Brian McWilliams

52 papers A* 10A 2Journal 32Unranked 8

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Live Music Models. CoRR Antoine Caillon, Brian McWilliams, Cassie Tarakajian, Ian Simon, Ilaria Manco, Jesse H. Engel, Noah Constant, Yunpeng Li, Timo I. Denk, Alberto Lalama, Andrea Agostinelli, Cheng-Zhi Anna Huang, Ethan Manilow, George Brower, Hakan Erdogan, Heidi Lei, Itai Rolnick, Ivan Grishchenko, Manu Orsini, Matej Kastelic, Mauricio Zuluaga, Mauro Verzetti, Michael Dooley, Ondrej Skopek, Rafael Ferrer, Zalán Borsos, Aäron van den Oord, Douglas Eck, Eli Collins, Jason Baldridge, Tom Hume, Chris Donahue, Kehang Han, Adam Roberts
2025	J	`jnl`	SpectroStream: A Versatile Neural Codec for General Audio. CoRR Yunpeng Li, Kehang Han, Brian McWilliams, Zalan Borsos, Marco Tagliasacchi
2024	A*	`conf`	MusicRL: Aligning Music Generation to Human Preferences. ICML 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
2024	J	`jnl`	MusicRL: Aligning Music Generation to Human Preferences. CoRR 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
2023	A*	`conf`	The Symmetric Generalized Eigenvalue Problem as a Nash Equilibrium. ICLR Ian Gemp, Charlie Chen, Brian McWilliams
2023	A*	`conf`	TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations at Twitter. KDD Xinyang Zhang, Yury Malkov, Omar Florez, Serim Park, Brian McWilliams, Jiawei Han, Ahmed El-Kishky
2022	A*	`conf`	EigenGame Unloaded: When playing games is better than optimizing. ICLR Ian Gemp, Brian McWilliams, Claire Vernade, Thore Graepel
2022	J	`jnl`	Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet? CoRR Nenad Tomasev, Ioana Bica, Brian McWilliams, Lars Buesing, Razvan Pascanu, Charles Blundell, Jovana Mitrovic
2022	J	`jnl`	The Generalized Eigenvalue Problem as a Nash Equilibrium. CoRR Ian Gemp, Charlie Chen, Brian McWilliams
2022	J	`jnl`	TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations. CoRR Xinyang Zhang, Yury Malkov, Omar Florez, Serim Park, Brian McWilliams, Jiawei Han, Ahmed El-Kishky
2021	J	`jnl`	EigenGame Unloaded: When playing games is better than optimizing. CoRR Ian Gemp, Brian McWilliams, Claire Vernade, Thore Graepel
2021	A*	`conf`	EigenGame: PCA as a Nash Equilibrium. ICLR Ian Gemp, Brian McWilliams, Claire Vernade, Thore Graepel
2021	A*	`conf`	Representation Learning via Invariant Causal Mechanisms. ICLR Jovana Mitrovic, Brian McWilliams, Jacob C. Walker, Lars Holger Buesing, Charles Blundell
2020	J	`jnl`	EigenGame: PCA as a Nash Equilibrium. CoRR Ian Gemp, Brian McWilliams, Claire Vernade, Thore Graepel
2020	—	`conf`	Less can be more in contrastive learning. ICBINB@NeurIPS Jovana Mitrovic, Brian McWilliams, Mélanie Rey
2020	J	`jnl`	Representation Learning via Invariant Causal Mechanisms. CoRR Jovana Mitrovic, Brian McWilliams, Jacob C. Walker, Lars Buesing, Charles Blundell
2020	A	`conf`	Social Diversity and Social Preferences in Mixed-Motive Reinforcement Learning. AAMAS Kevin R. McKee, Ian Gemp, Brian McWilliams, Edgar A. Duéñez-Guzmán, Edward Hughes, Joel Z. Leibo
2020	J	`jnl`	Social Diversity and Social Preferences in Mixed-Motive Reinforcement Learning. CoRR Kevin R. McKee, Ian Gemp, Brian McWilliams, Edgar A. Duéñez-Guzmán, Edward Hughes, Joel Z. Leibo
2019	J	`jnl`	Neural Importance Sampling. ACM Trans. Graph. Thomas Müller, Brian McWilliams, Fabrice Rousselle, Markus Gross, Jan Novák
2019	—	`conf`	Spectrogram Feature Losses for Music Source Separation. EUSIPCO Abhimanyu Sahai, Romann Weber, Brian McWilliams
2019	J	`jnl`	Spectrogram Feature Losses for Music Source Separation. CoRR Abhimanyu Sahai, Romann Weber, Brian McWilliams
2018	—	`conf`	A Fully Progressive Approach to Single-Image Super-Resolution. CVPR Workshops Yifan Wang, Federico Perazzi, Brian McWilliams, Alexander Sorkine-Hornung, Olga Sorkine-Hornung, Christopher Schroers
2018	J	`jnl`	A Fully Progressive Approach to Single-Image Super-Resolution. CoRR Yifan Wang, Federico Perazzi, Brian McWilliams, Alexander Sorkine-Hornung, Olga Sorkine-Hornung, Christopher Schroers
2018	J	`jnl`	Denoising with kernel prediction and asymmetric loss functions. ACM Trans. Graph. Thijs Vogels, Fabrice Rousselle, Brian McWilliams, Gerhard Röthlin, Alex Harvill, David Adler, Mark Meyer, Jan Novák
2018	J	`jnl`	Neural Importance Sampling. CoRR Thomas Müller, Brian McWilliams, Fabrice Rousselle, Markus Gross, Jan Novák
