Brian McWilliams

According to our database, Brian McWilliams authored at least 35 papers between 1996 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

MusicRL: Aligning Music Generation to Human Preferences.

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

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations at Twitter.

Xinyang Zhang, Yury Malkov, Omar Florez, Serim Park, Brian McWilliams, Jiawei Han, Ahmed El-Kishky

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

The Symmetric Generalized Eigenvalue Problem as a Nash Equilibrium.

Ian Gemp, Charlie Chen, Brian McWilliams

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations.

Xinyang Zhang, Yury Malkov, Omar Florez, Serim Park, Brian McWilliams, Jiawei Han, Ahmed El-Kishky

CoRR, 2022

The Generalized Eigenvalue Problem as a Nash Equilibrium.

Ian Gemp, Charlie Chen, Brian McWilliams

CoRR, 2022

Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet?

Nenad Tomasev, Ioana Bica, Brian McWilliams, Lars Buesing, Razvan Pascanu, Charles Blundell, Jovana Mitrovic

CoRR, 2022

EigenGame Unloaded: When playing games is better than optimizing.

Ian Gemp, Brian McWilliams, Claire Vernade, Thore Graepel

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Representation Learning via Invariant Causal Mechanisms.

Jovana Mitrovic, Brian McWilliams, Jacob C. Walker, Lars Holger Buesing, Charles Blundell

Proceedings of the 9th International Conference on Learning Representations, 2021

EigenGame: PCA as a Nash Equilibrium.

Ian Gemp, Brian McWilliams, Claire Vernade, Thore Graepel

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Less can be more in contrastive learning.

Jovana Mitrovic, Brian McWilliams, Mélanie Rey

Proceedings of the "I Can't Believe It's Not Better!" at NeurIPS Workshops, 2020

Social Diversity and Social Preferences in Mixed-Motive Reinforcement Learning.

Kevin R. McKee, Ian Gemp, Brian McWilliams, Edgar A. Duéñez-Guzmán, Edward Hughes, Joel Z. Leibo

Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, 2020

2019

Neural Importance Sampling.

Thomas Müller, Brian McWilliams, Fabrice Rousselle, Markus Gross, Jan Novák

ACM Trans. Graph., 2019

Spectrogram Feature Losses for Music Source Separation.

Abhimanyu Sahai, Romann Weber, Brian McWilliams

Proceedings of the 27th European Signal Processing Conference, 2019

2018

Denoising with kernel prediction and asymmetric loss functions.

Thijs Vogels, Fabrice Rousselle, Brian McWilliams, Gerhard Röthlin, Alex Harvill, David Adler, Mark Meyer, Jan Novák

ACM Trans. Graph., 2018

A Fully Progressive Approach to Single-Image Super-Resolution.

Yifan Wang, Federico Perazzi, Brian McWilliams, Alexander Sorkine-Hornung, Olga Sorkine-Hornung, Christopher Schroers

Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2018

PhaseNet for Video Frame Interpolation.

Simone Meyer, Abdelaziz Djelouah, Brian McWilliams, Alexander Sorkine-Hornung, Markus H. Gross, Christopher Schroers

Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

2017

Deep scattering: rendering atmospheric clouds with radiance-predicting neural networks.

Simon Kallweit, Thomas Müller, Brian McWilliams, Markus H. Gross, Jan Novák

ACM Trans. Graph., 2017

Kernel-predicting convolutional networks for denoising Monte Carlo renderings.

Steve Bako, Thijs Vogels, Brian McWilliams, Mark Meyer, Jan Novák, Alex Harvill, Pradeep Sen, Tony DeRose, Fabrice Rousselle

ACM Trans. Graph., 2017

Preserving Differential Privacy Between Features in Distributed Estimation.

Christina Heinze-Deml, Brian McWilliams, Nicolai Meinshausen

CoRR, 2017

Neural Taylor Approximations: Convergence and Exploration in Rectifier Networks.

David Balduzzi, Brian McWilliams, Tony Butler-Yeoman

Proceedings of the 34th International Conference on Machine Learning, 2017

The Shattered Gradients Problem: If resnets are the answer, then what is the question?

David Balduzzi, Marcus Frean, Lennox Leary, J. P. Lewis, Kurt Wan-Duo Ma, Brian McWilliams

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Scalable Adaptive Stochastic Optimization Using Random Projections.

Gabriel Krummenacher, Brian McWilliams, Yannic Kilcher, Joachim M. Buhmann, Nicolai Meinshausen

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation.

Federico Perazzi, Jordi Pont-Tuset, Brian McWilliams, Luc Van Gool, Markus H. Gross, Alexander Sorkine-Hornung

Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016

DUAL-LOCO: Distributing Statistical Estimation Using Random Projections.

Christina Heinze, Brian McWilliams, Nicolai Meinshausen

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Learning Representations for Outlier Detection on a Budget.

Barbora Micenková, Brian McWilliams, Ira Assent

CoRR, 2015

A Variance Reduced Stochastic Newton Method.

Aurélien Lucchi, Brian McWilliams, Thomas Hofmann

CoRR, 2015

Neighborhood Watch: Stochastic Gradient Descent with Neighbors.

Thomas Hofmann, Aurélien Lucchi, Brian McWilliams

CoRR, 2015

Variance Reduced Stochastic Gradient Descent with Neighbors.

Thomas Hofmann, Aurélien Lucchi, Simon Lacoste-Julien, Brian McWilliams

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014

Subspace clustering of high-dimensional data: a predictive approach.

Brian McWilliams, Giovanni Montana

Data Min. Knowl. Discov., 2014

Fast and Robust Least Squares Estimation in Corrupted Linear Models.

Brian McWilliams, Gabriel Krummenacher, Mario Lucic, Joachim M. Buhmann

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

2013

Correlated random features for fast semi-supervised learning.

Brian McWilliams, David Balduzzi, Joachim M. Buhmann

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

2012

Multi-view predictive partitioning in high dimensions.

Brian McWilliams, Giovanni Montana

Stat. Anal. Data Min., 2012

2011

Predictive Subspace Clustering.

Brian McWilliams, Giovanni Montana

Proceedings of the 10th International Conference on Machine Learning and Applications and Workshops, 2011

2010

Sparse partial least squares regression for on-line variable selection with multivariate data streams.

Brian McWilliams, Giovanni Montana

Stat. Anal. Data Min., 2010

1996

The value brokers: how to measure client/server payback.

Brian McWilliams

Inf. Manag. Comput. Secur., 1996