I am a research scientist at Disney Research Zurich heading the Deep Learning and Analytics group. I am particularly interested in deep learning, randomized algorithms and optimization for large scale learning.
I completed my Ph.D. in 2012 in the Statistics Section of the Department of Mathematics Imperial College, London under the supervision of Giovanni Montana.
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.
Until August 2015 I was a postdoctoral researcher and lecturer in the Institute of Machine Learning at ETH Zurich.
Neural Taylor Approximation: Convergence and Exploration in Rectifier Networks.
David Balduzzi, Brian McWilliams, Tony Butler-Yeoman. arXiv.
Preserving Differential Privacy Between Features in Distributed Estimation.
Christina Heinze, Brian McWilliams, Nicolai Meinshausen.
Semi-Supervised Learning with Correlated Convolutional Networks.
Ivan Ovinnikov, Brian McWilliams, Dmitry Laptev, Joachim Buhmann.
Scalable Adaptive Stochastic Optimization Using Random Projections.
Gabriel Krummenacher, Brian McWilliams, Yannic Kilcher, Joachim Buhmann, Nicolai Meinshausen. NIPS 2016. arXiv.
A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation.
Federico Perazzi, Jordi Pont-Tuset, Brian McWilliams, Markus Gross, Luc Van Gool, Alexander Sorkine-Hornung. CVPR 2016.Project page.
DUAL-LOCO: Distributing Statistical Estimation Using Random Projections.
Christina Heinze, Brian McWilliams, Nicolai Meinshausen. AISTATS 2016. arXiv. Software (maintained by Christina).
Variance Reduced Stochastic Gradient Descent with Neighbors.
Thomas Hofmann, Aurelien Lucchi, Simon Lacoste-Julien, Brian McWilliams. NIPS 28. arXiv.
DUAL-LOCO: Preserving privacy between features in distributed estimation.
Christina Heinze, Brian McWilliams, Nicolai Meinshausen. NIPS WS on Learning and privacy with incomplete data and weak supervision.
LOCO: Distributing Ridge Regression with Random Projections.
Christina Heinze, Brian McWilliams, Nicolai Meinshausen, Gabriel Krummenacher. arXiv.
Software (maintained by Christina).
Learning Representations for Outlier Detection on a Budget.
Barbora Micenková, Brian McWilliams, Ira Assent. arXiv.
A Variance Reduced Stochastic Newton Method.
Aurelien Lucchi, Brian McWilliams, Thomas Hofmann. arXiv.
RadaGrad: Random Projections for Adaptive Stochastic Optimization.
Gabriel Krummenacher, Brian McWilliams. 7th NIPS Workshop on Optimization for Machine Learning. pdf.
Fast and Robust Least Squares Estimation in Corrupted Linear Models.
With Gabriel Krummenacher, Mario Lučić, Joachim M. Buhmann. NIPS 27. arXiv. Software (maintained by Gabriel). Slides and video from the Zuri ML meetup.
Learning Outlier Ensembles: The Best of Both Worlds – Supervised and Unsupervised.
Barbora Micenková, Brian McWilliams, Ira Assent. KDD Workshop on Outlier Detection & Description under Data Diversity (ODD²). pdf.
- Subspace clustering of high-dimensional data: a predictive approach. With Giovanni Montana. Data Mining and Knowledge Discovery. 28(3): 736-772. arXiv.
- Correlated random features for fast semi-supervised learning.
With David Balduzzi, Joachim M. Buhmann. In Advances in Neural Information Processing Systems (NIPS) 26. arXiv. Matlab code. Poster.
- Pruning random features with correlated kitchen sinks (1 page abstract).
With David Balduzzi. SPARS 2013.
2012 and earlier
- Projection based models for high dimensional data. Ph.D. thesis.
- Multi-view predictive partitioning in high dimensions. With Giovanni Montana Statistical Analysis and Data Mining (2012). 5: 304-321. arXiv.
- Predictive subspace clustering.
With Giovanni Montana. In Proceedings of the 10th International Conference on Machine Learning and Applications (2011), 247-252. pdf.
- A PRESS statistic for two-block partial least squares regression.
With Giovanni Montana. In Proceedings of the 10th Conference on Computational Intelligence UK (2010), Colchester. pdf.
- Sparse partial least squares for on-line variable selection in multivariate data streams.
With Giovanni Montana Statistical Analysis and Data Mining (2010). 3: 170-193. pdf.
- Predictive modeling with high-dimensional data streams: an on-line variable selection approach.
With Giovanni Montana. Signal Processing with Adaptive Sparse Structured Representations (SPARS) 2009.
Fall Semester 2014
I co-lectured Probabilistic Graphical Models for Image Analysis with Dr. Aurelien Lucchi.
Spring Semester 2014
I was head TA of Computational Intelligence Laboratory. This course is now taught by Prof. Hofmann and headed by Martin Jaggi and Aurelien Lucchi.
The website for the probabilistic graphical models course that Dr. David Balduzzi and I taught at Uni Basel in summer 2013 is located here.
Disney Research Zurich