Probabilistic Methods · MCMC

TitleAuthors
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard ModelAtilim Gunes Baydin · Lei Shao · Wahid Bhimji · Lukas Heinrich · Saeid Naderiparizi · Andreas Munk · Jialin Liu · Bradley Gram-Hansen · Gilles Louppe · Lawrence Meadows · Philip Torr · Victor Lee · Kyle Cranmer · Mr. Prabhat · Frank Wood
Online sampling from log-concave distributionsHolden Lee · Oren Mangoubi · Nisheeth Vishnoi
Parameter elimination in particle Gibbs samplingAnna Wigren · Riccardo Sven Risuleo · Lawrence Murray · Fredrik Lindsten
Poisson-Minibatching for Gibbs Sampling with Convergence Rate GuaranteesRuqi Zhang · Christopher De Sa
Pseudo-Extended Markov chain Monte CarloChristopher Nemeth · Fredrik Lindsten · Maurizio Filippone · James Hensman
Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance ReductionDifan Zou · Pan Xu · Quanquan Gu
Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic RatesAdil SALIM · Dmitry Koralev · Peter Richtarik
Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and BeyondXuechen Li · Yi Wu · Lester Mackey · Murat Erdogdu
The Randomized Midpoint Method for Log-Concave SamplingRuoqi Shen · Yin Tat Lee
Computational Separations between Sampling and OptimizationKunal Talwar
Estimating Convergence of Markov chains with L-Lag CouplingsNiloy Biswas · Pierre E Jacob · Paul Vanetti
Exponential Family Estimation via Adversarial Dynamics EmbeddingBo Dai · Zhen Liu · Hanjun Dai · Niao He · Arthur Gretton · Le Song · Dale Schuurmans
Gradient-based Adaptive Markov Chain Monte CarloMichalis Titsias · Petros Dellaportas
On two ways to use determinantal point processes for Monte Carlo integrationGuillaume Gautier · RĂ©mi Bardenet · Michal Valko
Rapid Convergence of the Unadjusted Langevin Algorithm: Isoperimetry SufficesSantosh Vempala · Andre Wibisono
Sample Adaptive MCMCMichael Zhu
The Implicit Metropolis-Hastings AlgorithmKirill Neklyudov · Evgenii Egorov · Dmitry Vetrov