Resources

Books

• 

  Machine Learning: A Probabilistic Perspective

  Kevin P. Murphy

• 

  Pattern Recognition and Machine Learning

  Christopher M. Bishop

• 

  Probabilistic Graphical Models: Principles and Techniques

  Daphne Koller, Nir Friedman

• 

  Information Theory, Inference and Learning Algorithms

  David J. C. MacKay

• 

  Information, Physics, and Computation

  Marc Mézard, Andrea Montanari

• 

  An Introduction to Sequential Monte Carlo

  Nicolas Chopin , Omiros Papaspiliopoulos

• 

  Monte Carlo Methods

  Adrian Barbu , Song-Chun Zhu

• 

  Bandit Algorithms

  Tor Lattimore, Csaba Szepesvári

• 

  Algorithms for Decision Making

  Mykel J. Kochenderfer, Tim A. Wheeler, Kyle H. Wray

• 

  Reinforcement Learning, second edition: An Introduction

  Richard S. Sutton, Andrew G. Barto

• 

  Decision Making Under Uncertainty: Theory and Application

  Mykel J. Kochenderfer, Christopher Amato, Girish Chowdhary, Jonathan P. How, Hayley J. Davison Reynolds

• 

  Reinforcement Learning and Stochastic Optimization: A Unified Framework for Sequential Decisions

  Warren B. Powell

• 

  Multi-Agent Reinforcement Learning: Foundations and Modern Approaches

  Stefano V. Albrecht, Filippos Christianos, Lukas Schäfer

• 

  Calculus of Variations and Optimal Control Theory: A Concise Introduction

  Daniel Liberzon

• 

  Data-Driven Science and Engineering: Machine Learning, Dynamical Systems and Control

  Steven L. Brunton, J. Nathan Kutz

• 

  Model Predictive Control: Theory, Computation, and Design

  James B. Rawlings, David Q. Mayne, Moritz M. Diehl

• 

  Gaussian Processes for Machine Learning

  Carl Edward Rasmussen, Christopher K. I. Williams

• 

  Modelling and Control of Dynamic Systems Using Gaussian Process Models

  Juš Kocijan

• 

  Active Inference: The Free Energy Principle in Mind, Brain, and Behavior

  Thomas Parr, Giovanni Pezzulo, Karl J. Friston

• 

  Foundations of Probabilistic Programming

  Gilles Barthe, Joost-Pieter Katoen, Alexandra Silva

Courses

• 

  Probabilistic Programming

  University of British Columbia, Dr. Frank Wood, 2021

  🎥 lectures 📔 book
• 

  Probabilistic Machine Learning

  University of Tübingen, Dr. Philipp Hennig, 2023

  🎥 lectures
• 

  Probabilistic Reasoning & Machine Learning

  TU Dortmund, Prof. Stefan Harmeling, 2022

  🎥 lectures
• 

  Bayesian Statistics

  Virginia Tech, Prof. Scotland Leman, 2023

  🎥 lectures
• 

  Reinforcement Learning Theory

  Prof. Csaba Szepesvári, University of Alberta, 2022

  🎥 lectures 📔 webpage
• 

  Reinforcement Learning

  Prof. Emma Brunskill, Stanford, 2024

  🎥 lectures
• 

  Foundations of Reinforcement Learning

  Princeton University, Prof. Chi Jin, 2024

  🎥 lectures 📔 webpage
• 

  Reinforcement Learning

  DeepMind, UCL, Dr. Hado van Hasselt

  🎥 lectures
• 

  Deep Reinforcement Learning

  UC Berkeley, Prof. Sergey Levine

  🎥 lectures
• 

  Multi-Agent Reinforcement Learning

  Dr. Stefano V. Albrecht, 2023

  🎥 lectures 📔 book
• 

  Advanced Robotics

  UC Berkeley, Prof. Pieter Abbeel, 2019

  🎥 lectures
• 

  Control Bootcamp

  Steve Brunton

  🎥 lectures 📔 book
• 

  Data-Driven Control with Machine Learning

  Steve Brunton

  🎥 lectures 📔 book
• 

  Stochastic Control for Finance

  Durham University Business School, Prof. Neil Walton

  🎥 lectures

Papers

• 📔 A unifying framework for generalised Bayesian online learning in non-stationary environments, 2025

