Twelve papers with MLG authors to appear at NIPS 2017

“Convolutional Gaussian Processes.”, Mark van der Wilk, Carl Edward Rasmussen and James Hensman – CHOSEN FOR AN ORAL PRESENTATION.

“Data-Efficient Reinforcement Learning in Continuous State-Action Gaussian-POMDPs.”, Rowan McAllister and Carl Edward Rasmussen

“Real Time Image Saliency for Black Box Classifiers.” Piotr Dabkowski, Yarin Gal

“What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?” Alex Kendall, Yarin Gal

“Concrete Dropout” Yarin Gal, Jiri Hron, Alex Kendall

“Streaming Sparse Gaussian Process Approximations.” Thang D. Bui*, Cuong V. Nguyen*, Richard E. Turner. *Equal contribution.

“Learning Disentangled Representations with Semi-Supervised Deep Generative Models.” N. Siddharth, Brooks Paige, Jan-Willem Van de Meent, Alban Desmaison, Frank Wood, Noah D. Goodman, Pushmeet Kohli, Philip H.S. Torr

“Avoiding Discrimination through Causal Reasoning.” Niki Kilbertus, Mateo Rojas-Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, Bernhard Schölkopf

“Uprooting and Rerooting Higher-order Graphical Models.” Mark Rowland* and Adrian Weller*

“The Unreasonable Effectiveness of Random Orthogonal Embeddings.”

Krzysztof Choromanski, Mark Rowland and Adrian Weller

“From Parity to Preference: Learning with Cost-effective Notions of Fairness.” Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, Krishna Gummadi and Adrian Weller

“Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning.” Shixiang Gu, Timothy Lillicrap, Zoubin Ghahramani, Richard E. Turner, Bernhard Schölkopf, Sergey Levine

*Denotes equal contribution.