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Author Information
Marc Deisenroth (University College London)
Professor Marc Deisenroth is the DeepMind Chair in Artificial Intelligence at University College London and the Deputy Director of UCL's Centre for Artificial Intelligence. He also holds a visiting faculty position at the University of Johannesburg and Imperial College London. Marc's research interests center around data-efficient machine learning, probabilistic modeling and autonomous decision making. Marc was Program Chair of EWRL 2012, Workshops Chair of RSS 2013, EXPO-Co-Chair of ICML 2020, and Tutorials Co-Chair of NeurIPS 2021. In 2019, Marc co-organized the Machine Learning Summer School in London. He received Paper Awards at ICRA 2014, ICCAS 2016, and ICML 2020. He is co-author of the book [Mathematics for Machine Learning](https://mml-book.github.io) published by Cambridge University Press (2020).
Matthew D. Hoffman (Google)
More from the Same Authors
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2020 Poster: Matérn Gaussian Processes on Riemannian Manifolds »
Viacheslav Borovitskiy · Alexander Terenin · Peter Mostowsky · Marc Deisenroth -
2020 Poster: Probabilistic Active Meta-Learning »
Jean Kaddour · Steindor Saemundsson · Marc Deisenroth -
2020 Tutorial: (Track1) There and Back Again: A Tale of Slopes and Expectations Q&A »
Marc Deisenroth · Cheng Soon Ong -
2020 Tutorial: (Track1) There and Back Again: A Tale of Slopes and Expectations »
Marc Deisenroth · Cheng Soon Ong -
2018 Poster: Gaussian Process Conditional Density Estimation »
Vincent Dutordoir · Hugh Salimbeni · James Hensman · Marc Deisenroth -
2018 Poster: Maximizing acquisition functions for Bayesian optimization »
James Wilson · Frank Hutter · Marc Deisenroth -
2018 Poster: Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language »
Matthew D. Hoffman · Matthew Johnson · Dustin Tran -
2018 Poster: Orthogonally Decoupled Variational Gaussian Processes »
Hugh Salimbeni · Ching-An Cheng · Byron Boots · Marc Deisenroth -
2017 Poster: Doubly Stochastic Variational Inference for Deep Gaussian Processes »
Hugh Salimbeni · Marc Deisenroth -
2017 Spotlight: Doubly Stochastic Variational Inference for Deep Gaussian Processes »
Hugh Salimbeni · Marc Deisenroth -
2017 Poster: Identification of Gaussian Process State Space Models »
Stefanos Eleftheriadis · Tom Nicholson · Marc Deisenroth · James Hensman -
2015 Workshop: Advances in Approximate Bayesian Inference »
Dustin Tran · Tamara Broderick · Stephan Mandt · James McInerney · Shakir Mohamed · Alp Kucukelbir · Matthew D. Hoffman · Neil Lawrence · David Blei -
2014 Workshop: Advances in Variational Inference »
David Blei · Shakir Mohamed · Michael Jordan · Charles Blundell · Tamara Broderick · Matthew D. Hoffman -
2014 Workshop: Novel Trends and Applications in Reinforcement Learning »
Csaba Szepesvari · Marc Deisenroth · Sergey Levine · Pedro Ortega · Brian Ziebart · Emma Brunskill · Naftali Tishby · Gerhard Neumann · Daniel Lee · Sridhar Mahadevan · Pieter Abbeel · David Silver · Vicenç Gómez -
2013 Workshop: Advances in Machine Learning for Sensorimotor Control »
Thomas Walsh · Alborz Geramifard · Marc Deisenroth · Jonathan How · Jan Peters -
2012 Poster: Expectation Propagation in Gaussian Process Dynamical Systems »
Marc Deisenroth · Shakir Mohamed -
2010 Spotlight: Online Learning for Latent Dirichlet Allocation »
Matthew D. Hoffman · David Blei · Francis Bach -
2010 Poster: Online Learning for Latent Dirichlet Allocation »
Matthew D. Hoffman · David Blei · Francis Bach -
2009 Workshop: Probabilistic Approaches for Control and Robotics »
Marc Deisenroth · Hilbert J Kappen · Emo Todorov · Duy Nguyen-Tuong · Carl Edward Rasmussen · Jan Peters