Accepted Papers

Reinforcement Learning

  • Philip Thomas and Emma Brunskill.

Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning

  • Yarin Gal, Rowan Mcallister and Carl Rasmussen.

Improving PILCO with Bayesian Neural Network Dynamics Models

  • Rowan McAllister, Mark van der Wilk and Carl Rasmussen.

Data-Efficient Policy Search using PILCO and Directed-Exploration

  • Angela Zhou, Haitham Bou Ammar and Warren Powell.

Sequential Decision Making over Networks: Coupon Targeting

  • Supratik Paul, Kamil Ciosek, Michael Osborne and Shimon Whiteson.

Alternating Optimisation and Quadrature for Robust Reinforcement Learning

Deep Learning

  • Harrison Edwards and Amos Storkey.

Neural Statistician

  • Eric Nalisnick and Padhraic Smyth.

Nonparametric Deep Generative Models with Stick-Breaking Priors

  • Cedric De Boom, Sam Leroux, Steven Bohez, Pieter Simoens, Thomas Demeester and Bart Dhoedt.

Efficiency Evaluation of Character-level RNN Training Schedules

  • Augustus Odena.

Semi-Supervised Learning with Generative Adversarial Networks

  • Pierre Thodoroff and Joelle Pineau.

Automatic seizure detection using Deep Learning

  • Enoch Yeung, Lauren Charles-Smith and Courtney Corley.

Distributed Doc2Vec Models for Fine-Grained Classification

  • Yarin Gal.

A Theoretically Grounded Application of Dropout in Recurrent Neural Networks

Probabilistic Reasoning and Bayesian Analysis

  • Willie Neiswanger and Eric Xing

Efficient Bayesian Inference with Prior Swapping

  • Marta Soare, Mohammad Ammad-Ud-Din and Samuel Kaski.

Regression with n->1 by Expert Knowledge Elicitation

  • Nina Balcan, Travis Dick and Yishay Mansour.

Data Efficient Algorithms for Multi-class Output-code Based Learning

  • Ritabrata Dutta, Paul Blomstedt and Samuel Kaski.

Bayesian inference in hierarchical models by combining independent posteriors

  • Thang Bui, Carl Rasmussen and Richard Turner.

Bayesian Gaussian Process State Space Models via Power-Expectation Propagation

  • Wittawat Jitkrittum, Zoltan Szabo, Kacper Chwialkowski and Arthur Gretton.

Distinguishing Distributions with Interpretable Features

  • Yining Wang and Aarti Singh.

Minimax Linear Regression under Measurement Constraints

  • Harsh Nisar and Bhanu Pratap Singh Rawat.

Can Evolutionary Sampling Improve Bagged Ensembles?

  • Jonathan Falk and Andrew Gelman.

No Trump: A statistical exercise in priming.

Active Learning and Bayesian Optimization

  • Rika Antonova, Joe Runde, Christoph Dann and Emma Brunskill.

Improving the Sample Efficiency of Bayesian Optimization Policy Search for Optimal Stopping Problems

  • Joachim van der Herten, Ivo Couckuyt, Dirk Deschrijver and Tom Dhaene.

Active Learning for Approximation of Expensive Functions with Normal Distributed Output Uncertainty

  • Matthew Berger, Lucas Magee, Eric Heim and Lee Seversky.

Spatial Active Learning For Cost-Effective Sensing and Feature Extraction

  • Yifei Ma, Roman Garnett and Jeff Schneider.

Active Search for Sparse Signals with Region Sensing

  • Alina Beygelzimer, Daniel Hsu, John Langford and Chicheng Zhang.

Search Improves Label for Active Learning

  • Angela Zhou, Irineo Cabreros and Karan Singh.

Dynamic Task Allocation for Crowdsourcing Settings