  📝 G Duran-Martin, L Sánchez-Betancourt, AY Shestopaloff, K Murphy

  Reinforcement Learning

• 📔 Least squares variational inference, 2025

  📝 Yvann Le Fay, Nicolas Chopin, Simon Barthelmé

  Approximate Inference Variational Inference

• 📔 Adaptive Variational Inference in Probabilistic Graphical Models: Beyond Bethe, Tree-Reweighted, and Convex Free Energies, 2025

  📝 Harald Leisenberger, Franz Pernkopf

  Approximate Inference Variational Inference

• 📔 Gaussian processes for dynamics learning in model predictive control, 2025

  📝 Anna Scampicchio, Elena Arcari, Amon Lahr, Melanie N. Zeilinger

  Model Predictive Control Gaussian Process

• 📔 Boosting MCTS with Free Energy Minimization, 2025

  📝 Mawaba Pascal Dao, Adrian M. Peter

  Reinforcement Learning Active Inference

• 📔 Turing.jl: a general-purpose probabilistic programming language, 2025

  📝 Tor Erlend Fjelde, Kai Xu, David Widmann, Mohamed Tarek, Cameron Pfiffer, Martin Trapp, Seth D. Axen, Xianda Sun, Markus Hauru, Penelope Yong, Will Tebbutt, Zoubin Ghahramani, Hong Ge

  Probabilistic Programming

• 📔 Generalized Bayesian deep reinforcement learning, 2024

  📝 Shreya Sinha Roy, Richard G. Everitt, Christian P. Robert, Ritabrata Dutta

  Approximate Inference Reinforcement Learning

• 📔 The Elements of Differentiable Programming, 2024

  📝 Mathieu Blondel, Vincent Roulet

  Differentiable Programming

• 📔 Inference Plans for Hybrid Particle Filtering, 2024

  📝 Ellie Y. Cheng, Eric Atkinson, Guillaume Baudart, Louis Mandel, Michael Carbin

  Approximate Inference Probabilistic Programming Monte Carlo

• 📔 Discrete Probabilistic Inference as Control in Multi-path Environments, 2024

  📝 Tristan Deleu, Padideh Nouri, Nikolay Malkin, Doina Precup, Yoshua Bengio

  Reinforcement Learning Approximate Inference

• 📔 Belief sharing: a blessing or a curse, 2024

  📝 Ozan Catal, Toon Van de Maele, Riddhi J. Pitliya, Mahault Albarracin, Candice Pattisapu, Tim Verbelen

  Active Inference

• 📔 Probabilistic Programming with Programmable Variational Inference, 2024

  📝 McCoy R. Becker, Alexander K. Lew, Xiaoyan Wang, Matin Ghavami, Mathieu Huot, Martin C. Rinard, Vikash K. Mansinghka

  Approximate Inference Probabilistic Programming Variational Inference

• 📔 BlackJAX: Composable Bayesian inference in JAX, 2024

  📝 Alberto Cabezas, Adrien Corenflos, Junpeng Lao, Rémi Louf, Antoine Carnec, Kaustubh Chaudhari, Reuben Cohn-Gordon, Jeremie Coullon, Wei Deng, Sam Duffield, Gerardo Durán-Martín, Marcin Elantkowski, Dan Foreman-Mackey, Michele Gregori, Carlos Iguaran, Ravin Kumar, Martin Lysy, Kevin Murphy, Juan Camilo Orduz, Karm Patel, Xi Wang, Rob Zinkov

