Imperial College London

15 papers from Imperial academics accepted at top machine learning conferences


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Machine learning is the driving engine of modern AI, and an increasingly important research focus at Imperial College.

Eleven papers from Imperial academics have recently been accepted at three of the top machine learning conferences, evidencing the increasing importance and focus of this core research area at Imperial:

International Conference on Machine Learning, 2018

Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches
Simon Olofsson, Marc Peter Deisenroth, Ruth Misener

Continual Reinforcement Learning with Complex Synapses
Christos Kaplanis, Murray Shanahan, Claudia Clopath

Conditional Neural Processes
Marta Garnelo, Dan Rosenbaum, Christopher Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo Rezende, Ali Eslami

Bayesian Quadrature for Multiple Related Integrals
Xiaoyue Xi, François-Xavier Briol, Mark Girolami

Stein Points
Wilson Ye Chen, Lester Mackey, Jackson Gorham, François-Xavier Briol, Chris J. Oates

Semi-supervised Learning via Compact Latent Space Clustering
Konstantinos Kamnitsas, Daniel Coelho de Castro, Loic le Folgoc, Ian Walker, Ryutaro Tanno, Daniel Rueckert, Ben Glocker, Antonio Criminisi, Aditya Nori

LeapsAndBounds: A Method for Approximately Optimal Algorithm Configuration
Gellért Weisz, András György, Csaba Szepesvári

Time Limits in Reinforcement Learning
Fabio Pardo, Arash Tavakoli, Vitaly Levdik, Petar Kormushev

Autoregressive Convolutional Neural Networks for Asynchronous Time Series
Mikolaj Binkowski, Gautier Marti, Philippe Donnat

Fast Bellman Updates for Robust MDPs
Chin Pang Ho, Marek Petrik, Wolfram Wiesemann

Conference on Uncertainty in Artificial Intelligence, 2018

Meta Reinforcement Learning with Latent Variable Gaussian Processes
Steindor Sæmundsson, Katja Hofmann, Marc Peter Deisenroth


Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models
Hugh Salimbeni, Stefanos Eleftheriadis, James Hensman, 

Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control
Sanket Kamthe, Marc P. Deisenroth

Bayesian Approaches to Distribution Regression
H. Law, D. Sutherland, D. Sejdinovic, S. Flaxman

AdaGeo: Adaptive Geometric Learning for Optimization and Sampling
G. Abbati, A. Tosi, M. Osborne, S. Flaxman


Marc Deisenroth

Marc Deisenroth
Department of Computing

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