MCMC using Hamiltonian dynamics

9 June 2012

Papers citing "MCMC using Hamiltonian dynamics"

50 / 1,031 papers shown

Title
Position: Epistemic Artificial Intelligence is Essential for Machine Learning Models to Know When They Do Not Know Shireen Kudukkil Manchingal Fabio Cuzzolin 44 0 0 08 May 2025
Variational Formulation of the Particle Flow Particle Filter Yinzhuang Yi Jorge Cortés Nikolay A. Atanasov 29 0 0 06 May 2025
New affine invariant ensemble samplers and their dimensional scaling Yifan Chen 14 0 0 05 May 2025
Sparse mixed linear modeling with anchor-based guidance for high-entropy alloy discovery Ryo Murakami Seiji Miura Akihiro Endo Satoshi Minamoto 38 0 0 29 Apr 2025
Numerical Generalized Randomized Hamiltonian Monte Carlo for piecewise smooth target densities Jimmy Huy Tran T. S. Kleppe BDL 38 0 0 25 Apr 2025
Resonances in reflective Hamiltonian Monte Carlo Namu Kroupa Gábor Csányi Will Handley 24 0 0 16 Apr 2025
Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching Aaron J. Havens Benjamin Kurt Miller Bing Yan Carles Domingo-Enrich Anuroop Sriram ... Brandon Amos Brian Karrer Xiang Fu Guan-Horng Liu Ricky T. Q. Chen DiffM 43 0 0 16 Apr 2025
Towards Scalable Bayesian Optimization via Gradient-Informed Bayesian Neural Networks Georgios Makrygiorgos Joshua Hang Sai Ip Ali Mesbah BDL 44 0 0 14 Apr 2025
MCBlock: Boosting Neural Radiance Field Training Speed by MCTS-based Dynamic-Resolution Ray Sampling Yunpeng Tan Junlin Hao Jiangkai Wu Liming Liu Qingyang Li Xinggong Zhang 31 0 0 14 Apr 2025
Incorporating the ChEES Criterion into Sequential Monte Carlo Samplers Andrew Millard Joshua Murphy Daniel Frisch Simon Maskell BDL 41 0 0 03 Apr 2025
Neural Network Approach to Stochastic Dynamics for Smooth Multimodal Density Estimation Z. Zarezadeh N. Zarezadeh DiffM 46 1 0 22 Mar 2025
Bayesian Modeling of Zero-Shot Classifications for Urban Flood Detection Matt W Franchi N. Garg Wendy Ju Emma Pierson 47 1 0 18 Mar 2025
Do you understand epistemic uncertainty? Think again! Rigorous frequentist epistemic uncertainty estimation in regression Enrico Foglia Benjamin Bobbia N. Durasov Michaël Bauerheim Pascal Fua S. Moreau Thierry Jardin UQCV UD PER 70 0 0 17 Mar 2025
Massively Parallel Expectation Maximization For Approximate Posteriors Thomas Heap Sam Bowyer Laurence Aitchison 39 0 0 11 Mar 2025
Can Diffusion Models Provide Rigorous Uncertainty Quantification for Bayesian Inverse Problems? Evan Scope Crafts Umberto Villa 39 0 0 04 Mar 2025
LAPD: Langevin-Assisted Bayesian Active Learning for Physical Discovery Cindy Xiangrui Kong Haoyang Zheng Guang Lin AI4CE 37 0 0 04 Mar 2025
Underdamped Diffusion Bridges with Applications to Sampling Denis Blessing Julius Berner Lorenz Richter Gerhard Neumann DiffM 34 1 0 02 Mar 2025
Bayesian Active Learning for Multi-Criteria Comparative Judgement in Educational Assessment Andy Gray Alma A. M. Rahat Tom Crick Stephen Lindsay ELM 29 1 0 01 Mar 2025
Bayesian Computation in Deep Learning Wenlong Chen Bolian Li Ruqi Zhang Yingzhen Li BDL 70 0 0 25 Feb 2025
In-Context Parametric Inference: Point or Distribution Estimators? Sarthak Mittal Yoshua Bengio Nikolay Malkin Guillaume Lajoie 67 0 0 17 Feb 2025
Neural Flow Samplers with Shortcut Models Wuhao Chen Zijing Ou Yingzhen Li 77 0 0 11 Feb 2025
Microcanonical Langevin Ensembles: Advancing the Sampling of Bayesian Neural Networks Emanuel Sommer Jakob Robnik Giorgi Nozadze U. Seljak David Rügamer BDL UQCV 84 1 0 10 Feb 2025
Muti-Fidelity Prediction and Uncertainty Quantification with Laplace Neural Operators for Parametric Partial Differential Equations Haoyang Zheng Guang Lin AI4CE 41 0 0 01 Feb 2025
