The value brokers: how to measure client/server payback. - Brian McWilliams (Inf. Manag. Comput. Secur., 1996)

Sparse partial least squares regression for on-line variable selection with multivariate data streams. - Brian McWilliams; Giovanni Montana (Stat. Anal. Data Min., 2010)

Predictive Subspace Clustering. - Brian McWilliams; Giovanni Montana (ICMLA (1), 2011)

Multi-view predictive partitioning in high dimensions. - Brian McWilliams; Giovanni Montana (Stat. Anal. Data Min., 2012)

Correlated random features for fast semi-supervised learning. - Brian McWilliams; David Balduzzi; Joachim M. Buhmann (NIPS, 2013)

Fast and Robust Least Squares Estimation in Corrupted Linear Models. - Brian McWilliams; Gabriel Krummenacher; Mario Lucic; Joachim M. Buhmann (NIPS, 2014)

Subspace clustering of high-dimensional data: a predictive approach. - Brian McWilliams; Giovanni Montana (Data Min. Knowl. Discov., 2014)

Variance Reduced Stochastic Gradient Descent with Neighbors. - Thomas Hofmann 001; Aurélien Lucchi; Simon Lacoste-Julien; Brian McWilliams (NIPS, 2015)

Neighborhood Watch: Stochastic Gradient Descent with Neighbors. - Thomas Hofmann 001; Aurélien Lucchi; Brian McWilliams (CoRR, 2015)

A Variance Reduced Stochastic Newton Method. - Aurélien Lucchi; Brian McWilliams; Thomas Hofmann 001 (CoRR, 2015)

Learning Representations for Outlier Detection on a Budget. - Barbora Micenková; Brian McWilliams; Ira Assent (CoRR, 2015)

DUAL-LOCO: Distributing Statistical Estimation Using Random Projections. - Christina Heinze; Brian McWilliams; Nicolai Meinshausen (AISTATS, 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 (CVPR, 2016)

Scalable Adaptive Stochastic Optimization Using Random Projections. - Gabriel Krummenacher; Brian McWilliams; Yannic Kilcher; Joachim M. Buhmann; Nicolai Meinshausen (NIPS, 2016)

The Shattered Gradients Problem: If resnets are the answer, then what is the question? - David Balduzzi; Marcus Frean; Lennox Leary; J. P. Lewis 001; Kurt Wan-Duo Ma; Brian McWilliams (ICML, 2017)

Neural Taylor Approximations: Convergence and Exploration in Rectifier Networks. - David Balduzzi; Brian McWilliams; Tony Butler-Yeoman (ICML, 2017)

Preserving Differential Privacy Between Features in Distributed Estimation. - Christina Heinze-Deml; Brian McWilliams; Nicolai Meinshausen (CoRR, 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)

Deep scattering: rendering atmospheric clouds with radiance-predicting neural networks. - Simon Kallweit; Thomas Müller 013; Brian McWilliams; Markus H. Gross; Jan Novák (ACM Trans. Graph., 2017)

PhaseNet for Video Frame Interpolation. - Simone Meyer; Abdelaziz Djelouah; Brian McWilliams; Alexander Sorkine-Hornung; Markus H. Gross; Christopher Schroers (CVPR, 2018)

A Fully Progressive Approach to Single-Image Super-Resolution. - Yifan Wang 011; Federico Perazzi; Brian McWilliams; Alexander Sorkine-Hornung; Olga Sorkine-Hornung; Christopher Schroers (CVPR Workshops, 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)

Spectrogram Feature Losses for Music Source Separation. - Abhimanyu Sahai; Romann Weber; Brian McWilliams (EUSIPCO, 2019)

Neural Importance Sampling. - Thomas Müller 013; Brian McWilliams; Fabrice Rousselle; Markus Gross 001; Jan Novák (ACM Trans. Graph., 2019)

Social Diversity and Social Preferences in Mixed-Motive Reinforcement Learning. - Kevin R. McKee; Ian Gemp; Brian McWilliams; Edgar A. Duéñez-Guzmán; Edward Hughes 001; Joel Z. Leibo (AAMAS, 2020)

Less can be more in contrastive learning. - Jovana Mitrovic; Brian McWilliams; Mélanie Rey (ICBINB@NeurIPS, 2020)

EigenGame: PCA as a Nash Equilibrium. - Ian Gemp; Brian McWilliams; Claire Vernade; Thore Graepel (ICLR, 2021)

Representation Learning via Invariant Causal Mechanisms. - Jovana Mitrovic; Brian McWilliams; Jacob C. Walker; Lars Holger Buesing; Charles Blundell (ICLR, 2021)

EigenGame Unloaded: When playing games is better than optimizing. - Ian Gemp; Brian McWilliams; Claire Vernade; Thore Graepel (ICLR, 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)

The Generalized Eigenvalue Problem as a Nash Equilibrium. - Ian Gemp; Charlie Chen; Brian McWilliams (CoRR, 2022)

TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations. - Xinyang Zhang 002; Yury Malkov; Omar Florez; Serim Park; Brian McWilliams; Jiawei Han 001; Ahmed El-Kishky (CoRR, 2022)

The Symmetric Generalized Eigenvalue Problem as a Nash Equilibrium. - Ian Gemp; Charlie Chen; Brian McWilliams (ICLR, 2023)

TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations at Twitter. - Xinyang Zhang 002; Yury Malkov; Omar Florez; Serim Park; Brian McWilliams; Jiawei Han 001; Ahmed El-Kishky (KDD, 2023)

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; et al. (ICML, 2024)

Live Music Models. - Antoine Caillon; Brian McWilliams; Cassie Tarakajian; Ian Simon; Ilaria Manco; Jesse H. Engel; Noah Constant; Yunpeng Li 008; Timo I. Denk; Alberto Lalama; Andrea Agostinelli; Cheng-Zhi Anna Huang; et al. (CoRR, 2025)

SpectroStream: A Versatile Neural Codec for General Audio. - Yunpeng Li 008; Kehang Han; Brian McWilliams; Zalan Borsos; Marco Tagliasacchi (CoRR, 2025)