Fetching the paper…
Reading the bibliography…
We present a unified framework to analyze the global convergence of Langevin dynamics based algorithms for nonconvex finite-sum optimization with $n$ component functions.
Laplace’s method revisited: weak convergence of probability measures
Chii-Ruey Hwang · 1980
Earlier work this paper cites.
Mimicking the one-dimensional marginal distributions of processes having an Itô differential
István Gyöngy · 1986
Earlier work this paper cites.
Diffusion for global optimization in rˆn
Tzuu-Shuh Chiang, Chii-Ruey Hwang, and Shuenn Jyi Sheu · 1987
Earlier work this paper cites.
Recursive stochastic algorithms for global optimization in ℝ d \mathbb{R}^{d}
Saul B Gelfand and Sanjoy K Mitter · 1991
Earlier work this paper cites.
Higher-order implicit strong numerical schemes for stochastic differential equations
Peter E Kloeden and Eckhard Platen · 1992
Earlier work this paper cites.
Exponential convergence of Langevin distributions and their discrete approximations
Gareth O Roberts and Richard L Tweedie · 1996
Earlier work this paper cites.
Ergodicity for sdes and approximations: locally lipschitz vector fields and degenerate noise
Jonathan C Mattingly, Andrew M Stuart, and Desmond J Higham · 2002
Earlier work this paper cites.
Metastability in reversible diffusion processes i: Sharp asymptotics for capacities and exit times
Anton Bovier, Michael Eckhoff, Véronique Gayrard, and Markus Klein · 2004
Earlier work this paper cites.
Weighted csiszár-kullback-pinsker inequalities and applications to transportation inequalities
Francois Bolley and Cedric Villani · 2005
Earlier work this paper cites.
Cubic regularization of newton method and its global performance
Yurii Nesterov and Boris T Polyak · 2006
Earlier work this paper cites.
Spectral gaps in wasserstein distances and the 2d stochastic navier-stokes equations
Martin Hairer and Jonathan C Mattingly · 2008
Earlier work this paper cites.
Markov chains and mixing times
David Asher Levin, Yuval Peres, and Elizabeth Lee Wilmer · 2009
Earlier work this paper cites.
Convergence of numerical time-averaging and stationary measures via poisson equations
Jonathan C Mattingly, Andrew M Stuart, and Michael V Tretyakov · 2010
Earlier work this paper cites.
Bayesian learning via stochastic gradient Langevin dynamics
Max Welling and Yee W Teh · 2011
Earlier work this paper cites.
Bayesian posterior sampling via stochastic gradient fisher scoring
Sungjin Ahn, Anoop Korattikara, and Max Welling · 2012
Earlier work this paper cites.
Analysis and geometry of Markov diffusion operators , volume 348
Dominique Bakry, Ivan Gentil, and Michel Ledoux · 2013
Earlier work this paper cites.
Stochastic first-and zeroth-order methods for nonconvex stochastic programming
Saeed Ghadimi and Guanghui Lan · 2013
Earlier work this paper cites.
Accelerating stochastic gradient descent using predictive variance reduction
Rie Johnson and Tong Zhang · 2013
Earlier work this paper cites.
Statistics of random Processes: I. general Theory , volume 5
Robert S Liptser and Albert N Shiryaev · 2013
Cited alongside, same era.
Introductory lectures on convex optimization: A basic course , volume 87
Yurii Nesterov · 2013
Cited alongside, same era.
A trust region algorithm with a worst-case iteration complexity of O ( ϵ − 3 / 2 ) {O}(\epsilon^{-3/2}) for nonconvex optimization
Frank E Curtis, Daniel P Robinson, and Mohammadreza Samadi · 2014
Cited alongside, same era.
Stochastic differential equations and diffusion processes , volume 24
Nobuyuki Ikeda and Shinzo Watanabe · 2014
Cited alongside, same era.
Approximation analysis of stochastic gradient Langevin dynamics by using fokker-planck equation and ito process
Issei Sato and Hiroshi Nakagawa · 2014
Cited alongside, same era.
