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.
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.
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
The Unreasonable Effectiveness of Adam on CyclesI 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 MethodA Lucchi, B McWilliams, T Hofmann | arXiv
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
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.