of Langevin diffusion and noisy gradient descent
Rishav Chourasia, Jiayuan Ye, and Reza Shokri · 2021
Later among the works it cites.
Lecture notes for statistics 311/electrical engineering 377
John Duchi · 2021
Later among the works it cites.
On the convergence of Langevin Monte Carlo: The interplay between tail growth and smoothness
Murat A Erdogdu and Rasa Hosseinzadeh · 2021
Later among the works it cites.
Lower bounds on Metropolized sampling methods for well-conditioned distributions
Yin Tat Lee, Ruoqi Shen, and Kevin Tian · 2021
Later among the works it cites.
The Langevin Monte Carlo algorithm in the non-smooth log-concave case
Joseph Lehec · 2021
Later among the works it cites.
A proximal algorithm for sampling from non-smooth potentials
Jiaming Liang and Yongxin Chen · 2021
Later among the works it cites.
Is there an analog of Nesterov acceleration for gradient-based MCMC?
Yi-An Ma, Niladri S Chatterji, Xiang Cheng, Nicolas Flammarion, Peter L Bartlett, and Michael I Jordan · 2021
Later among the works it cites.
Unadjusted langevin algorithm for non-convex weakly smooth potentials
Dao Nguyen, Xin Dang, and Yixin Chen · 2021
Later among the works it cites.
Privacy amplification via iteration for shuffled and online PNSGD
Matteo Sordello, Zhiqi Bu, and Jinshuo Dong · 2021
Later among the works it cites.
Minimax mixing time of the Metropolis-adjusted langevin algorithm for log-concave sampling
Keru Wu, Scott Schmidler, and Yuansi Chen · 2021
Later among the works it cites.
Towards a theory of non-log-concave sampling: first-order stationarity guarantees for Langevin Monte Carlo
Krishna Balasubramanian, Sinho Chewi, Murat A Erdogdu, Adil Salim, and Shunshi Zhang · 2022
Closest in time.
Theory and algorithms for diffusion processes on Riemannian Manifolds
Xiang Cheng, Jingzhao Zhang, and Suvrit Sra · 2022
Closest in time.
Log-Concave Sampling
Sinho Chewi · 2022
Closest in time.
Convergence of Langevin Monte Carlo in chi-squared and Rényi divergence
Murat A Erdogdu, Rasa Hosseinzadeh, and Shunshi Zhang · 2022
Closest in time.
Convergence of the Riemannian Langevin Algorithm
Khashayar Gatmiry and Santosh S Vempala · 2022
Closest in time.
Sampling with Riemannian Hamiltonian Monte Carlo in a constrained space
Yunbum Kook, Yin Tat Lee, Ruoqi Shen, and Santosh S Vempala · 2022
Closest in time.
Improved bounds for discretization of Langevin diffusions: Near-optimal rates without convexity
Wenlong Mou, Nicolas Flammarion, Martin J Wainwright, and Peter L Bartlett · 2022
Closest in time.
Differential privacy guarantees for stochastic gradient Langevin dynamics
Theo Ryffel, Francis Bach, and David Pointcheval · 2022
Closest in time.
Differentially private learning needs hidden state (or much faster convergence
Jiayuan Ye and Reza Shokri · 2022
Closest in time.
The query complexity of sampling from strongly log-concave distributions in one dimension
Sinho Chewi, Patrik R Gerber, Chen Lu, Thibaut Le Gouic, and Philippe Rigollet · 2059
Closest in time.