On the convergence of stochastic gradient mcmc algorithms with high-order integrators
Sampling from a log-concave distribution with compact support with proximal Langevin Monte Carlo
Nicolas Brosse, Alain Durmus, Éric Moulines, and Marcelo Pereyra · 2017
Closest in time.
User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient
Arnak S Dalalyan and Avetik G Karagulyan · 2017
Closest in time.
Rong Ge, Holden Lee, and Andrej Risteski · 2017
Closest in time.
How to escape saddle points efficiently
Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M Kakade, and Michael I Jordan · 2017
Closest in time.
Non-convex learning via stochastic gradient Langevin dynamics: a nonasymptotic analysis
Maxim Raginsky, Alexander Rakhlin, and Matus Telgarsky · 2017
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Changyou Chen, Nan Ding, and Lawrence Carin · 2015
Cited alongside, same era.
Non-asymptotic convergence analysis for the unadjusted Langevin algorithm
Alain Durmus and Eric Moulines · 2015
Cited alongside, same era.
Escaping from saddle points-online stochastic gradient for tensor decomposition
Rong Ge, Furong Huang, Chi Jin, and Yang Yuan · 2015
Cited alongside, same era.
A complete recipe for stochastic gradient mcmc
Yi-An Ma, Tianqi Chen, and Emily Fox · 2015
Cited alongside, same era.
Variance reduction for faster non-convex optimization
Zeyuan Allen-Zhu and Elad Hazan · 2016
Cited alongside, same era.
Gradient descent efficiently finds the Cubic-regularized non-convex newton step
Yair Carmon and John C Duchi · 2016
Cited alongside, same era.
Variance reduction in stochastic gradient Langevin dynamics
Kumar Avinava Dubey, Sashank J Reddi, Sinead A Williamson, Barnabas Poczos, Alexander J Smola, and Eric P Xing · 2016
Cited alongside, same era.
A hitting time analysis of stochastic gradient Langevin dynamics
Yuchen Zhang, Percy Liang, and Moses Charikar · 2017
Closest in time.
Sampling from a log-concave distribution with projected Langevin Monte Carlo
Sébastien Bubeck, Ronen Eldan, and Joseph Lehec · 2018
Closest in time.
Accelerated methods for nonconvex optimization
Yair Carmon, John C Duchi, Oliver Hinder, and Aaron Sidford · 2018
Closest in time.
On the theory of variance reduction for stochastic gradient Monte Carlo
Niladri Chatterji, Nicolas Flammarion, Yian Ma, Peter Bartlett, and Michael Jordan · 2018
Closest in time.
Underdamped Langevin mcmc: A non-asymptotic analysis
Xiang Cheng, Niladri S. Chatterji, Peter L. Bartlett, and Michael I. Jordan · 2018
Closest in time.
Log-concave sampling: Metropolis-hastings algorithms are fast!
Raaz Dwivedi, Yuansi Chen, Martin J Wainwright, and Bin Yu · 2018
Closest in time.
Global non-convex optimization with discretized diffusions
Murat A Erdogdu, Lester Mackey, and Ohad Shamir · 2018
Closest in time.
Accelerated gradient descent escapes saddle points faster than gradient descent
Chi Jin, Praneeth Netrapalli, and Michael I. Jordan · 2018
Closest in time.
Stochastic gradient hamiltonian Monte Carlo with variance reduction for bayesian inference
Zhize Li, Tianyi Zhang, and Jian Li · 2018
Closest in time.
Asynchronous stochastic quasi-Newton MCMC for non-convex optimization
Umut Simsekli, Cagatay Yildiz, Than Huy Nguyen, Taylan Cemgil, and Gael Richard · 2018
Closest in time.
Local optimality and generalization guarantees for the Langevin algorithm via empirical metastability
Belinda Tzen, Tengyuan Liang, and Maxim Raginsky · 2018
Closest in time.
Stochastic variance-reduced cubic regularized Newton methods
Dongruo Zhou, Pan Xu, and Quanquan Gu · 2018
Closest in time.