  Approximate Inference Probabilistic Programming Monte Carlo

• 📔 EigenVI: score-based variational inference with orthogonal function expansions, 2024

  📝 Diana Cai, Chirag Modi, Charles C. Margossian, Robert M. Gower, David M. Blei, Lawrence K. Saul

  Approximate Inference Variational Inference

• 📔 Batch, match, and patch: low-rank approximations for score-based variational inference, 2024

  📝 Chirag Modi, Diana Cai, Lawrence K. Saul

  Approximate Inference Variational Inference

• 📔 A theoretical perspective on mode collapse in variational inference, 2024

  📝 Roman Soletskyi, Marylou Gabrié, Bruno Loureiro

  Approximate Inference Variational Inference

• 📔 Variational Inference Using Material Point Method, 2024

  📝 Yongchao Huang

  Approximate Inference Variational Inference Particle-Based Inference

• 📔 Variational Inference via Smoothed Particle Hydrodynamics, 2024

  📝 Yongchao Huang

  Approximate Inference Variational Inference Particle-Based Inference

• 📔 Particle Semi-Implicit Variational Inference, 2024

  📝 Jen Ning Lim, Adam M. Johansen

  Approximate Inference Variational Inference Particle-Based Inference

• 📔 Efficient Mixture Learning in Black-Box Variational Inference, 2024

  📝 Alexandra Hotti, Oskar Kviman, Ricky Molén, Víctor Elvira, Jens Lagergren

  Approximate Inference Variational Inference

• 📔 You Only Accept Samples Once: Fast, Self-Correcting Stochastic Variational Inference, 2024

  📝 Dominic B. Dayta

  Approximate Inference Variational Inference

• 📔 Riemannian Black Box Variational Inference, 2024

  📝 Mykola Lukashchuk, Wouter Nuijten, Dmitry V. Bagaev, Ismail Senoz, Bert de Vries

  Approximate Inference Variational Inference

• 📔 Toward Information Theoretic Active Inverse Reinforcement Learning, 2024

  📝 Ondrej Bajgar, Sid William Gould, Rohan Narayan Langford Mitta, Jonathon Liu, Oliver Newcombe, Jack Golden

  Reinforcement Learning

• 📔 Kernel Semi-Implicit Variational Inference, 2024

  📝 Ziheng Cheng, Longlin Yu, Tianyu Xie, Shiyue Zhang, Cheng Zhang

  Approximate Inference Variational Inference

• 📔 Deep Exploration with PAC-Bayes, 2024

  📝 Bahareh Tasdighi, Manuel Haussmann, Nicklas Werge, Yi-Shan Wu, Melih Kandemir

  Reinforcement Learning

• 📔 Actually Sparse Variational Gaussian Processes, 2023

  📝 Harry Jake Cunningham, Daniel Augusto de Souza, So Takao, Mark van der Wilk, Marc Peter Deisenroth

  Gaussian Process

• 📔 Thin and Deep Gaussian Processes, 2023

  📝 Daniel Augusto de Souza, Alexander Nikitin, ST John, Magnus Ross, Mauricio A. \u00c1lvarez, Marc Peter Deisenroth, Jo\u00e3o P. P. Gomes, Diego Mesquita, C\u00e9sar Lincoln C. Mattos

  Gaussian Process

• 📔 Automatic Rao-Blackwellization for Sequential Monte Carlo with Belief Propagation, 2023

  📝 Wa\u00efss Azizian, Guillaume Baudart, Marc Lelarge

  Approximate Inference Monte Carlo Probabilistic Programming

• 📔 Automatically Marginalized MCMC in Probabilistic Programming, 2023

  📝 Jinlin Lai, Javier Burroni, Hui Guan, Daniel Sheldon

  Probabilistic Programming Monte Carlo Approximate Inference

• 📔 Probabilistic Inference in Reinforcement Learning Done Right, 2023

  📝 Jean Tarbouriech, Tor Lattimore, Brendan O'Donoghue

  Reinforcement Learning

• 📔 Collective behavior from surprise minimization, 2023

  📝 Conor Heins, Beren Millidge, Lancelot da Costa, Richard Mann, Karl Friston, Iain Couzin

