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ORCIDBrian McWilliams
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2020 – today
- 2024
[c20]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. ICML 2024- [i22]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 abs/2402.04229 (2024) - 2023
- [c19]Ian Gemp, Charlie Chen, Brian McWilliams:
The Symmetric Generalized Eigenvalue Problem as a Nash Equilibrium. ICLR 2023 - [c18]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: 5597-5607 - 2022
- [c17]Ian Gemp, Brian McWilliams, Claire Vernade, Thore Graepel:
EigenGame Unloaded: When playing games is better than optimizing. ICLR 2022 - [i21]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 abs/2201.05119 (2022) - [i20]Ian Gemp, Charlie Chen, Brian McWilliams:
The Generalized Eigenvalue Problem as a Nash Equilibrium. CoRR abs/2206.04993 (2022) - [i19]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 abs/2209.07562 (2022) - 2021
- [c16]Ian Gemp, Brian McWilliams, Claire Vernade, Thore Graepel:
EigenGame: PCA as a Nash Equilibrium. ICLR 2021 - [c15]Jovana Mitrovic, Brian McWilliams, Jacob C. Walker, Lars Holger Buesing, Charles Blundell:
Representation Learning via Invariant Causal Mechanisms. ICLR 2021 - [i18]Ian Gemp, Brian McWilliams, Claire Vernade, Thore Graepel:
EigenGame Unloaded: When playing games is better than optimizing. CoRR abs/2102.04152 (2021) - 2020
- [c14]Kevin R. McKee, Ian Gemp, Brian McWilliams, Edgar A. Duéñez-Guzmán, Edward Hughes, Joel Z. Leibo:
Social Diversity and Social Preferences in Mixed-Motive Reinforcement Learning. AAMAS 2020: 869-877 - [c13]Jovana Mitrovic, Brian McWilliams, Mélanie Rey:
Less can be more in contrastive learning. ICBINB@NeurIPS 2020: 70-75 - [i17]Kevin R. McKee, Ian Gemp, Brian McWilliams, Edgar A. Duéñez-Guzmán, Edward Hughes, Joel Z. Leibo:
Social Diversity and Social Preferences in Mixed-Motive Reinforcement Learning. CoRR abs/2002.02325 (2020) - [i16]Ian Gemp, Brian McWilliams, Claire Vernade, Thore Graepel:
EigenGame: PCA as a Nash Equilibrium. CoRR abs/2010.00554 (2020) - [i15]Jovana Mitrovic, Brian McWilliams, Jacob C. Walker, Lars Buesing, Charles Blundell:
Representation Learning via Invariant Causal Mechanisms. CoRR abs/2010.07922 (2020)
2010 – 2019
- 2019
- [j8]Thomas Müller, Brian McWilliams, Fabrice Rousselle, Markus Gross, Jan Novák:
Neural Importance Sampling. ACM Trans. Graph. 38(5): 145:1-145:19 (2019) - [c12]Abhimanyu Sahai, Romann Weber, Brian McWilliams:
Spectrogram Feature Losses for Music Source Separation. EUSIPCO 2019: 1-5 - [i14]Abhimanyu Sahai, Romann Weber, Brian McWilliams:
Spectrogram Feature Losses for Music Source Separation. CoRR abs/1901.05061 (2019) - 2018
- [j7]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): 124 (2018) - [c11]Simone Meyer, Abdelaziz Djelouah, Brian McWilliams, Alexander Sorkine-Hornung, Markus H. Gross, Christopher Schroers:
PhaseNet for Video Frame Interpolation. CVPR 2018: 498-507 - [c10]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: 864-873 - [i13]Simone Meyer, Abdelaziz Djelouah, Brian McWilliams, Alexander Sorkine-Hornung, Markus H. Gross, Christopher Schroers:
PhaseNet for Video Frame Interpolation. CoRR abs/1804.00884 (2018) - [i12]Yifan Wang, Federico Perazzi, Brian McWilliams, Alexander Sorkine-Hornung, Olga Sorkine-Hornung, Christopher Schroers:
A Fully Progressive Approach to Single-Image Super-Resolution. CoRR abs/1804.02900 (2018) - [i11]Thomas Müller, Brian McWilliams, Fabrice Rousselle, Markus Gross, Jan Novák:
Neural Importance Sampling. CoRR abs/1808.03856 (2018) - 2017
