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

Recently, Imperial researchers got nine papers accepted at the Neural Information Processing Systems (NeurIPS, formerly: NIPS) conference, evidencing the increasing importance and focus of this core research area at Imperial.

Maximizing Acquisition Functions for Bayesian Optimization
James T. Wilson, Frank Hutter, Marc P. Deisenroth
Neural Information Processing Systems (NeurIPS), 2018

Orthogonally Decoupled Variational Gaussian Processes
Hugh Salimbeni, Ching-An Cheng, Byron Boots, Marc P. Deisenroth
Neural Information Processing Systems (NeurIPS), 2018

Gaussian Process Conditional Density Estimation
Vincent Dutordoir, Hugh Salimbeni, James Hensman, Marc P. Deisenroth
Neural Information Processing Systems (NeurIPS), 2018

Variational Learning on Aggregate Outputs with Gaussian Processes
Ho Chung Leon Law, Dino Sejdinovic, Ewan Cameron, Tim CD Lucas, Seth Flaxman, Katherine Battle, Kenji Fukumizu
Neural Information Processing Systems (NeurIPS), 2018

Implicit Probabilistic Integrators for ODEs
Onur Teymur, Han Cheng Lie, Tim Sullivan, Ben Calderhead
Neural Information Processing Systems (NeurIPS), 2018

Representation Balancing MDPs for Off-policy Policy Evaluation
Yao Liu, Omer Gottesman, Aniruddh Raghu, Matthieu Komorowski, A. Aldo Faisal, Finale Doshi-Velez, Emma Brunskill
Neural Information Processing Systems (NeurIPS), 2018

Manifold Structured Prediction
Alessandro Rudi, Carlo Ciliberto, Gian Maria Marconi, Lorenzo Rosasco
Neural Information Processing Systems (NeurIPS), 2018

Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance
Giulia Luise, Alessandro Rudi, Massimiliano Pontil, Carlo Ciliberto
Neural Information Processing Systems (NeurIPS), 2018

Learning to Learn Around a Common Mean
Giulia Denevi, Carlo Ciliberto, Dimitris Stamos, Massimiliano Pontil
Neural Information Processing Systems (NeurIPS), 2018