  Active Inference

• 📔 Bayesian Exploration Networks, 2023

  📝 Mattie Fellows, Brandon Kaplowitz, Christian Schroeder de Witt, Shimon Whiteson

  Reinforcement Learning

• 📔 PAC-Bayesian Soft Actor-Critic Learning, 2023

  📝 Bahareh Tasdighi, Abdullah Akgül, Manuel Haussmann, Kenny Kazimirzak Brink, Melih Kandemir

  Reinforcement Learning

• 📔 Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box, 2023

  📝 Ryan Giordano, Martin Ingram, Tamara Broderick

  Approximate Inference Variational Inference

• 📔 Reactive Probabilistic Programming for Scalable Bayesian Inference, 2023

  📝 Dmitry V. Bagaev

  Probabilistic Programming Approximate Inference Belief Propagation

• 📔 Reactive Message Passing for Scalable Bayesian Inference, 2023

  📝 Dmitry V. Bagaev, Bert de Vries

  Probabilistic Programming Approximate Inference

• 📔 Amortized Variational Inference: When and Why?, 2023

  📝 Charles C. Margossian, David M. Blei

  Approximate Inference Variational Inference

• 📔 RxInfer: A Julia package for reactive real-time Bayesian inference, 2023

  📝 Dmitry V. Bagaev, Albert Podusenko, Bert de Vries

  Probabilistic Programming Approximate Inference

• 📔 GFlowNets and variational inference, 2022

  📝 Nikolay Malkin, Salem Lahlou, Tristan Deleu, Xu Ji, Edward Hu, Katie Everett, Dinghuai Zhang, Yoshua Bengio

  Approximate Inference Variational Inference

• 📔 Hierarchical bayesian bandits, 2022

  📝 Joey Hong, Branislav Kveton, Manzil Zaheer, Mohammad Ghavamzadeh

  Reinforcement Learning

• 📔 A Variational Perspective on Generative Flow Networks, 2022

  📝 Heiko Zimmermann, Fredrik Lindsten, Jan-Willem van de Meent, Christian A. Naesseth

  Reinforcement Learning Approximate Inference Variational Inference

• 📔 De-Sequentialized Monte Carlo: a parallel-in-time particle smoother, 2022

  📝 Adrien Corenflos, Nicolas Chopin, Simo S채rkk채

  Approximate Inference Monte Carlo

• 📔 Second Order Ensemble Langevin Method for Sampling and Inverse Problems, 2022

  📝 Ziming Liu, Andrew M. Stuart, Yixuan Wang

  Approximate Inference Monte Carlo

• 📔 PAC-Bayesian Lifelong Learning For Multi-Armed Bandits, 2022

  📝 Hamish Flynn, David Reeb, Melih Kandemir, Jan Peters

  Reinforcement Learning

• 📔 Semi-Symbolic Inference for Efficient Streaming Probabilistic Programming, 2022

  📝 Eric Atkinson, Charles Yuan, Guillaume Baudart, Louis Mandel, Michael Carbin

  Approximate Inference Probabilistic Programming

• 📔 Bayesian Bellman Operators, 2021

  📝 Matthew Fellows, Kristian Hartikainen, Shimon Whiteson

  Reinforcement Learning

• 📔 Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes, 2021

  📝 Manuel Haussmann, Sebastian Gerwinn, Andreas Look, Barbara Rakitsch, Melih Kandemir

