NeurIPS 2018 Workshop Participation

Members of the group are organizing and participating in a number of NeurIPS 2018 Workshops and a co-located symposium.

• Tom Rainforth is giving an invited talk, “Inference Trees: Adaptive Inference with Exploration”, at the Symposium on Advances in Approximate Bayesian Inference.

• Ben Bloem-Reddy is giving an invited talk, “Left-neutrality: an old friend in the mirror”, at the NeurIPS Workshop on Bayesian Nonparametrics.

• Tom Rainforth, Ben Bloem-Reddy, and Yee Whye Teh are co-organizing (with Brooks Paige, Matt Kusner, and Rick Caruana) a workshop on Critiquing and Correcting Trends in Machine Learning, scheduled for Friday, December 7.

• Yee Whye Teh is co-organizing the NeurIPS Workshop on Continual Learning, scheduled for Friday, December 7.

Contributed papers being presented as posters and/or spotlights:

• Ho Chung Leon Law, Peilin Zhao, Junzhou Huang, and Dino Sejdinovic. Hyperparameter Learning via Distributional Transfer. NeurIPS Workshop on Meta-Learning.

• Emile Mathieu*, Tom Rainforth*, Siddharth Narayanaswamy* and Yee Whye Teh. Disentangling Disentanglement. NeurIPS Workshop on Bayesian Deep Learning. (*Equal contribution.)

• Adam Foster, Martin Jankowiak, Eli Bingham, Yee Whye Teh, Tom Rainforth and Noah Goodman. Variational Optimal Experiment Design: Efficient Automation of Adaptive Experiments. NeurIPS Workshop on Bayesian Deep Learning.

• Benjamin Bloem-Reddy and Yee Whye Teh. Neural network models of exchangeable sequences. NeurIPS Workshop on Bayesian Deep Learning.

• Hyunjik Kim, Andriy Mnih, Jonathan Schwarz, Marta Garnelo, Ali Eslami, Dan Rosenbaum, Oriol Vinyals, Yee Whye Teh. Attentive Neural Processes. NeurIPS Workshop on Bayesian Deep Learning.

• Tuan Anh Le, Hyunjik Kim, Marta Garnelo, Dan Rosenbaum, Jonathan Schwarz, Yee Whye Teh. Empirical Evaluation of Neural Process Objectives. NeurIPS Workshop on Bayesian Deep Learning.

• Aki Matsukawa, Yee Whye Teh, Dilan Gorur, Balaji Lakshminarayanan. Hybrid Models with Deep and Invertible Features. NeurIPS Workshop on Bayesian Deep Learning.

• Dieterich Lawson, George Tucker, Christian A. Naesseth, Christopher Maddison, Ryan P. Adams, Yee Whye Teh. Twisted Variational Sequential Monte Carlo.

• Balaji Lakshminarayanan, Aki Matsukawa, Dilan Gorur, Yee Whye Teh. Do Deep Generative Models Know What They Don’t Know? NeurIPS Workshop on Bayesian Deep Learning.

• Jovana Mitrovic, Peter Wirnsberger, Charles Blundell, Dino Sejdinovic, Yee Whye Teh. Infinitely Deep Infinite-Width Networks.

• Tim G. J. Rudner, Vincent Fortuin, Yee Whye Teh, Yarin Gal. On the Connection between Neural Processes and Approximate Gaussian Processes. NeurIPS Workshop on Bayesian Deep Learning.

• Tim G. J. Rudner, Marc Rußwurm, Jakub Fil, Ramona Pelich, Benjamin Bischke, Veronika Kopackova, Piotr Bilinski. Rapid Computer Vision-aided Disaster Response via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery. NeurIPS Workshop on AI for Social Good.

• Matthew Fellows, Anuj Mahajan, Tim G. J. Rudner, Shimon Whiteson. VIREL: A Variational Inference Framework for Reinforcement Learning. NeurIPS Workshop on Probabilistic Reinforcement Learning and Structured Control.

• Aidan N. Gomez, Ivan Zhang, Kevin Swersky, Yarin Gal, and Geoffrey E. Hinton. Targeted Dropout. NeurIPS Workshop on Compact Deep Neural Network Representation with Industrial Applications.

• Tammo Rukat and Christopher Yau. Bayesian Nonparametric Boolean Factor Models. NeurIPS Workshop on Bayesian Nonparametrics.

• Bradley Gram-Hansen, Patrick Helber, Indhu Varatharajan, Faiza Azam, Alejandro Coca Castro, Veronika Kopačková, Piotr Bilinski. Generating Material Maps to Map Informal Settlements. NeurIPS Workshop on Machine Learning for the Developing World.

• Stefan Webb, Tom Rainforth, Yee Whye Teh, M. Pawan Kumar. Statistical Verification of Neural Networks. NeurIPS Workshop on Security in Machine Learning.

• Matthew Willetts, Aiden Doherty, Stephen Roberts, Chris Holmes. Semi-unsupervised Learning using Deep Generative Models. NeurIPS Workshop on Bayesian Deep Learning and NeurIPS Workshop on Machine Learning for Health.

• Adam Golinski, Yee Whye Teh, Frank Wood, Tom Rainforth. Amortized Monte Carlo Integration. Symposium on Advances in Approximate Bayesian Inference.

• F.B. Fuchs, O. Groth, A.R. Kosiorek, A. Bewley, M. Wulfmeier, A. Vedaldi, I. Posner. Learning Physics with Neural Stethoscopes. NeurIPS Workshop on Modeling the Physical World: Learning, Perception, and Control.

• Jonathan Schwarz, Andrew Joseph Dudzik, Oriol Vinyals, Razvan Pascanu, Yee Whye Teh. Towards a natural benchmark for continual learning. NeurIPS Workshop on Continual Learning.