Brian McWilliams

AI Research Scientist at DeepMind

I am a research scientist at DeepMind. I am particularly interested in theoretical aspects of deep learning, unsupervised representation learning, randomized algorithms, sampling and optimization for large scale learning. I am passionate about the use of AI for conservation. See my Google Scholar profile and some representative papers (organized by topic) below for more. Contact me at bmcw @ google.

Until July 2018 I headed the Deep Learning and Analytics group at Disney Research Zurich. I worked on developing ML algorithms for production quality rendering, image and video processing, contributing to Toy Story 4 and Frozen 2.

Between 2012 and 2015 I was a postdoctoral researcher and lecturer in the Institute of Machine Learning at ETH Zurich.

Games and Multi-Agent.

Outstanding Paper Award

EigenGame receives an Outstanding Paper Award at ICLR 2021

Game theory as an engine for large-scale data analysis

B McWilliams, I Gemp, C Vernade | DeepMind blog post

EigenGame: PCA as a Nash Equilibrium

I Gemp, B McWilliams, C Vernade, T Graepel | ICLR 2021 | video | arXiv

EigenGame Unloaded When playing games is better than optimizing

I Gemp, B McWilliams, C Vernade, T Graepel | arXiv

Social Diversity and Social Preferences in Mixed-Motive Reinforcement Learning

K McKee, E Hughes, J Leibo, I Gemp, B McWilliams, E Duéñez-Guzmán | AAMAS 2020 | arXiv

The Unreasonable Effectiveness of Adam on Cycles

I Gemp, B McWilliams | Bridging Game Theory & Deep Learning 2019 | paper

Causality and Representation Learning.

Representation Learning via Invariant Causal Mechanisms

J Mitrovic, B McWilliams, J Walker, L Buesing, C Blundell | ICLR 2021 | arXiv | poster

Less can be more in Contrastive Learning

J Mitrovic, B McWilliams, M Rey | ICBINB@NeurIPS2020 (Best paper award)

Correlated random features for fast semi-supervised learning.

B McWilliams, D Balduzzi, J Buhmann | NeurIPS 2013 | arXiv

Using machine learning to accelerate ecological research

"DeepMind is collaborating with ecologists and conservationists to develop machine learning methods to help study the behavioural dynamics of an entire African animal community in the Serengeti National Park and Grumeti Reserve in Tanzania."

S Petersen, et al | blog post

Graphics, Rendering and Vision.

Neural Importance Sampling

T Müller, B McWilliams, F Rousselle, M Gross, J Novak | arXiv | video | interactive test suite | SIGGRAPH 2019

Denoising with Kernel Prediction and Asymmetric Loss Functions

T Vogels, F Rousselle, B McWilliams, G Rothlin, A Harvill, D Adler, M Meyer, J Novak | SIGGRAPH 2018 | paper | video | slides

PhaseNet for Video Frame Interpolation

S Meyer, A Djelouah, C Schroers, B McWilliams, A Sorkine-Hornung, M Gross | CVPR 2018 | arXiv | video

A Fully Progressive Approach to Single-Image Super-Resolution

Y Wang, F Perazzi, B McWilliams, A Sorkine-Hornung, O Sorkine-Hornung, C Schröers | CVPR NTIRE 2018 | arXiv | code | 2 minute summary

Deep Scattering: Rendering Atmospheric Clouds with Radiance-Predicting Neural Networks

S Kallweit, T Muller, B McWilliams, M Gross, J Novak | SIGGRAPH Asia 2017 | arXiv | video | project page | 2 minute summary

Kernel-predicting Convolutional Networks for Denoising Monte Carlo Renderings

S Bako, T Vogels, B McWilliams, M Meyer, J Novak, A Harvill, P Sen, T DeRose, F Rouselle | SIGGRAPH 2017 | Project page

A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation

F Perazzi, J Pont-Tuset, B McWilliams, M Gross, L Van Gool, A Sorkine-Hornung | CVPR 2016 | Project page

Optimization and Randomization.

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

D Balduzzi, M Frean, L Leary, JP Lewis, K Ma, B McWilliams | ICML 2017 | arXiv | video

Neural Taylor Approximation: Convergence and Exploration in Rectifier Networks

D Balduzzi, B McWilliams, T Butler-Yeoman | ICML 2017 | arXiv | video

Preserving Differential Privacy Between Features in Distributed Estimation

C Heinze-Deml, B McWilliams, N Meinshausen | Stat | arXiv

Scalable Adaptive Stochastic Optimization Using Random Projections

G Krummenacher, B McWilliams, Y Kilcher, J Buhmann, N Meinshausen | NeurIPS 2016 | arXiv

DUAL-LOCO: Distributing Statistical Estimation Using Random Projections

C Heinze, B McWilliams, N Meinshausen | AISTATS 2016 | arXiv | software

Variance Reduced Stochastic Gradient Descent with Neighbors

T Hofmann, A Lucchi, S Lacoste-Julien, B McWilliams | NeurIPS 2015 | arXiv

LOCO: Distributing Ridge Regression with Random Projections.

C Heinze, B McWilliams, N Meinshausen, G Krummenacher | arXiv | software

Fast and Robust Least Squares Estimation in Corrupted Linear Models

B McWilliams, G Krummenacher, M Lučić, J Buhmann | NeurIPS 2014 | arXiv | Software | slides | video