  Reinforcement Learning

• 📔 Variational Marginal Particle Filters, 2021

  📝 Jinlin Lai, Justin Domke, Daniel Sheldon

  Approximate Inference Variational Inference

• 📔 Sample-efficient reinforcement learning using deep Gaussian processes, 2020

  📝 Charles Gadd, Markus Heinonen, Harri L\ucc44hdesm\ucc44ki, Samuel Kaski

  Reinforcement Learning Gaussian Process

• 📔 Bayesian Neural Ordinary Differential Equations, 2020

  📝 Raj Dandekar, Karen Chung, Vaibhav Dixit, Mohamed Tarek, Aslan Garcia-Valadez, Krishna Vishal Vemula, Chris Rackauckas

  Latent ODE/PDE Differentiable Programming

• 📔 Reactive Probabilistic Programming, 2019

  📝 Guillaume Baudart, Louis Mandel, Eric Atkinson, Benjamin Sherman, Marc Pouzet, Michael Carbin

  Approximate Inference Probabilistic Programming

• 📔 Sampling-Free Variational Inference of Bayesian Neural Nets with Variance Backpropagation, 2019

  📝 Manuel Haussmann, Fred A Hamprecht, Melih Kandemir

  Approximate Inference

• 📔 Deep Gaussian Processes with Importance-Weighted Variational Inference, 2019

  📝 Hugh Salimbeni, Vincent Dutordoir, James Hensman, Marc Peter Deisenroth

  Gaussian Process

• 📔 Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro, 2019

  📝 Du Phan, Neeraj Pradhan, Martin Jankowiak

  Probabilistic Programming

• 📔 Gen: a general-purpose probabilistic programming system with programmable inference, 2019

  📝 Marco F. Cusumano-Towner, Feras A. Saad, Alexander K. Lew, Vikash K. Mansinghka

  Approximate Inference Probabilistic Programming

• 📔 Active inference: demystified and compared, 2019

  📝 Noor Sajid, Philip J. Ball, Thomas Parr, Karl J. Friston

  Active Inference Reinforcement Learning

• 📔 Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support, 2019

  📝 Yuan Zhou, Hongseok Yang, Yee Whye Teh, Tom Rainforth

  Probabilistic Programming

• 📔 Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review, 2018

  📝 Sergey Levine

  Reinforcement Learning

• 📔 Meta Reinforcement Learning with Latent Variable Gaussian Processes, 2018

  📝 Steind\u00f3r S\u00e6mundsson, Katja Hofmann, Marc Peter Deisenroth

  Gaussian Process Reinforcement Learning

• 📔 Continuous-time Gaussian process motion planning via probabilistic inference, 2018

  📝 Mukadam M, Dong J, Yan X, Dellaert F, Boots B

  Reinforcement Learning Gaussian Process

• 📔 An Introduction to Probabilistic Programming, 2018

  📝 Jan-Willem van de Meent, Brooks Paige, Hongseok Yang, Frank Wood

  Probabilistic Programming

• 📔 Pyro: Deep Universal Probabilistic Programming, 2018

  📝 Eli Bingham, Jonathan P. Chen, Martin Jankowiak, Fritz Obermeyer, Neeraj Pradhan, Theofanis Karaletsos, Rohit Singh, Paul Szerlip, Paul Horsfall, Noah D. Goodman

  Probabilistic Programming

• 📔 VIREL: A Variational Inference Framework for Reinforcement Learning, 2018

  📝 Matthew Fellows, Anuj Mahajan, Tim G. J. Rudner, Shimon Whiteson

  Reinforcement Learning

• 📔 Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model, 2018

  📝 At\u0131l\u0131m G\u00fcne\u015f Baydin, Lukas Heinrich, Wahid Bhimji, Lei Shao, Saeid Naderiparizi, Andreas Munk, Jialin Liu, Bradley Gram-Hansen, Gilles Louppe, Lawrence Meadows, Philip Torr, Victor Lee, Prabhat, Kyle Cranmer, Frank Wood

