Brian McWilliams

Publication Activity (10 Years)

Years Active: 1996-2024
Publications (10 Years): 36

Top Topics

Nash Equilibrium

Source Separation

Statistical Estimation

Top Venues

ACM Trans. Graph.

Publications

Geoffrey Cideron, Sertan Girgin, Mauro Verzetti, Damien Vincent, Matej Kastelic, Zalán Borsos, Brian McWilliams, Victor Ungureanu, Olivier Bachem, Olivier Pietquin, Matthieu Geist, Léonard Hussenot, Neil Zeghidour, Andrea Agostinelli
MusicRL: Aligning Music Generation to Human Preferences. CoRR (2024)
Xinyang Zhang, Yury Malkov, Omar Florez, Serim Park, Brian McWilliams, Jiawei Han, Ahmed El-Kishky
TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations at Twitter. KDD (2023)
Ian Gemp, Charlie Chen, Brian McWilliams
The Symmetric Generalized Eigenvalue Problem as a Nash Equilibrium. ICLR (2023)
Nenad Tomasev, Ioana Bica, Brian McWilliams, Lars Buesing, Razvan Pascanu, Charles Blundell, Jovana Mitrovic
Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet? CoRR (2022)
Ian M. Gemp, Brian McWilliams, Claire Vernade, Thore Graepel
EigenGame Unloaded: When playing games is better than optimizing. ICLR (2022)
Ian M. Gemp, Charlie Chen, Brian McWilliams
The Generalized Eigenvalue Problem as a Nash Equilibrium. CoRR (2022)
Xinyang Zhang, Yury Malkov, Omar Florez, Serim Park, Brian McWilliams, Jiawei Han, Ahmed El-Kishky
TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations. CoRR (2022)
Jovana Mitrovic, Brian McWilliams, Jacob C. Walker, Lars Holger Buesing, Charles Blundell
Representation Learning via Invariant Causal Mechanisms. ICLR (2021)
Ian M. Gemp, Brian McWilliams, Claire Vernade, Thore Graepel
EigenGame Unloaded: When playing games is better than optimizing. CoRR (2021)
Ian M. Gemp, Brian McWilliams, Claire Vernade, Thore Graepel
EigenGame: PCA as a Nash Equilibrium. ICLR (2021)
Jovana Mitrovic, Brian McWilliams, Mélanie Rey
Less can be more in contrastive learning. ICBINB@NeurIPS (2020)
Ian M. Gemp, Brian McWilliams, Claire Vernade, Thore Graepel
EigenGame: PCA as a Nash Equilibrium. CoRR (2020)
Kevin R. McKee, Ian M. Gemp, Brian McWilliams, Edgar A. Duéñez-Guzmán, Edward Hughes, Joel Z. Leibo
Social Diversity and Social Preferences in Mixed-Motive Reinforcement Learning. AAMAS (2020)
Jovana Mitrovic, Brian McWilliams, Jacob C. Walker, Lars Buesing, Charles Blundell
Representation Learning via Invariant Causal Mechanisms. CoRR (2020)
Kevin R. McKee, Ian M. Gemp, Brian McWilliams, Edgar A. Duéñez-Guzmán, Edward Hughes, Joel Z. Leibo
Social Diversity and Social Preferences in Mixed-Motive Reinforcement Learning. CoRR (2020)
Thomas Müller, Brian McWilliams, Fabrice Rousselle, Markus Gross, Jan Novák
Neural Importance Sampling. ACM Trans. Graph. 38 (5) (2019)
Abhimanyu Sahai, Romann Weber, Brian McWilliams
Spectrogram Feature Losses for Music Source Separation. CoRR (2019)
Abhimanyu Sahai, Romann Weber, Brian McWilliams
Spectrogram Feature Losses for Music Source Separation. EUSIPCO (2019)
Yifan Wang, Federico Perazzi, Brian McWilliams, Alexander Sorkine-Hornung, Olga Sorkine-Hornung, Christopher Schroers
A Fully Progressive Approach to Single-Image Super-Resolution. CVPR Workshops (2018)
Thomas Müller, Brian McWilliams, Fabrice Rousselle, Markus Gross, Jan Novák
Neural Importance Sampling. CoRR (2018)
Yifan Wang, Federico Perazzi, Brian McWilliams, Alexander Sorkine-Hornung, Olga Sorkine-Hornung, Christopher Schroers
A Fully Progressive Approach to Single-Image Super-Resolution. CoRR (2018)
Simone Meyer, Abdelaziz Djelouah, Brian McWilliams, Alexander Sorkine-Hornung, Markus H. Gross, Christopher Schroers
PhaseNet for Video Frame Interpolation. CoRR (2018)
Simone Meyer, Abdelaziz Djelouah, Brian McWilliams, Alexander Sorkine-Hornung, Markus H. Gross, Christopher Schroers
PhaseNet for Video Frame Interpolation. CVPR (2018)
Thijs Vogels, Fabrice Rousselle, Brian McWilliams, Gerhard Röthlin, Alex Harvill, David Adler, Mark Meyer, Jan Novák
