MCMC using Hamiltonian dynamics

9 June 2012

Papers citing "MCMC using Hamiltonian dynamics"

50 / 1,031 papers shown

Title
FAdam: Adam is a natural gradient optimizer using diagonal empirical Fisher information Dongseong Hwang ODL 29 4 0 21 May 2024
Robust Approximate Sampling via Stochastic Gradient Barker Dynamics Lorenzo Mauri Giacomo Zanella 22 3 0 14 May 2024
Constrained Exploration via Reflected Replica Exchange Stochastic Gradient Langevin Dynamics Haoyang Zheng Hengrong Du Qi Feng Wei Deng Guang Lin 31 4 0 13 May 2024
Multi-fidelity Hamiltonian Monte Carlo Dhruv V. Patel Jonghyun Lee Matthew W. Farthing P. Kitanidis Eric F. Darve 40 0 0 08 May 2024
Gaussian Stochastic Weight Averaging for Bayesian Low-Rank Adaptation of Large Language Models Emre Onal Klemens Flöge Emma Caldwell A. Sheverdin Vincent Fortuin UQCV BDL 37 9 0 06 May 2024
Liouville Flow Importance Sampler Yifeng Tian Nishant Panda Yen Ting Lin 28 8 0 03 May 2024
S $^2$ AC: Energy-Based Reinforcement Learning with Stein Soft Actor Critic Safa Messaoud Billel Mokeddem Zhenghai Xue L. Pang Bo An Haipeng Chen Sanjay Chawla 28 3 0 02 May 2024
Attacking Bayes: On the Adversarial Robustness of Bayesian Neural Networks Yunzhen Feng Tim G. J. Rudner Nikolaos Tsilivis Julia Kempe AAML BDL 35 1 0 27 Apr 2024
GIST: Gibbs self-tuning for locally adaptive Hamiltonian Monte Carlo Nawaf Bou-Rabee Bob Carpenter Milo Marsden 41 6 0 23 Apr 2024
Using early rejection Markov chain Monte Carlo and Gaussian processes to accelerate ABC methods Xuefei Cao Shijia Wang Yongdao Zhou 28 3 0 13 Apr 2024
Leveraging viscous Hamilton-Jacobi PDEs for uncertainty quantification in scientific machine learning Zongren Zou Tingwei Meng Paula Chen Jérome Darbon George Karniadakis 36 7 0 12 Apr 2024
Constrained 6-DoF Grasp Generation on Complex Shapes for Improved Dual-Arm Manipulation Gaurav Singh Sanket Kalwar Md Faizal Karim Bipasha Sen Nagamanikandan Govindan Srinath Sridhar K. M. Krishna 21 7 0 06 Apr 2024
RADIUM: Predicting and Repairing End-to-End Robot Failures using Gradient-Accelerated Sampling Charles Dawson Anjali Parashar Chuchu Fan 40 0 0 04 Apr 2024
Proximal Oracles for Optimization and Sampling Jiaming Liang Yongxin Chen 23 3 0 02 Apr 2024
Divide, Conquer, Combine Bayesian Decision Tree Sampling Jodie A. Cochrane Adrian G. Wills Sarah J. Johnson 19 1 0 26 Mar 2024
Variational Inference for Uncertainty Quantification: an Analysis of Trade-offs C. Margossian Loucas Pillaud-Vivien Lawrence K. Saul UD 63 2 0 20 Mar 2024
Energy Correction Model in the Feature Space for Out-of-Distribution Detection Marc Lafon Clément Rambour Nicolas Thome OODD 23 0 0 15 Mar 2024
Enhancing Transfer Learning with Flexible Nonparametric Posterior Sampling Hyungi Lee G. Nam Edwin Fong Juho Lee BDL 27 5 0 12 Mar 2024
Bayesian Uncertainty Estimation by Hamiltonian Monte Carlo: Applications to Cardiac MRI Segmentation Yidong Zhao João Tourais Iain Pierce Christian Nitsche T. Treibel Sebastian Weingartner Artur M. Schweidtmann Qian Tao BDL UQCV 25 5 0 04 Mar 2024
Joint Parameter and Parameterization Inference with Uncertainty Quantification through Differentiable Programming Yongquan Qu Mohamed Aziz Bhouri Pierre Gentine AI4CE 22 4 0 04 Mar 2024
Can a Confident Prior Replace a Cold Posterior? Martin Marek Brooks Paige Pavel Izmailov UQCV BDL 27 4 0 02 Mar 2024
Listening to the Noise: Blind Denoising with Gibbs Diffusion David Heurtel-Depeiges C. Margossian Ruben Ohana Bruno Régaldo-Saint Blancard DiffM 33 1 0 29 Feb 2024
Stable Training of Normalizing Flows for High-dimensional Variational Inference Daniel Andrade BDL TPM 35 1 0 26 Feb 2024
Dissecting Human and LLM Preferences Junlong Li Fan Zhou Shichao Sun Yikai Zhang Hai Zhao Pengfei Liu ALM 16 5 0 17 Feb 2024