CreINNs: Credal-Set Interval Neural Networks for Uncertainty Estimation in Classification Tasks Kaizheng Wang Keivan K1 Shariatmadar Shireen Kudukkil Manchingal Fabio Cuzzolin David Moens Hans Hallez UQCV BDL 82 12 0 28 Jan 2025
Random Reshuffling for Stochastic Gradient Langevin Dynamics Luke Shaw Peter A. Whalley 75 3 0 28 Jan 2025
Can Bayesian Neural Networks Explicitly Model Input Uncertainty? Matias Valdenegro-Toro Marco Zullich BDL PER UQCV UD 161 0 0 14 Jan 2025
Generative Modelling with High-Order Langevin Dynamics Ziqiang Shi Rujie Liu DiffM 54 2 0 03 Jan 2025
Improving Pareto Set Learning for Expensive Multi-objective Optimization via Stein Variational Hypernetworks Minh-Duc Nguyen Phuong Mai Dinh Quang-Huy Nguyen L. P. Hoang Dung D. Le 41 1 0 23 Dec 2024
Uncertainty separation via ensemble quantile regression Navid Ansari Hans-Peter Seidel Vahid Babaei UD UQCV 67 0 0 18 Dec 2024
Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Gianni Franchi Dat Nguyen Trong Nacim Belkhir Guoxuan Xia Andrea Pilzer UQLM 73 0 0 04 Dec 2024
Exponential speed up in Monte Carlo sampling through Radial Updates Johann Ostmeyer 61 2 0 27 Nov 2024
Cautious Optimizers: Improving Training with One Line of Code Kaizhao Liang Lizhang Chen B. Liu Qiang Liu ODL 98 5 0 25 Nov 2024
Benchmarking Active Learning for NILM Dhruv Patel Ankita Kumari Jain Haikoo Khandor Xhitij Choudhary Nipun Batra 66 0 0 24 Nov 2024
On theoretical guarantees and a blessing of dimensionality for nonconvex sampling Martin Chak 35 2 0 12 Nov 2024
Running Markov Chain Monte Carlo on Modern Hardware and Software Pavel Sountsov Colin Carroll Matthew D. Hoffman BDL 29 2 0 06 Nov 2024
Your copula is a classifier in disguise: classification-based copula density estimation David Huk Mark Steel Ritabrata Dutta 25 0 0 05 Nov 2024
Denoising Fisher Training For Neural Implicit Samplers Weijian Luo Wei Deng 23 0 0 03 Nov 2024
Contrasting with Symile: Simple Model-Agnostic Representation Learning for Unlimited Modalities A. Saporta A. Puli Mark Goldstein Rajesh Ranganath SSL 29 0 0 01 Nov 2024
Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models Jinlin Lai Justin Domke Daniel Sheldon 21 0 0 31 Oct 2024
Functional Gradient Flows for Constrained Sampling Shiyue Zhang Longlin Yu Ziheng Cheng Cheng Zhang 34 0 0 30 Oct 2024
Reweighting Local Mimina with Tilted SAM Tian Li Tianyi Zhou J. Bilmes 28 0 0 30 Oct 2024
Hamiltonian Monte Carlo on ReLU Neural Networks is Inefficient Vu C. Dinh L. Ho Cuong V Nguyen 20 1 0 29 Oct 2024
ATLAS: Adapting Trajectory Lengths and Step-Size for Hamiltonian Monte Carlo Chirag Modi 21 1 0 28 Oct 2024
Hamiltonian Score Matching and Generative Flows Peter Holderrieth Yilun Xu Tommi Jaakkola 21 0 0 27 Oct 2024
Low-rank Bayesian matrix completion via geodesic Hamiltonian Monte Carlo on Stiefel manifolds Tiangang Cui Alex Gorodetsky 11 0 0 27 Oct 2024
Learned Reference-based Diffusion Sampling for multi-modal distributions Maxence Noble Louis Grenioux Marylou Gabrié Alain Durmus DiffM 29 2 0 25 Oct 2024
AutoStep: Locally adaptive involutive MCMC Tiange Liu Nikola Surjanovic Miguel Biron-Lattes Alexandre Bouchard-Coté Trevor Campbell 23 1 0 24 Oct 2024
Sacred and Profane: from the Involutive Theory of MCMC to Helpful Hamiltonian Hacks N. Glatt-Holtz Andrew J. Holbrook J. Krometis Cecilia F. Mondaini Ami D. Sheth 26 0 0 22 Oct 2024
Error estimates between SGD with momentum and underdamped Langevin diffusion Arnaud Guillin Yu Wang Lihu Xu Haoran Yang 19 1 0 22 Oct 2024
Stochastic Exploration of Real Varieties via Variety Distributions David Kahle Jonathan D Hauenstein 16 0 0 21 Oct 2024