Fetching the paper…
Reading the bibliography…
For sampling from a log-concave density, we study implicit integrators resulting from $\theta$-method discretization of the overdamped Langevin diffusion stochastic differential equation.
Zur umkehrbarkeit der statistischen naturgesetze
Andrei N. Kolmogorov · 1937
Earlier work this paper cites.
Monte Carlo sampling methods using Markov chains and their applications
Wilfred K. Hastings · 1970
Earlier work this paper cites.
Monotone operators and the proximal point algorithm
R Tyrrell Rockafellar · 1976
Earlier work this paper cites.
Population correlation matrices for sampling experiments
Robert B. Bendel and M. Ray Mickey · 1978
Earlier work this paper cites.
Numerical Methods and Software
David Kahaner, Cleve B. Moler, Stephen Nash, and George E. Forsythe · 1989
Earlier work this paper cites.
Generalized linear models , volume 37
Peter McCullagh and John A. Nelder · 1989
Earlier work this paper cites.
Numerical methods for ordinary differential systems: the initial value problem
John Denholm Lambert · 1991
Earlier work this paper cites.
Local linearization method for the numerical solution of stochastic differential equations
R. Biscay, J. C. Jimenez, J. J. Riera, and P. A. Valdes · 1996
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.
Computer Methods for Ordinary Differential Equations and Differential-Algebraic Equations
Uri M. Ascher and Linda Petzold · 1998
Earlier work this paper cites.
Monte Carlo Statistical Methods
Christian P. Robert and George Casella · 1999
Earlier work this paper cites.
Optimal scaling for various Metropolis-Hastings algorithms
Gareth O. Roberts and Jeffrey S. Rosenthal · 2001
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.
Bayesian regression and classification
Christopher M. Bishop and Michael E. Tipping · 2003
Earlier work this paper cites.
Geometric ergodicity of discrete-time approximations to multivariate diffusions
Niels Richard Hansen · 2003
Earlier work this paper cites.
An introduction to numerical analysis
Endre Süli and David F. Mayers · 2003
Earlier work this paper cites.
Numerical Optimization
Jorge Nocedal and Stephen Wright · 2006
Earlier work this paper cites.
Numerical methods for evolutionary differential equations
Uri M. Ascher · 2008
Cited alongside, same era.
Mathematical Statistics
Jun Shao · 2008
Cited alongside, same era.
Optimal Transport: Old and New , volume 338
Cédric Villani · 2008
Cited alongside, same era.
An introduction to generalized linear models
Chris Chatfield, Jim Zidek, and Jim Lindsey · 2010
Cited alongside, same era.
A weak trapezoidal method for a class of stochastic differential equations
David Anderson and Jonathan Mattingly · 2011
Cited alongside, same era.
Stability of partially implicit Langevin schemes and their MCMC variants
Bruno Casella, Gareth Roberts, and Osnat Stramer · 2011
Cited alongside, same era.
Proximal Algorithms
Neal Parikh and Stephen Boyd · 2014
Later among the works it cites.
Improved bounds on sample size for implicit matrix trace estimators
Farbod Roosta-Khorasani and Uri M. Ascher · 2015
Later among the works it cites.
Proximal Markov Chain Monte Carlo algorithms
Marcelo Pereyra · 2016
Later among the works it cites.
Linear and Nonlinear Optimization
Richard W. Cottle and Mukund N. Thapa · 2017
Later among the works it cites.
Nonasymptotic convergence analysis for the unadjusted Langevin algorithm
Alain Durmus and Eric Moulines · 2017
Later among the works it cites.
Kernel mean embedding of distributions: A review and beyond
Krikamol Muandet, Kenji Fukumizu, Bharath Sriperumbudur, and Bernhard Schölkopf · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Patrick L. Combettes and Jean-Christophe Pesquet · 2011
Cited alongside, same era.
Probability for Statistics and Machine Learning: Fundamentals and Advanced Topics
Anirban DasGupta · 2011
Cited alongside, same era.
Riemann manifold Langevin and Hamiltonian Monte Carlo methods
Mark Girolami and Ben Calderhead · 2011
Cited alongside, same era.
Matrix Computations , volume 3
Gene H. Golub and Charles F. Van Loan · 2012
Cited alongside, same era.
A kernel two-sample test
Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
Cited alongside, same era.
Markov chains and stochastic stability
Sean P. Meyn and Richard L. Tweedie · 2012
Cited alongside, same era.
Newton-type methods for non-convex optimization under inexact Hessian information
Peng Xu, Fred Roosta, and Michael W Mahoney · 2017
Later among the works it cites.
Convergence of Langevin MCMC in KL-divergence
Xiang Cheng and Peter Bartlett · 2018
Later among the works it cites.
Informed sub-sampling MCMC: approximate Bayesian inference for large datasets
Florian Maire, Nial Friel, and Pierre Alquier · 2018
Later among the works it cites.
Newton-MR: Newton’s Method Without Smoothness or Convexity
Fred Roosta, Yang Liu, Peng Xu, and Michael W. Mahoney · 2018
Later among the works it cites.
Sub-sampled Newton methods
Farbod Roosta-Khorasani and Michael W. Mahoney · 2018
Later among the works it cites.
Sampling as optimization in the space of measures: The Langevin dynamics as a composite optimization problem
Andre Wibisono · 2018
Later among the works it cites.
DINGO: Distributed Newton-type method for gradient-norm optimization
Rixon Crane and Fred Roosta · 2019
Closest in time.
User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient
Arnak S Dalalyan and Avetik Karagulyan · 2019
Closest in time.
UCI machine learning repository, 2019
Dheeru Dua and Casey Graff · 2019
Closest in time.
High-dimensional Bayesian inference via the unadjusted Langevin algorithm
Alain Durmus and Eric Moulines · 2019
Closest in time.