## Brian McWilliams

AI Research Scientist at DeepMind

I am a research scientist at DeepMind. I am particularly interested in theoretical aspects of deep 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 papers 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. Once, a video I narrated went a bit viral.

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

I completed my Ph.D. in 2012 in the Statistics Section of the Department of Mathematics Imperial College, London. Previously, I did the MSc. in Informatics specialising in Machine Learning, Neuroinformatics and Intelligent Robotics at the University of Edinburgh and a BEng. in Computer Systems Engineering at the University of Warwick.

## New.

**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

**Using machine learning to accelerate ecological research**

S Petersen, et al | blog post

## Selected Papers.

**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

**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 | 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

**Preserving Differential Privacy Between Features in Distributed Estimation**

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

**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

**DUAL-LOCO: Distributing Statistical Estimation Using Random Projections**

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

**More Papers.**

**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 | paper

**Spectrogram Feature Losses for Music Source Separation**. A Sahai, R Weber, B McWilliams | EUSIPCO 2019 | arXiv

**Automatically Learning an Intuitive Animation Interface From a Collection of Human Motion Clips**. M Lüdi, M Guay, B McWilliams, R W Sumner | CGVCVIP 2017.

**Learning Representations for Outlier Detection on a Budget**. B Micenková, B McWilliams, I Assent | arXiv

**A Variance Reduced Stochastic Newton Method**A Lucchi, B McWilliams, T Hofmann | arXiv

**RadaGrad: Random Projections for Adaptive Stochastic Optimization**. G Krummenacher, B McWilliams | 7th NIPS WS on OptML

**Learning Outlier Ensembles: The Best of Both Worlds – Supervised and Unsupervised**. B Micenková, B McWilliams, I Assent | KDD WS on ODD2

**Subspace clustering of high-dimensional data: a predictive approach**. B McWilliams, G Montana | Data Mining and Knowledge Discovery. 28(3): 736-772 | arXiv

**Pruning random features with correlated kitchen sinks**B McWilliams, D Balduzzi | SPARS 2013.

**Projection based models for high dimensional data**. Ph.D. thesis.

**Multi-view predictive partitioning in high dimensions**. B McWilliams, G Montana | Statistical Analysis and Data Mining (2012). 5: 304-321. arXiv.

**Predictive subspace clustering**. B McWilliams, G Montana. In Proceedings of the 10th International Conference on Machine Learning and Applications (2011), 247-252 | paper

**A PRESS statistic for two-block partial least squares regression**. B McWilliams, G Montana | In Proceedings of the 10th Conference on Computational Intelligence UK (2010), Colchester | paper

**Sparse partial least squares for on-line variable selection in multivariate data streams**. B McWilliams, G Montana | Statistical Analysis and Data Mining (2010). 3: 170-193

**Predictive modeling with high-dimensional data streams: an on-line variable selection approach**. B McWilliams, G Montana | SPARS 2009.