Geometric Methods for Sampling, Optimisation, Inference and Adaptive Agents

20 March 2022

Papers citing "Geometric Methods for Sampling, Optimisation, Inference and Adaptive Agents"

41 / 41 papers shown

Title
Sampling and estimation on manifolds using the Langevin diffusion Karthik Bharath Alexander Lewis Akash Sharma M. Tretyakov DiffM 96 5 0 22 Dec 2023
Targeted Separation and Convergence with Kernel Discrepancies Alessandro Barp Carl-Johann Simon-Gabriel Mark Girolami Lester W. Mackey 94 15 0 26 Sep 2022
Active Inference in Robotics and Artificial Agents: Survey and Challenges Pablo Lanillos Cristian Meo Corrado Pezzato A. Meera Mohamed Baioumy ... Alexander Tschantz Beren Millidge M. Wisse Christopher L. Buckley Jun Tani AI4CE 72 79 0 03 Dec 2021
Optimal friction matrix for underdamped Langevin sampling Martin Chak N. Kantas T. Lelièvre G. Pavliotis 36 9 0 30 Nov 2021
Contrastive Active Inference Pietro Mazzaglia Tim Verbelen Bart Dhoedt 56 25 0 19 Oct 2021
Optimization on manifolds: A symplectic approach G. Francca Alessandro Barp Mark Girolami Michael I. Jordan 39 11 0 23 Jul 2021
Geometry of Sample Spaces Philipp Harms P. Michor Xavier Pennec Stefan Sommer OT 177 3 0 15 Oct 2020
Control as Hybrid Inference Alexander Tschantz Beren Millidge A. Seth Christopher L. Buckley 51 9 0 11 Jul 2020
Deep active inference agents using Monte-Carlo methods Zafeirios Fountas Noor Sajid P. Mediano Karl J. Friston 67 104 0 07 Jun 2020
Structure preserving deep learning E. Celledoni Matthias Joachim Ehrhardt Christian Etmann R. McLachlan B. Owren Carola-Bibiane Schönlieb Ferdia Sherry AI4CE 85 44 0 05 Jun 2020
Optimization with Momentum: Dynamical, Control-Theoretic, and Symplectic Perspectives Michael Muehlebach Michael I. Jordan 59 65 0 28 Feb 2020
Introducing an Explicit Symplectic Integration Scheme for Riemannian Manifold Hamiltonian Monte Carlo Adam D. Cobb A. G. Baydin Andrew Markham Stephen J. Roberts 43 32 0 14 Oct 2019
Deep Active Inference as Variational Policy Gradients Beren Millidge BDL 51 103 0 08 Jul 2019
Statistical Inference for Generative Models with Maximum Mean Discrepancy François‐Xavier Briol Alessandro Barp Andrew B. Duncan Mark Girolami 51 72 0 13 Jun 2019
A Riemann-Stein Kernel Method Alessandro Barp Christine J. Oates Emilio Porcu Mark Girolami 50 22 0 11 Oct 2018
Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review Sergey Levine AI4CE BDL 78 672 0 02 May 2018
Advances in Variational Inference Cheng Zhang Judith Butepage Hedvig Kjellström Stephan Mandt BDL 175 691 0 15 Nov 2017
Geometric integrators and the Hamiltonian Monte Carlo method Nawaf Bou-Rabee J. Sanz-Serna 55 98 0 14 Nov 2017
Using Perturbed Underdamped Langevin Dynamics to Efficiently Sample from Probability Distributions A. B. Duncan Nikolas Nusken G. Pavliotis 45 41 0 29 Apr 2017
Measuring Sample Quality with Kernels Jackson Gorham Lester W. Mackey 118 223 0 06 Mar 2017
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm Qiang Liu Dilin Wang BDL 65 1,092 0 16 Aug 2016
The Zig-Zag Process and Super-Efficient Sampling for Bayesian Analysis of Big Data J. Bierkens Paul Fearnhead Gareth O. Roberts 70 233 0 11 Jul 2016
Kernel Distribution Embeddings: Universal Kernels, Characteristic Kernels and Kernel Metrics on Distributions Carl-Johann Simon-Gabriel Bernhard Schölkopf 49 92 0 18 Apr 2016
A Kernelized Stein Discrepancy for Goodness-of-fit Tests and Model Evaluation Qiang Liu Jason D. Lee Michael I. Jordan 100 483 0 10 Feb 2016
A Kernel Test of Goodness of Fit Kacper P. Chwialkowski Heiko Strathmann Arthur Gretton BDL 198 328 0 09 Feb 2016
On the Geometric Ergodicity of Hamiltonian Monte Carlo Samuel Livingstone M. Betancourt Simon Byrne Mark Girolami 89 116 0 29 Jan 2016
Variational Inference: A Review for Statisticians David M. Blei A. Kucukelbir Jon D. McAuliffe BDL 266 4,787 0 04 Jan 2016
Identifying the Optimal Integration Time in Hamiltonian Monte Carlo M. Betancourt 34 37 0 02 Jan 2016
Non-asymptotic convergence analysis for the Unadjusted Langevin Algorithm Alain Durmus Eric Moulines 64 412 0 17 Jul 2015
Training generative neural networks via Maximum Mean Discrepancy optimization Gintare Karolina Dziugaite Daniel M. Roy Zoubin Ghahramani GAN 84 530 0 14 May 2015
A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights Weijie Su Stephen P. Boyd Emmanuel J. Candes 160 1,166 0 04 Mar 2015
Information-geometric Markov Chain Monte Carlo methods using Diffusions Samuel Livingstone Mark Girolami DiffM 91 45 0 31 Mar 2014
Stochastic Gradient Hamiltonian Monte Carlo Tianqi Chen E. Fox Carlos Guestrin BDL 106 910 0 17 Feb 2014
Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances Marco Cuturi OT 212 4,259 0 04 Jun 2013
MCMC using Hamiltonian dynamics Radford M. Neal 290 3,281 0 09 Jun 2012
The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo Matthew D. Hoffman Andrew Gelman 165 4,300 0 18 Nov 2011
Planning to Be Surprised: Optimal Bayesian Exploration in Dynamic Environments Yi Sun Faustino J. Gomez Jürgen Schmidhuber 108 163 0 29 Mar 2011
Proper local scoring rules M. Parry A. Dawid Steffen Lauritzen 89 140 0 26 Jan 2011
Universality, Characteristic Kernels and RKHS Embedding of Measures Bharath K. Sriperumbudur Kenji Fukumizu Gert R. G. Lanckriet 216 530 0 03 Mar 2010
Elliptical slice sampling Iain Murray Ryan P. Adams D. MacKay 124 465 0 31 Dec 2009
Hilbert space embeddings and metrics on probability measures Bharath K. Sriperumbudur Arthur Gretton Kenji Fukumizu Bernhard Schölkopf Gert R. G. Lanckriet 208 744 0 30 Jul 2009