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Author Information
Marc Deisenroth (he/him) (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, and EXPO-Co-Chair of ICML 2020. 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 (he/him) -
2020 Poster: Probabilistic Active Meta-Learning »
Jean Kaddour · Steindor Saemundsson · Marc Deisenroth (he/him) -
2020 Tutorial: (Track1) There and Back Again: A Tale of Slopes and Expectations Q&A »
Marc Deisenroth (he/him) · Cheng Soon Ong -
2020 Tutorial: (Track1) There and Back Again: A Tale of Slopes and Expectations »
Marc Deisenroth (he/him) · Cheng Soon Ong -
2018 Poster: Gaussian Process Conditional Density Estimation »
Vincent Dutordoir · Hugh Salimbeni · James Hensman · Marc Deisenroth (he/him) -
2018 Poster: Maximizing acquisition functions for Bayesian optimization »
James Wilson · Frank Hutter · Marc Deisenroth (he/him) -
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 (he/him) -
2017 Poster: Doubly Stochastic Variational Inference for Deep Gaussian Processes »
Hugh Salimbeni · Marc Deisenroth (he/him) -
2017 Spotlight: Doubly Stochastic Variational Inference for Deep Gaussian Processes »
Hugh Salimbeni · Marc Deisenroth (he/him) -
2017 Poster: Identification of Gaussian Process State Space Models »
Stefanos Eleftheriadis · Tom Nicholson · Marc Deisenroth (he/him) · 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 (he/him) · 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 (he/him) · Jonathan How · Jan Peters -
2012 Poster: Expectation Propagation in Gaussian Process Dynamical Systems »
Marc Deisenroth (he/him) · 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 (he/him) · Hilbert J Kappen · Emo Todorov · Duy Nguyen-Tuong · Carl Edward Rasmussen · Jan Peters