  Probabilistic Programming Approximate Inference

• 📔 Automated learning with a probabilistic programming language: Birch, 2018

  📝 Lawrence M. Murray, Thomas B. Sch\u00f6n

  Probabilistic Programming

• 📔 Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control, 2017

  📝 Sanket Kamthe, Marc Peter Deisenroth

  Gaussian Process Reinforcement Learning

• 📔 Identification of Gaussian Process State Space Models, 2017

  📝 Stefanos Eleftheriadis, Thomas F.W. Nicholson, Marc Peter Deisenroth, James Hensman

  Gaussian Process

• 📔 The reparameterization trick for acquisition functions, 2017

  📝 James T. Wilson, Riccardo Moriconi, Frank Hutter, Marc Peter Deisenroth

  Gaussian Process

• 📔 Reinforcement Learning with Deep Energy-Based Policies, 2017

  📝 Tuomas Haarnoja, Haoran Tang, Pieter Abbeel, Sergey Levine

  Reinforcement Learning Energy-Based Models

• 📔 Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs, 2017

  📝 Lawrence M. Murray, Daniel Lund\u00e9n, Jan Kudlicka, David Broman, Thomas B. Sch\u00f6n

  Probabilistic Programming Monte Carlo

• 📔 Doubly Stochastic Variational Inference for Deep Gaussian Processes, 2017

  📝 Hugh Salimbeni, Marc Deisenroth

  Gaussian Process

• 📔 Exact Bayesian inference by symbolic disintegration, 2017

  📝 Chung-chieh Shan, Norman Ramsey

  Probabilistic Programming

• 📔 Inference Compilation and Universal Probabilistic Programming, 2016

  📝 Tuan Anh Le, Atilim Gunes Baydin, Frank Wood

  Approximate Inference Probabilistic Programming

• 📔 Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm, 2016

  📝 Qiang Liu, Dilin Wang

  Approximate Inference Particle-Based Inference Variational Inference

• 📔 Bayesian Reinforcement Learning: A Survey, 2016

  📝 Mohammad Ghavamzadeh, Shie Mannor, Joelle Pineau, Aviv Tamar

  Reinforcement Learning

• 📔 Bayesian Policy Gradient and Actor-Aritic Algorithms, 2016

  📝 Mohammad Ghavamzadeh, Yaakov Engel, Michal Valko

  Reinforcement Learning

• 📔 Design and Implementation of Probabilistic Programming Language Anglican, 2016

  📝 David Tolpin, Jan Willem van de Meent, Hongseok Yang, Frank Wood

  Probabilistic Programming

• 📔 Distributed Gaussian Processes, 2015

  📝 Marc Peter Deisenroth, Jun Wei Ng

  Gaussian Process

• 📔 Gaussian Processes for Data-Efficient Learning in Robotics and Control, 2015

  📝 Marc Peter Deisenroth, Dieter Fox, Carl Edward Rasmussen

  Gaussian Process

• 📔 Langevin and Hamiltonian based Sequential MCMC for Efficient Bayesian Filtering in High-dimensional Spaces, 2015

  📝 Francois Septier, Gareth W. Peters

  Approximate Inference

• 📔 Probabilistic Programming in Python using PyMC, 2015

  📝 John Salvatier, Thomas Wiecki, Christopher Fonnesbeck

  Probabilistic Programming

• 📔 Variational Inference with Normalizing Flows, 2015

  📝 Danilo Jimenez Rezende, Shakir Mohamed

  Approximate Inference Variational Inference

• 📔 Reinforced variational inference, 2015

  📝 Weber, Theophane, Nicolas Heess, Ali Eslami, John Schulman, David Wingate, and David Silver

  Approximate Inference Variational Inference Reinforcement Learning

• 📔 Manifold Gaussian Processes for Regression, 2014

  📝 Roberto Calandra, Jan Peters, Carl Edward Rasmussen, Marc Peter Deisenroth

  Gaussian Process

• 📔 Variational Gaussian Process State-Space Models, 2014

  📝 Roger Frigola, Yutian Chen, Carl E. Rasmussen

  Gaussian Process

• 📔 Approximate real-time optimal control based on sparse Gaussian process models, 2014