Denoising with kernel prediction and asymmetric loss functions. ACM Trans. Graph. 37 (4) (2018)
David Balduzzi, Marcus Frean, Lennox Leary, J. P. Lewis, Kurt Wan-Duo Ma, Brian McWilliams
The Shattered Gradients Problem: If resnets are the answer, then what is the question? ICML (2017)
Christina Heinze-Deml, Brian McWilliams, Nicolai Meinshausen
Preserving Differential Privacy Between Features in Distributed Estimation. CoRR (2017)
Simon Kallweit, Thomas Müller, Brian McWilliams, Markus H. Gross, Jan Novák
Deep scattering: rendering atmospheric clouds with radiance-predicting neural networks. ACM Trans. Graph. 36 (6) (2017)
Steve Bako, Thijs Vogels, Brian McWilliams, Mark Meyer, Jan Novák, Alex Harvill, Pradeep Sen, Tony DeRose, Fabrice Rousselle
Kernel-predicting convolutional networks for denoising Monte Carlo renderings. ACM Trans. Graph. 36 (4) (2017)
David Balduzzi, Brian McWilliams, Tony Butler-Yeoman
Neural Taylor Approximations: Convergence and Exploration in Rectifier Networks. ICML (2017)
Simon Kallweit, Thomas Müller, Brian McWilliams, Markus H. Gross, Jan Novák
Deep Scattering: Rendering Atmospheric Clouds with Radiance-Predicting Neural Networks. CoRR (2017)
David Balduzzi, Marcus Frean, Lennox Leary, J. P. Lewis, Kurt Wan-Duo Ma, Brian McWilliams
The Shattered Gradients Problem: If resnets are the answer, then what is the question? CoRR (2017)
Christina Heinze, Brian McWilliams, Nicolai Meinshausen
DUAL-LOCO: Distributing Statistical Estimation Using Random Projections. AISTATS (2016)
Gabriel Krummenacher, Brian McWilliams, Yannic Kilcher, Joachim M. Buhmann, Nicolai Meinshausen
Scalable Adaptive Stochastic Optimization Using Random Projections. NIPS (2016)
Gabriel Krummenacher, Brian McWilliams, Yannic Kilcher, Joachim M. Buhmann, Nicolai Meinshausen
Scalable Adaptive Stochastic Optimization Using Random Projections. CoRR (2016)
Federico Perazzi, Jordi Pont-Tuset, Brian McWilliams, Luc Van Gool, Markus H. Gross, Alexander Sorkine-Hornung
A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation. CVPR (2016)
David Balduzzi, Brian McWilliams, Tony Butler-Yeoman
Neural Taylor Approximations: Convergence and Exploration in Rectifier Networks. CoRR (2016)
Thomas Hofmann, Aurélien Lucchi, Simon Lacoste-Julien, Brian McWilliams
Variance Reduced Stochastic Gradient Descent with Neighbors. NIPS (2015)
Thomas Hofmann, Aurélien Lucchi, Brian McWilliams
Neighborhood Watch: Stochastic Gradient Descent with Neighbors. CoRR (2015)
Barbora Micenková, Brian McWilliams, Ira Assent
Learning Representations for Outlier Detection on a Budget. CoRR (2015)
Christina Heinze, Brian McWilliams, Nicolai Meinshausen
DUAL-LOCO: Distributing Statistical Estimation Using Random Projections. CoRR (2015)
Aurélien Lucchi, Brian McWilliams, Thomas Hofmann
A Variance Reduced Stochastic Newton Method. CoRR (2015)
Brian McWilliams, Giovanni Montana
Subspace clustering of high-dimensional data: a predictive approach. Data Min. Knowl. Discov. 28 (3) (2014)
Brian McWilliams, Gabriel Krummenacher, Mario Lucic, Joachim M. Buhmann
Fast and Robust Least Squares Estimation in Corrupted Linear Models. NIPS (2014)
Brian McWilliams, David Balduzzi, Joachim M. Buhmann
Correlated random features for fast semi-supervised learning. NIPS (2013)
Brian McWilliams, David Balduzzi, Joachim M. Buhmann
Correlated random features for fast semi-supervised learning. CoRR (2013)
Brian McWilliams, Giovanni Montana
Multi-view predictive partitioning in high dimensions. Stat. Anal. Data Min. 5 (4) (2012)
Brian McWilliams, Giovanni Montana
Predictive Subspace Clustering. ICMLA (1) (2011)
Brian McWilliams, Giovanni Montana
Sparse partial least squares regression for on-line variable selection with multivariate data streams. Stat. Anal. Data Min. 3 (3) (2010)
Brian McWilliams
The value brokers: how to measure client/server payback. Inf. Manag. Comput. Secur. 4 (5) (1996)