- [j6]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): 97:1-97:14 (2017) - [j5]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): 231:1-231:11 (2017) - [c9]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: 342-350 - [c8]David Balduzzi, Brian McWilliams, Tony Butler-Yeoman:
Neural Taylor Approximations: Convergence and Exploration in Rectifier Networks. ICML 2017: 351-360 - [i10]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 abs/1702.08591 (2017) - [i9]Christina Heinze-Deml, Brian McWilliams, Nicolai Meinshausen:
Preserving Differential Privacy Between Features in Distributed Estimation. CoRR abs/1703.00403 (2017) - [i8]Simon Kallweit, Thomas Müller, Brian McWilliams, Markus H. Gross, Jan Novák:
Deep Scattering: Rendering Atmospheric Clouds with Radiance-Predicting Neural Networks. CoRR abs/1709.05418 (2017) - 2016
- [c7]Christina Heinze, Brian McWilliams, Nicolai Meinshausen:
DUAL-LOCO: Distributing Statistical Estimation Using Random Projections. AISTATS 2016: 875-883 - [c6]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: 724-732 - [c5]Gabriel Krummenacher, Brian McWilliams, Yannic Kilcher, Joachim M. Buhmann, Nicolai Meinshausen:
Scalable Adaptive Stochastic Optimization Using Random Projections. NIPS 2016: 1750-1758 - [i7]David Balduzzi, Brian McWilliams, Tony Butler-Yeoman:
Neural Taylor Approximations: Convergence and Exploration in Rectifier Networks. CoRR abs/1611.02345 (2016) - [i6]Gabriel Krummenacher, Brian McWilliams, Yannic Kilcher, Joachim M. Buhmann, Nicolai Meinshausen:
Scalable Adaptive Stochastic Optimization Using Random Projections. CoRR abs/1611.06652 (2016) - 2015
- [c4]Thomas Hofmann, Aurélien Lucchi, Simon Lacoste-Julien, Brian McWilliams:
Variance Reduced Stochastic Gradient Descent with Neighbors. NIPS 2015: 2305-2313 - [i5]Aurélien Lucchi, Brian McWilliams, Thomas Hofmann:
A Variance Reduced Stochastic Newton Method. CoRR abs/1503.08316 (2015) - [i4]Christina Heinze, Brian McWilliams, Nicolai Meinshausen:
DUAL-LOCO: Distributing Statistical Estimation Using Random Projections. CoRR abs/1506.02554 (2015) - [i3]Thomas Hofmann, Aurélien Lucchi, Brian McWilliams:
Neighborhood Watch: Stochastic Gradient Descent with Neighbors. CoRR abs/1506.03662 (2015) - [i2]Barbora Micenková, Brian McWilliams, Ira Assent:
Learning Representations for Outlier Detection on a Budget. CoRR abs/1507.08104 (2015) - 2014
- [j4]Brian McWilliams, Giovanni Montana:
Subspace clustering of high-dimensional data: a predictive approach. Data Min. Knowl. Discov. 28(3): 736-772 (2014) - [c3]Brian McWilliams, Gabriel Krummenacher, Mario Lucic, Joachim M. Buhmann:
Fast and Robust Least Squares Estimation in Corrupted Linear Models. NIPS 2014: 415-423 - 2013
- [c2]Brian McWilliams, David Balduzzi, Joachim M. Buhmann:
Correlated random features for fast semi-supervised learning. NIPS 2013: 440-448 - [i1]Brian McWilliams, David Balduzzi, Joachim M. Buhmann:
Correlated random features for fast semi-supervised learning. CoRR abs/1306.5554 (2013) - 2012
- [j3]Brian McWilliams, Giovanni Montana:
Multi-view predictive partitioning in high dimensions. Stat. Anal. Data Min. 5(4): 304-321 (2012) - 2011
- [c1]Brian McWilliams, Giovanni Montana:
Predictive Subspace Clustering. ICMLA (1) 2011: 247-252 - 2010
- [j2]Brian McWilliams, Giovanni Montana:
Sparse partial least squares regression for on-line variable selection with multivariate data streams. Stat. Anal. Data Min. 3(3): 170-193 (2010)
1990 – 1999
- 1996
- [j1]Brian McWilliams:
The value brokers: how to measure client/server payback. Inf. Manag. Comput. Secur. 4(5): 15-17 (1996)
Coauthor Index
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