Relativistic Monte Carlo

Relativistic Monte Carlo

14 September 2016

Valerio Perrone

Leonard Hasenclever

Sebastian J. Vollmer

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Papers citing "Relativistic Monte Carlo"

16 / 16 papers shown

Title
Born-Infeld (BI) for AI: Energy-Conserving Descent (ECD) for OptimizationInternational Conference on Machine Learning (ICML), 2022 G. Luca E. Silverstein 179 12 0 26 Jan 2022
A fresh take on 'Barker dynamics' for MCMCMonte Carlo and Quasi-Monte Carlo Methods (MCQMC), 2020 Max Hird Samuel Livingstone T. Rigon 274 10 0 17 Dec 2020
On the accept-reject mechanism for Metropolis-Hastings algorithms N. Glatt-Holtz J. Krometis Cecilia F. Mondaini 276 12 0 09 Nov 2020
Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks Umut Simsekli Ozan Sener George Deligiannidis Murat A. Erdogdu 241 66 0 16 Jun 2020
Bayesian Neural Networks Tom Charnock Laurence Perreault Levasseur F. Lanusse UQCV BDL 228 3 0 02 Jun 2020
Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient NoiseInternational Conference on Machine Learning (ICML), 2020 Umut Simsekli Lingjiong Zhu Yee Whye Teh Mert Gurbuzbalaban 194 53 0 13 Feb 2020
Quantum-Inspired Hamiltonian Monte Carlo for Bayesian Sampling Ziming Liu Zheng Zhang 272 11 0 04 Dec 2019
Bayesian Inference for Large Scale Image Classification Jonathan Heek Nal Kalchbrenner UQCV BDL 193 36 0 09 Aug 2019
Conformal Symplectic and Relativistic Optimization G. Francca Jeremias Sulam Daniel P. Robinson René Vidal 471 72 0 11 Mar 2019
Understanding MCMC Dynamics as Flows on the Wasserstein SpaceInternational Conference on Machine Learning (ICML), 2019 Yu Xie Jingwei Zhuo Jun Zhu 342 23 0 01 Feb 2019
Modified Hamiltonian Monte Carlo for Bayesian inferenceStatistics and computing (Stat. Comput.), 2017 Tijana Radivojević E. Akhmatskaya 390 31 0 13 Jun 2017
Kinetic energy choice in Hamiltonian/hybrid Monte Carlo Samuel Livingstone Michael F Faulkner Gareth O. Roberts 163 46 0 08 Jun 2017
Stochastic Gradient Monomial Gamma SamplerInternational Conference on Machine Learning (ICML), 2017 Yizhe Zhang Changyou Chen Zhe Gan Ricardo Henao Lawrence Carin BDL 181 11 0 05 Jun 2017
On the convergence of Hamiltonian Monte Carlo Alain Durmus Eric Moulines E. Saksman 154 71 0 29 Apr 2017
Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server Leonard Hasenclever Stefan Webb Thibaut Lienart Sebastian J. Vollmer Balaji Lakshminarayanan Charles Blundell Yee Whye Teh BDL 392 73 0 31 Dec 2015
Recycling intermediate steps to improve Hamiltonian Monte Carlo A. Nishimura David B. Dunson 238 10 0 21 Nov 2015