Warde-Farley, David

12 publications

ICML 2024 Evaluating Model Bias Requires Characterizing Its Mistakes Isabela Albuquerque, Jessica Schrouff, David Warde-Farley, Ali Taylan Cemgil, Sven Gowal, Olivia Wiles

ICLRW 2024 Evaluating Model Bias Requires Characterizing Its Mistakes Isabela Albuquerque, Jessica Schrouff, David Warde-Farley, Ali Taylan Cemgil, Sven Gowal, Olivia Wiles

ICLR 2022 Learning More Skills Through Optimistic Exploration Dj Strouse, Kate Baumli, David Warde-Farley, Volodymyr Mnih, Steven Stenberg Hansen

NeurIPS 2021 Entropic Desired Dynamics for Intrinsic Control Steven Hansen, Guillaume Desjardins, Kate Baumli, David Warde-Farley, Nicolas Heess, Simon Osindero, Volodymyr Mnih

AAAI 2021 Relative Variational Intrinsic Control Kate Baumli, David Warde-Farley, Steven Hansen, Volodymyr Mnih

ICLR 2020 Fast Task Inference with Variational Intrinsic Successor Features Steven Hansen, Will Dabney, Andre Barreto, Tom Van de Wiele, David Warde-Farley, Volodymyr Mnih

ICLR 2019 Unsupervised Control Through Non-Parametric Discriminative Rewards David Warde-Farley, Tom Van de Wiele, Tejas Kulkarni, Catalin Ionescu, Steven Hansen, Volodymyr Mnih

ICLR 2017 Improving Generative Adversarial Networks with Denoising Feature Matching David Warde-Farley, Yoshua Bengio

ICLR 2015 Self-Informed Neural Network Structure Learning David Warde-Farley, Andrew Rabinovich, Dragomir Anguelov

ICLR 2014 An Empirical Analysis of Dropout in Piecewise Linear Networks David Warde-Farley, Ian J. Goodfellow, Aaron C. Courville, Yoshua Bengio

NeurIPS 2014 Generative Adversarial Nets Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio

ICML 2013 Maxout Networks Ian Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron Courville, Yoshua Bengio