Fetching the paper…
Reading the bibliography…
Hamiltonian Monte Carlo (HMC) is a popular method in sampling.
Some methods of speeding up the convergence of iteration methods
B.T. Polyak · 1964
Earlier work this paper cites.
Hybrid monte carlo
Simon Duane, A. D. Kennedy, Brian J. Pendleton, and Duncan Roweth · 1987
Earlier work this paper cites.
Online convex optimization in the bandit setting: gradient descent without a gradient
Abraham D. Flaxman, Adam Tauman Kalai, and H. Brendan McMahan · 2005
Earlier work this paper cites.
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo
Ruslan Salakhutdinov and Andriy Mnih · 2008
Earlier work this paper cites.
Probabilistic graphical models: Principles and techniques
Daphne Koller and Nir Friedman · 2009
Earlier work this paper cites.
Riemann manifold Langevin and Hamiltonian Monte Carlo methods
Mark Girolami and Ben Calderhead · 2011
Earlier work this paper cites.
Bayesian learning via Stochastic Gradient Langevin dynamics
Max Welling and Yee Whye Teh · 2011
Earlier work this paper cites.
Randomized smoothing for stochastic optimization
John C. Duchi, Peter L. Bartlett, and Martin J. Wainwright · 2012
Earlier work this paper cites.
MCMC using Hamiltonian dynamics
Radford M. Neal · 2012
Earlier work this paper cites.
Introductory lectures on convex optimization: a basic course
Yurii Nesterov · 2013
Earlier work this paper cites.
Stochastic gradient Hamiltonian Monte Carlo
Tianqi Chen, Emily B. Fox, and Carlos Guestrin · 2014
Earlier work this paper cites.
The No-U-Turn sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo
Matthew D. Hoffman and Andrew Gelman · 2014
Earlier work this paper cites.
A conceptual introduction to Hamiltonian Monte Carlo
Michael Betancourt · 2017
Earlier work this paper cites.
Randomized Hamiltonian Monte Carlo
Nawaf Bou-Rabee and Jesus Maria Sanz-Serna · 2017
Earlier work this paper cites.
Theoretical guarantees for approximate sampling from a smooth and log-concave density
Arnak S. Dalalyan · 2017
Earlier work this paper cites.
On the convergence of Hamiltonian Monte Carlo
Alain Durmus, Eric Moulines, and Eero Saksman · 2017
Earlier work this paper cites.
How to escape saddle points efficiently
Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M. Kakade, and Michael I. Jordan · 2017
Cited alongside, same era.
Magnetic Hamiltonian Monte Carlo
Nilesh Tripuraneni, Mark Rowland, Zoubin Ghahramani, and Richard Turner · 2017
Cited alongside, same era.
Generalizing Hamiltonian Monte Carlo with neural networks
Daniel Levy, Matthew D. Hoffman, and Jascha Sohl-Dickstein · 2018
Cited alongside, same era.
Dimensionally tight bounds for second-order Hamiltonian Monte Carlo
Oren Mangoubi and Nisheeth K. Vishnoi · 2018
Cited alongside, same era.
Optimal convergence rate of Hamiltonian Monte Carlo for strongly logconcave distributions
Zongchen Chen and Santosh S Vempala · 2019
Cited alongside, same era.
Analysis of Langevin Monte Carlo via convex optimization
Alain Durmus, Szymon Majewski, and Błażej Miasojedow · 2019
Cited alongside, same era.
Evaluating the implicit midpoint integrator for Riemannian manifold Hamiltonian Monte Carlo
James A. Brofos and Roy R. Lederman · 2021
Later among the works it cites.
Acceleration methods
Alexandre d’Aspremont, Damien Scieur, and Adrien Taylor · 2021
Later among the works it cites.
Entropy-based adaptive Hamiltonian Monte Carlo
Marcel Hirt, Michalis K. Titsias, and Petros Dellaportas · 2021
Later among the works it cites.
An adaptive-MCMC scheme for setting trajectory lengths in Hamiltonian Monte Carlo
Matthew D. Hoffman, Alexey Radul, and Pavel Sountsov · 2021
Later among the works it cites.
Is there an analog of Nesterov acceleration for 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.
Mixing of Hamiltonian Monte Carlo on strongly logconcave distributions 1: Continuous dynamics
Oren Mangoubi and Aaron Smith · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Arviz a unified library for exploratory analysis of bayesian models in python
Ravin Kumar, Colin Carroll, Ari Hartikainen, and Osvaldo Martin · 2019
Cited alongside, same era.
Mixing of Hamiltonian Monte Carlo on strongly logconcave distributions 2: Numerical integrators
Oren Mangoubi and Aaron Smith · 2019
Cited alongside, same era.
Rapid convergence of the Unadjusted Langevin Algorithm: Isoperimetry suffices
Santosh S. Vempala and Andre Wibisono · 2019
Cited alongside, same era.
Fast mixing of Metropolized Hamiltonian Monte Carlo: Benefits of multi-step gradients
Yuansi Chen, Raaz Dwivedi, Martin J. Wainwright, and Bin Yu · 2020
Cited alongside, same era.
Exponential ergodicity of mirror-langevin diffusions
Sinho Chewi, Thibaut Le Gouic, Chen Lu, Tyler Maunu, Philippe Rigollet, and Austin J. Stromme · 2020
Cited alongside, same era.
On sampling from a log-concave density using kinetic Langevin diffusions
Arnak S. Dalalyan and Lionel Riou-Durand · 2020
Cited alongside, same era.
Later among the works it cites.
Acceleration without momentum, 2021
Fabian Pedregosa · 2021
Later among the works it cites.
Hamiltonian dynamics with non-newtonian momentum for rapid sampling
Greg Ver Steeg and Aram Galstyan · 2021
Later among the works it cites.
An introduction to Hamiltonian Monte Carlo method for sampling
Nisheeth K. Vishnoi · 2021
Later among the works it cites.
A modular analysis of provable acceleration via Polyak’s momentum: Training a wide ReLU network and a deep linear network
Jun-Kun Wang, Chi-Heng Lin, and Jacob Abernethy · 2021
Later among the works it cites.
On the convergence of Hamiltonian Monte Carlo with stochastic gradients
Difan Zou and Quanquan Gu · 2021
Later among the works it cites.
On the dissipation of ideal hamiltonian monte carlo sampler
Qijia Jiang · 2022
Closest in time.
Sqrt(d) dimension dependence of Langevin Monte Carlo
Ruilin Li, Hongyuan Zha, and Molei Tao · 2022
Closest in time.
Metropolis Adjusted Langevin trajectories: a robust alternative to Hamiltonian Monte Carlo
Lionel Riou-Durand and Jure Vogrinc · 2022
Closest in time.
Provable Acceleration of Heavy Ball beyond Quadratics for a Class of Polyak-Lojasiewicz Functions when the Non-Convexity is Averaged-Out
Jun-Kun Wang, Chi-Heng Lin, Andre Wibisono, and Bin Hu · 2022
Closest in time.