Imperial College researchers regularly publish at top machine learning conferences. Here's an overview of the first six months of 2019:

Robust Conditional Generative Adversarial Networks
Chrysos, Grigorios G., Jean Kossaifi, and Stefanos Zafeiriou
International Conference on Learning Representations (ICLR), 2019

Adaptive MCMC via Combining Local Samplers
Kiarash Shaloudegi and András György
International Conference on Artificial Intelligence and Statistics (AISTATS), 2019

Deep Gaussian Processes with Importance-Weighted Variational Inference
Hugh Salimbeni, Vincent Dutordoir, James Hensman, Marc P. Deisenroth
International Conference on Machine Learning (ICML), 2019

Adaptive Neural Trees
Ryutaro Tanno, Kai Arulkumaran, Daniel C. Alexander, Antonio Criminisi, Aditya Nori
International Conference on Machine Learning (ICML), 2019

Learning-to-Learn Stochastic Gradient Descent with Biased Regularization
Giulia Denevi, Carlo Ciliberto, Riccardo Grazzi, Massimiliano Pontil
International Conference on Machine Learning (ICML), 2019

Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction
Giulia Luise, Dimitris Stamos, Massimiliano Pontil, Carlo Ciliberto
International Conference on Machine Learning (ICML), 2019

Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation
Ruohan Wang, Carlo Ciliberto, Pierluigi Amadori, Yiannis Demiris
International Conference on Machine Learning (ICML), 2019

CapsAndRuns: An Improved Method for Approximately Optimal Algorithm Configuration
Gellért Weisz, András György, Csaba Szepesvári
International Conference on Machine Learning (ICML), 2019

Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems
Timothy A. Mann, Sven Gowal, Huiyi Hu, Ray Jiang, Balaji Lakshminarayanan, András György, Prav Srinivasan
International Conference on Machine Learning (ICML), 2019

Graph Convolutional Gaussian Processes
Ian Walker, Ben Glocker
International Conference on Machine Learning (ICML), 2019