Parameterizations for Gradient-based Markov Chain Monte Carlo on the Stiefel Manifold: A Comparative Study Masahiro Tanaka 14 1 0 12 Feb 2024
Iterated Denoising Energy Matching for Sampling from Boltzmann Densities Tara Akhound-Sadegh Jarrid Rector-Brooks A. Bose Sarthak Mittal Pablo Lemos ... Siamak Ravanbakhsh Gauthier Gidel Yoshua Bengio Nikolay Malkin Alexander Tong DiffM 32 41 0 09 Feb 2024
Non-reversible lifts of reversible diffusion processes and relaxation times Andreas Eberle Francis Lörler 27 8 0 07 Feb 2024
Diffusive Gibbs Sampling Wenlin Chen Mingtian Zhang Brooks Paige José Miguel Hernández-Lobato David Barber 14 7 0 05 Feb 2024
Fisher information dissipation for time inhomogeneous stochastic differential equations Qi Feng Xinzhe Zuo Wuchen Li 15 3 0 01 Feb 2024
A non-homogeneous Semi-Markov model for Interval Censoring M.N.M. van Lieshout R. Markwitz 21 0 0 31 Jan 2024
Ensemble-Based Annealed Importance Sampling Haoxuan Chen Lexing Ying 31 2 0 28 Jan 2024
Langevin Unlearning: A New Perspective of Noisy Gradient Descent for Machine Unlearning Eli Chien Haoyu Wang Ziang Chen Pan Li MU 22 8 0 18 Jan 2024
Reliability Analysis of Complex Systems using Subset Simulations with Hamiltonian Neural Networks Denny Thaler Somayajulu L. N. Dhulipala F. Bamer Bernd Markert Michael D. Shields 29 7 0 10 Jan 2024
Energy based diffusion generator for efficient sampling of Boltzmann distributions Yan Wang Ling Guo Hao Wu Tao Zhou DiffM 34 3 0 04 Jan 2024
Channelling Multimodality Through a Unimodalizing Transport: Warp-U Sampler and Stochastic Bridge Sampling Fei Ding David E. Jones Shiyuan He Xiao-Li Meng OT 15 0 0 01 Jan 2024
A Compact Representation for Bayesian Neural Networks By Removing Permutation Symmetry Tim Z. Xiao Weiyang Liu Robert Bamler 23 5 0 31 Dec 2023
Super-Efficient Exact Hamiltonian Monte Carlo for the von Mises Distribution Ari Pakman 17 1 0 27 Dec 2023
GAD-PVI: A General Accelerated Dynamic-Weight Particle-Based Variational Inference Framework Fangyikang Wang Huminhao Zhu Chao Zhang Han Zhao Hui Qian 19 5 0 27 Dec 2023
A Bayesian approach to functional regression: theory and computation J. Berrendero A. Coín Antonio Cuevas 19 0 0 21 Dec 2023
Risk-Sensitive Stochastic Optimal Control as Rao-Blackwellized Markovian Score Climbing Hany Abdulsamad Sahel Iqbal Adrien Corenflos Simo Särkkä 35 2 0 21 Dec 2023
Metropolis-adjusted interacting particle sampling Bjorn Sprungk Simon Weissmann Jakob Zech 13 7 0 21 Dec 2023
Scaling Up Bayesian Neural Networks with Neural Networks Zahra Moslemi Yang Meng Shiwei Lan B. Shahbaba BDL 14 1 0 19 Dec 2023
Fast sampling from constrained spaces using the Metropolis-adjusted Mirror Langevin algorithm Vishwak Srinivasan Andre Wibisono Ashia C. Wilson 22 7 0 14 Dec 2023
World Models via Policy-Guided Trajectory Diffusion Marc Rigter Jun Yamada Ingmar Posner 26 19 0 13 Dec 2023
Randomized Physics-Informed Machine Learning for Uncertainty Quantification in High-Dimensional Inverse Problems Yifei Zong D. Barajas-Solano A. Tartakovsky 25 2 0 11 Dec 2023
Transition Path Sampling with Boltzmann Generator-based MCMC Moves Michael Plainer Hannes Stärk Charlotte Bunne Stephan Günnemann 18 5 0 08 Dec 2023
Luck, skill, and depth of competition in games and social hierarchies Max Jerdee Mark E. J. Newman 14 6 0 07 Dec 2023
Improving Gradient-guided Nested Sampling for Posterior Inference Pablo Lemos Nikolay Malkin Will Handley Yoshua Bengio Y. Hezaveh Laurence Perreault Levasseur BDL 31 9 0 06 Dec 2023
Bootstrap Your Own Variance Polina Turishcheva Jason Ramapuram Sinead Williamson Dan Busbridge Eeshan Gunesh Dhekane Russ Webb UQCV 13 0 0 06 Dec 2023
Learning Energy-based Model via Dual-MCMC Teaching Jiali Cui Tian Han 19 10 0 05 Dec 2023