19 Neurips 2019 Accepted Papers!
19 papers co-authored by the OxCSML group members have been accepted to the main program of Neurips 2019
-
Variational Bayesian Optimal Experimental Design (spotlight)
Adam Foster (University of Oxford) · Martin Jankowiak (Uber AI Labs) · Elias Bingham (Uber AI Labs) · Paul Horsfall (Uber AI Labs) · Yee Whye Teh (University of Oxford, DeepMind) · Thomas Rainforth (University of Oxford) · Noah Goodman (Stanford University) -
VIREL: A Variational Inference Framework for Reinforcement Learning (Spotlight)
Matthew Fellows (University of Oxford) · Anuj Mahajan (University of Oxford) · Tim G. J. Rudner (University of Oxford) · Shimon Whiteson (University of Oxford) -
On the Fairness of Disentangled Representations
Francesco Locatello (ETH Zürich - MPI Tübingen) · Gabriele Abbati (University of Oxford) · Thomas Rainforth (University of Oxford) · Stefan Bauer (MPI for Intelligent Systems) · Bernhard Schölkopf (MPI for Intelligent Systems) · Olivier Bachem (Google Brain) -
Hamiltonian Descent for Composite Objectives
Brendan O’Donoghue (DeepMind) · Chris J. Maddison (Institute for Advanced Study, Princeton, University of Oxford) -
Estimating Convergence of Markov chains with L-Lag Couplings
Niloy Biswas (Harvard University) · Pierre E Jacob (Harvard University) · Paul Vanetti (University of Oxford) -
Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders
Emile Mathieu (University of Oxford) · Charline Le Lan (University of Oxford) · Chris J. Maddison (Institute for Advanced Study, Princeton, University of Oxford) · Ryota Tomioka (Microsoft Research Cambridge) · Yee Whye Teh (University of Oxford, DeepMind) -
Augmented Neural ODEs
Emilien Dupont (University of Oxford) · Arnaud Doucet (University of Oxford) · Yee Whye Teh (University of Oxford, DeepMind) -
Hyperparameter Learning via Distributional Transfer
Ho Chung Law (University of Oxford) · Peilin Zhao (Tencent AI Lab) · Leung Sing Chan (University of Oxford) · Junzhou Huang (University of Texas at Arlington / Tencent AI Lab) · Dino Sejdinovic (University of Oxford) -
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning
Andreas Kirsch (University of Oxford) · Joost van Amersfoort (University of Oxford) · Yarin Gal (University of Oxford) -
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
Atilim Gunes Baydin (University of Oxford) · Lei Shao (Intel Corporation) · Wahid Bhimji (Berkeley lab) · Lukas Heinrich (New York University) · Saeid Naderiparizi (University of British Columbia) · Andreas Munk (University of British Columbia) · Jialin Liu (Lawrence Berkeley National Lab) · Bradley Gram-Hansen (University of Oxford) · Gilles Louppe (University of Liège) · Lawrence Meadows (Intel Corporation) · Philip Torr (University of Oxford) · Victor Lee (Intel Corporation) · Kyle Cranmer (New York University) · Mr. Prabhat (LBL/NERSC) · Frank Wood (University of British Columbia) -
Optimal Statistical Rates for Decentralised Non-Parametric Regression with Linear Speed-Up
Dominic Richards (University of Oxford) · Patrick Rebeschini (University of Oxford) -
Decentralized Cooperative Stochastic Bandits
David Martínez-Rubio (University of Oxford) · Varun Kanade (University of Oxford) · Patrick Rebeschini (University of Oxford) -
Implicit Regularization for Optimal Sparse Recovery
Tomas Vaskevicius (University of Oxford) · Varun Kanade (University of Oxford) · Patrick Rebeschini (University of Oxford) -
Continual Unsupervised Representation Learning
Dushyant Rao (DeepMind) · Francesco Visin (DeepMind) · Andrei Rusu (DeepMind) · Razvan Pascanu (DeepMind) · Yee Whye Teh (University of Oxford, DeepMind) · Raia Hadsell (DeepMind) -
Random Tessellation Forests
Shufei Ge (Simon Fraser University) · Shijia Wang (Nankai University) · Yee Whye Teh (University of Oxford, DeepMind) · Liangliang Wang (Simon Fraser University) · Lloyd Elliott (Simon Fraser University) -
Stacked Capsule Autoencoders
Adam Kosiorek (University of Oxford) · Sara Sabour (Google) · Yee Whye Teh (University of Oxford, DeepMind) · Geoffrey E Hinton (Google & University of Toronto) -
Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance
Kimia Nadjahi (Télécom ParisTech) · Alain Durmus (ENS Paris Saclay) · Umut Simsekli (Institut Polytechnique de Paris, University of Oxford) · Roland Badeau (Télécom ParisTech) -
Generalized Sliced Wasserstein Distances
Soheil Kolouri (HRL Laboratories LLC) · Kimia Nadjahi (Télécom ParisTech) · Umut Simsekli (Institut Polytechnique de Paris, University of Oxford) · Roland Badeau (Télécom ParisTech) · Gustavo Rohde (University of Virginia) -
First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient Noise
Thanh Huy Nguyen (Telecom ParisTech) · Umut Simsekli (Institut Polytechnique de Paris, University of Oxford) · Mert Gurbuzbalaban (Rutgers) · Gaël RICHARD (Télécom ParisTech)