ML Anthology

Deisenroth, Marc

21 publications

AISTATS 2024 A Unifying Variational Framework for Gaussian Process Motion Planning Lucas C. Cosier, Rares Iordan, Sicelukwanda N. T. Zwane, Giovanni Franzese, James T. Wilson, Marc Deisenroth, Alexander Terenin, Yasemin Bekiroglu
NeurIPS 2023 Thin and Deep Gaussian Processes Daniel Augusto de Souza, Alexander Nikitin, St John, Magnus Ross, Mauricio A Álvarez, Marc Deisenroth, João Paulo Gomes, Diego Mesquita, César Lincoln Mattos
AISTATS 2021 Aligning Time Series on Incomparable Spaces Samuel Cohen, Giulia Luise, Alexander Terenin, Brandon Amos, Marc Deisenroth
AISTATS 2021 Learning Contact Dynamics Using Physically Structured Neural Networks Andreas Hochlehnert, Alexander Terenin, Steindor Saemundsson, Marc Deisenroth
AISTATS 2021 Matérn Gaussian Processes on Graphs Viacheslav Borovitskiy, Iskander Azangulov, Alexander Terenin, Peter Mostowsky, Marc Deisenroth, Nicolas Durrande
NeurIPS 2021 Vector-Valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels Michael Hutchinson, Alexander Terenin, Viacheslav Borovitskiy, So Takao, Yee W. Teh, Marc Deisenroth
ICML 2020 Efficiently Sampling Functions from Gaussian Process Posteriors James Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Deisenroth
ICML 2020 Healing Products of Gaussian Process Experts Samuel Cohen, Rendani Mbuvha, Tshilidzi Marwala, Marc Deisenroth
NeurIPS 2020 Matérn Gaussian Processes on Riemannian Manifolds Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Deisenroth
NeurIPS 2020 Probabilistic Active Meta-Learning Jean Kaddour, Steindor Saemundsson, Marc Deisenroth
AISTATS 2020 Variational Integrator Networks for Physically Structured Embeddings Steindor Saemundsson, Alexander Terenin, Katja Hofmann, Marc Deisenroth
ICML 2019 Deep Gaussian Processes with Importance-Weighted Variational Inference Hugh Salimbeni, Vincent Dutordoir, James Hensman, Marc Deisenroth
ICML 2018 Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches Simon Olofsson, Marc Deisenroth, Ruth Misener
NeurIPS 2018 Gaussian Process Conditional Density Estimation Vincent Dutordoir, Hugh Salimbeni, James Hensman, Marc Deisenroth
NeurIPS 2018 Maximizing Acquisition Functions for Bayesian Optimization James Wilson, Frank Hutter, Marc Deisenroth
NeurIPS 2018 Orthogonally Decoupled Variational Gaussian Processes Hugh Salimbeni, Ching-An Cheng, Byron Boots, Marc Deisenroth
NeurIPS 2017 Doubly Stochastic Variational Inference for Deep Gaussian Processes Hugh Salimbeni, Marc Deisenroth
NeurIPS 2017 Identification of Gaussian Process State Space Models Stefanos Eleftheriadis, Tom Nicholson, Marc Deisenroth, James Hensman
ICML 2015 Distributed Gaussian Processes Marc Deisenroth, Jun Wei Ng
NeurIPS 2012 Expectation Propagation in Gaussian Process Dynamical Systems Marc Deisenroth, Shakir Mohamed
AISTATS 2010 State-Space Inference and Learning with Gaussian Processes Ryan Turner, Marc Deisenroth, Carl Rasmussen