In addition to 6 papers in the main program of NIPS 2017 and Yee Whye Teh’s Breiman keynote lecture, OxCSML will be represented at several NIPS workshops with the following contributions.

Symposium on Interpretable Machine Learning

  • An interpretable latent variable model for attribute applicability in the Amazon catalogue
    Tammo Rukat, Dustin Lange, Cedric Archambeau

Advances in Approximate Bayesian Inference

  • On Exploration, Exploitation and Learning in Adaptive Importance Sampling
    Xiaoyu Lu, Tom Rainforth, Yuan Zhou, Yee Whye Teh, Frank Wood, Hongseok Yang, Jan-Willem van de Meen

  • Sampling and Inference for Discrete Random Probability Measures in Probabilistic Programs
    Benjamin Bloem-Reddy, Emile Mathieu, Adam Foster, Tom Rainforth, Hong Ge, Maria Lomeli, Zoubin Ghahramani, Yee Whye Teh

  • Nesting Probabilistic Programs
    Tom Rainforth

  • Inference Trees: Adaptive Inference with Exploration
    Tom Rainforth, Yuan Zhou, Xiaoyu Lu, Yee Whye Teh, Frank Wood, Hongseok Yang, Jan-Willem van de Meent

  • Faithful Model Inversion Substantially Improves Auto-encoding Variational Inference
    Stefan Webb, Adam Golinski, Robert Zinkov, Yee Whye Teh, Frank Wood

Bayesian Deep Learning

  • Variational Inference with Orthogonal Normalizing Flow
    Leonard Hasenclever, Jakub Tomczak, Rianne van den Berg, Max Welling

  • Tighter Variational Bounds are Not Necessarily Better
    Tom Rainforth, Tuan Anh Le, Maximilian Igl, Chris J. Maddison, Yee Whye Teh, Frank Wood

  • Faithful Model Inversion Substantially Improves Auto-encoding Variational Inference
    Stefan Webb, Adam Golinski, Robert Zinkov, Yee Whye Teh, Frank Wood

  • Inter-domain Deep Gaussian Processes
    Tim Georg Johann Rudner, Dino Sejdinovic

Causal Inference and Machine Learning for Intelligent Decision Making

  • Causal Inference via Kernel Deviance Measures
    Jovana Mitrovic, Dino Sejdinovic and Yee Whye Teh

Time Series

  • Bayesian Delay Embeddings for Dynamical Systems
    Niel Dhir, Adam Kosiorek, Ingmar Posner

Learning on Distributions, Functions, Graphs and Groups

  • Bayesian Distribution Regression
    Ho Chung Leon Law, Dougal J. Sutherland, Dino Sejdinovic, Seth Flaxman

Learning Disentangled Representations

  • Disentangling by Factorising
    Hyunjik Kim, Andriy Mnih

Meta-learning

  • Multiple Adaptive Bayesian Linear Regression for Scalable Bayesian Optimization with Warm Start
    Valerio Perrone, Rodolphe Jenatton, Matthias Seeger, Cedric Archambeau

Machine Learning in Computational Biology

  • Deep learning for likelihood-free inference in population genetics
    Jeffrey Chan, Valerio Perrone, Jeffrey P. Spence, Paul A. Jenkins, Sara Mathieson, Yun S. Song

Women in Machine Learning

  • Hawkes processes for sparse and modular graphs with reciprocating relationships
    Xenia Miscouridou, Francois Caron, Yee Whye Teh