  📝 Joschka Boedecker, Jost Tobias Springenberg, Jan Wülfing, Martin Riedmiller

  Approximate Inference Gaussian Process

• 📔 Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC, 2013

  📝 Roger Frigola, Fredrik Lindsten, Thomas B. Sch\u00f6n, Carl E. Rasmussen

  Gaussian Process Approximate Inference

• 📔 Auto-Encoding Variational Bayes, 2013

  📝 Diederik P Kingma, Max Welling

  Approximate Inference Variational Inference

• 📔 Expectation Propagation for approximate Bayesian inference, 2013

  📝 Thomas P. Minka

  Approximate Inference Expectation Propagation

• 📔 Expectation Propagation in Gaussian Process Dynamical Systems: Extended Version, 2012

  📝 Marc Peter Deisenroth, Shakir Mohamed

  Gaussian Process

• 📔 PILCO: A model-based and data-efficient approach to policy search, 2011

  📝 Marc Peter Deisenroth, Carl Edward Rasmussen

  Reinforcement Learning

• 📔 Optimal Reinforcement Learning for Gaussian Systems, 2011

  📝 Philipp Hennig

  Gaussian Process Reinforcement Learning

• 📔 The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo, 2011

  📝 Matthew D. Hoffman, Andrew Gelman

  Approximate Inference Monte Carlo

• 📔 Particle markov chain monte carlo methods, 2010

  📝 Andrieu, Christophe, Arnaud Doucet, and Roman Holenstein

  Approximate Inference Monte Carlo Particle-Based Inference

• 📔 Sparse spectrum Gaussian process regression, 2010

  📝 Miguel Lázaro-Gredilla, Joaquin Quiñonero-Candela, Carl Edward Rasmussen, Aníbal R Figueiras-Vidal

  Gaussian Process

• 📔 Efficient Reinforcement Learning using Gaussian Processes, 2010

  📝 Marc Peter Deisenroth

  Reinforcement Learning Gaussian Process

• 📔 Gaussian process dynamic programming, 2009

  📝 Marc Peter Deisenroth, Carl Edward Rasmussen, Jan Peters

  Gaussian Process Reinforcement Learning

• 📔 Bayesian Actor-Critic Algorithms, 2007

  📝 Mohammad Ghavamzadeh, Yaakov Engel

  Reinforcement Learning

• 📔 Bayesian Policy Gradient Algorithms, 2006

  📝 Mohammad Ghavamzadeh, Yaakov Engel

  Reinforcement Learning

• 📔 Variational Message Passing, 2005

  📝 John Winn, Christopher M. Bishop

  Approximate Inference Variational Inference

• 📔 Loopy Belief Propagation: Convergence and Effects of Message Errors, 2005

  📝 Alexander T. Ihler, John W. Fisher III, Alan S. Willsky

  Approximate Inference Belief Propagation

• 📔 Warped Gaussian processes, 2004

  📝 Edward Snelson, Carl Edward Rasmussen, Zoubin Ghahramani

  Gaussian Process

• 📔 Gaussian Processes in Reinforcement Learning, 2003

  📝 Malte Kuss, Carl E. Rasmussen

  Reinforcement Learning Gaussian Process

• 📔 Bethe free energy, Kikuchi approximations, and belief propagation algorithms, 2001

  📝 Yedidia, Jonathan S., William T. Freeman, and Yair Weiss

  Approximate Inference Variational Inference Belief Propagation

• 📔 Propagation Algorithms for Variational Bayesian Learning, 2000

  📝 Zoubin Ghahramani, Matthew J. Beal

  Approximate Inference Belief Propagation

• 📔 Fusion, propagation, and structuring in belief networks, 1986

  📝 Judea Pearl

  Approximate Inference Belief Propagation