2018	A*	`conf`	PhaseNet for Video Frame Interpolation. CVPR Simone Meyer, Abdelaziz Djelouah, Brian McWilliams, Alexander Sorkine-Hornung, Markus H. Gross, Christopher Schroers
2018	J	`jnl`	PhaseNet for Video Frame Interpolation. CoRR Simone Meyer, Abdelaziz Djelouah, Brian McWilliams, Alexander Sorkine-Hornung, Markus H. Gross, Christopher Schroers
2017	J	`jnl`	Deep Scattering: Rendering Atmospheric Clouds with Radiance-Predicting Neural Networks. CoRR Simon Kallweit, Thomas Müller, Brian McWilliams, Markus H. Gross, Jan Novák
2017	J	`jnl`	Deep scattering: rendering atmospheric clouds with radiance-predicting neural networks. ACM Trans. Graph. Simon Kallweit, Thomas Müller, Brian McWilliams, Markus H. Gross, Jan Novák
2017	J	`jnl`	Kernel-predicting convolutional networks for denoising Monte Carlo renderings. ACM Trans. Graph. Steve Bako, Thijs Vogels, Brian McWilliams, Mark Meyer, Jan Novák, Alex Harvill, Pradeep Sen, Tony DeRose, Fabrice Rousselle
2017	A*	`conf`	Neural Taylor Approximations: Convergence and Exploration in Rectifier Networks. ICML David Balduzzi, Brian McWilliams, Tony Butler-Yeoman
2017	J	`jnl`	Preserving Differential Privacy Between Features in Distributed Estimation. CoRR Christina Heinze-Deml, Brian McWilliams, Nicolai Meinshausen
2017	A*	`conf`	The Shattered Gradients Problem: If resnets are the answer, then what is the question? ICML David Balduzzi, Marcus Frean, Lennox Leary, J. P. Lewis, Kurt Wan-Duo Ma, Brian McWilliams
2017	J	`jnl`	The Shattered Gradients Problem: If resnets are the answer, then what is the question? CoRR David Balduzzi, Marcus Frean, Lennox Leary, J. P. Lewis, Kurt Wan-Duo Ma, Brian McWilliams
2016	A*	`conf`	A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation. CVPR Federico Perazzi, Jordi Pont-Tuset, Brian McWilliams, Luc Van Gool, Markus H. Gross, Alexander Sorkine-Hornung
2016	A	`conf`	DUAL-LOCO: Distributing Statistical Estimation Using Random Projections. AISTATS Christina Heinze, Brian McWilliams, Nicolai Meinshausen
2016	J	`jnl`	Neural Taylor Approximations: Convergence and Exploration in Rectifier Networks. CoRR David Balduzzi, Brian McWilliams, Tony Butler-Yeoman
2016	—	`conf`	Scalable Adaptive Stochastic Optimization Using Random Projections. NIPS Gabriel Krummenacher, Brian McWilliams, Yannic Kilcher, Joachim M. Buhmann, Nicolai Meinshausen
2016	J	`jnl`	Scalable Adaptive Stochastic Optimization Using Random Projections. CoRR Gabriel Krummenacher, Brian McWilliams, Yannic Kilcher, Joachim M. Buhmann, Nicolai Meinshausen
2015	J	`jnl`	A Variance Reduced Stochastic Newton Method. CoRR Aurélien Lucchi, Brian McWilliams, Thomas Hofmann
2015	J	`jnl`	DUAL-LOCO: Distributing Statistical Estimation Using Random Projections. CoRR Christina Heinze, Brian McWilliams, Nicolai Meinshausen
2015	J	`jnl`	Learning Representations for Outlier Detection on a Budget. CoRR Barbora Micenková, Brian McWilliams, Ira Assent
2015	J	`jnl`	Neighborhood Watch: Stochastic Gradient Descent with Neighbors. CoRR Thomas Hofmann, Aurélien Lucchi, Brian McWilliams
2015	—	`conf`	Variance Reduced Stochastic Gradient Descent with Neighbors. NIPS Thomas Hofmann, Aurélien Lucchi, Simon Lacoste-Julien, Brian McWilliams
2014	—	`conf`	Fast and Robust Least Squares Estimation in Corrupted Linear Models. NIPS Brian McWilliams, Gabriel Krummenacher, Mario Lucic, Joachim M. Buhmann
2014	J	`jnl`	Subspace clustering of high-dimensional data: a predictive approach. Data Min. Knowl. Discov. Brian McWilliams, Giovanni Montana
2013	—	`conf`	Correlated random features for fast semi-supervised learning. NIPS Brian McWilliams, David Balduzzi, Joachim M. Buhmann
2013	J	`jnl`	Correlated random features for fast semi-supervised learning. CoRR Brian McWilliams, David Balduzzi, Joachim M. Buhmann
2012	J	`jnl`	Multi-view predictive partitioning in high dimensions. Stat. Anal. Data Min. Brian McWilliams, Giovanni Montana
2011	—	`conf`	Predictive Subspace Clustering. ICMLA (1) Brian McWilliams, Giovanni Montana
2010	J	`jnl`	Sparse partial least squares regression for on-line variable selection with multivariate data streams. Stat. Anal. Data Min. Brian McWilliams, Giovanni Montana
1996	J	`jnl`	The value brokers: how to measure client/server payback. Inf. Manag. Comput. Secur. Brian McWilliams