Ten papers from the group to appear at ICML 2016

Ten papers involving authors from MLG will appear at the International Conference on Machine Learning 2016. They are:

Unitary Evolution Recurrent Neural Networks
Martin Arjovsky, Amar Shah and Yoshua Bengio

Predictive Entropy Search for Multi-objective Bayesian Optimization
Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Amar Shah and Ryan P. Adams.

Pareto Frontier Learning with Expensive Correlated Objectives
Amar Shah and Zoubin Ghahramani

Continuous Deep Q-Learning with Model-based Acceleration
Shixiang Gu, Timothy Lillicrap, Ilya Sutskever and Sergey Levine.

Black-box alpha-divergence Minimization.
José Miguel Hernández-Lobato, Yingzhen Li, Mark Rowland, Daniel Hernández-Lobato, Thang Bui and Richard E. Turner.
(* joint first author)

Deep Gaussian Processes for Regression using Approximate Expectation Propagation.
Thang Bui, Daniel Hernández-Lobato, Yingzhen Li, José Miguel Hernández-Lobato and Richard E. Turner.

Scalable Discrete Sampling as a Multi-Armed Bandit Problem.
Yutian Chen and Zoubin Ghahramani

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Yarin Gal and Zoubin Ghahramani

Uprooting and Rerooting Graphical Models
Adrian Weller

Train and Test Tightness of LP Relaxations in Structured Prediction
Ofer Meshi, Mehrdad Mahdavi, Adrian Weller and David Sontag

For more details see the author’s personal webpages.