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

• 2021 : Imitation Learning from Pixel Observations for Continuous Control »
  Samuel Cohen · Brandon Amos · Marc Deisenroth · Mikael Henaff · Eugene Vinitsky · Denis Yarats
• 2021 : On Combining Expert Demonstrations in Imitation Learning via Optimal Transport »
  ilana sebag · Samuel Cohen · Marc Deisenroth
• 2021 : Sliced Multi-Marginal Optimal Transport »
  Samuel Cohen · Alexander Terenin · Yannik Pitcan · Brandon Amos · Marc Deisenroth · Senanayak Sesh Kumar Karri
• 2022 : Actually Sparse Variational Gaussian Processes »
  Jake Cunningham · So Takao · Mark van der Wilk · Marc Deisenroth
• 2022 : Short-term Prediction and Filtering of Solar Power Using State-Space Gaussian Processes »
  So Takao · Sean Nassimiha · Peter Dudfield · Jack Kelly · Marc Deisenroth
• 2022 : Optimal Transport for Offline Imitation Learning »
  Yicheng Luo · zhengyao Jiang · Samuel Cohen · Edward Grefenstette · Marc Deisenroth
• 2021 Poster: Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels »
  Michael Hutchinson · Alexander Terenin · Viacheslav Borovitskiy · So Takao · Yee Teh · Marc Deisenroth
• 2020 : GENNI: Visualising the Geometry of Equivalences for Neural Network Identifiability »
  Arinbjörn Kolbeinsson · Nicholas Jennings · Marc Deisenroth · Daniel Lengyel · Janith Petangoda · Michalis Lazarou · Kate Highnam · John IF Falk
• 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 : Discussion Panel: Hugo Larochelle, Finale Doshi-Velez, Devi Parikh, Marc Deisenroth, Julien Mairal, Katja Hofmann, Phillip Isola, and Michael Bowling »
  Hugo Larochelle · Finale Doshi-Velez · Marc Deisenroth · Devi Parikh · Julien Mairal · Katja Hofmann · Phillip Isola · Michael Bowling
• 2020 Tutorial: (Track1) There and Back Again: A Tale of Slopes and Expectations »
  Marc Deisenroth · Cheng Soon Ong
• 2019 : Invited Talk - Marc Deisenroth »
  Marc Deisenroth
• 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 : Panel: On the Foundations and Future of Approximate Inference »
  David Blei · Zoubin Ghahramani · Katherine Heller · Tim Salimans · Max Welling · Matthew D. Hoffman
• 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 : Applications of Bayesian Optimization to Systems »
  Marc Deisenroth
• 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