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Poster
Wed 17:00 Sample Adaptive MCMC
Michael Zhu
Poster
Thu 10:45 Thompson Sampling and Approximate Inference
My Phan · Yasin Abbasi Yadkori · Justin Domke
Poster
Tue 10:45 AGEM: Solving Linear Inverse Problems via Deep Priors and Sampling
Bichuan Guo · Yuxing Han · Jiangtao Wen
Poster
Wed 10:45 An Adaptive Empirical Bayesian Method for Sparse Deep Learning
Wei Deng · Xiao Zhang · Faming Liang · Guang Lin
Poster
Tue 17:30 The Randomized Midpoint Method for Log-Concave Sampling
Ruoqi Shen · Yin Tat Lee
Poster
Tue 17:30 Pseudo-Extended Markov chain Monte Carlo
Christopher Nemeth · Fredrik Lindsten · Maurizio Filippone · James Hensman
Poster
Tue 17:30 Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction
Difan Zou · Pan Xu · Quanquan Gu
Poster
Tue 17:30 Parameter elimination in particle Gibbs sampling
Anna Wigren · Riccardo Sven Risuleo · Lawrence Murray · Fredrik Lindsten
Poster
Tue 17:30 Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond
Xuechen (Chen) Li · Denny Wu · Lester Mackey · Murat Erdogdu
Poster
Wed 17:00 Estimating Convergence of Markov chains with L-Lag Couplings
Niloy Biswas · Pierre E Jacob · Paul Vanetti
Poster
Tue 17:30 Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates
Adil Salim · Dmitry Kovalev · Peter Richtarik
Poster
Wed 17:00 Gradient-based Adaptive Markov Chain Monte Carlo
Michalis Titsias · Petros Dellaportas