Brian McWilliams.

I am a postdoctoral researcher and lecturer in the
Institute of Machine Learning at ETH Zurich. I am particularly interested in randomized algorithms and optimization for large scale learning.

I completed my Ph.D. in April 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.

Contact details.



  • Learning Representations for Outlier Detection on a Budget.
    Barbora Micenková, Brian McWilliams and Ira Assent.

  • A Variance Reduced Stochastic Newton Method.
    Aurelien Lucchi, Brian McWilliams and Thomas Hofmann. arXiv.

  • 2014

  • RadaGrad: Random Projections for Adaptive Stochastic Optimization.
    Gabriel Krummenacher and Brian McWilliams. 7th NIPS Workshop on Optimization for Machine Learning. pdf.

  • LOCO: Distributing Ridge Regression with Random Projections.
    With Christina Heinze, Nicolai Meinshausen, Gabriel Krummenacher and Hastagiri P. Vanchinathan. NIPS 2014 Workshop on Distributed Machine Learning and Matrix Computations. arXiv.

  • Fast and Robust Least Squares Estimation in Corrupted Linear Models.
    With Gabriel Krummenacher, Mario Lučić and Joachim M. Buhmann. To appear in Advances in Neural Information Processing Systems (NIPS) 27.
  • Learning Outlier Ensembles: The Best of Both Worlds – Supervised and Unsupervised.
    Barbora Micenková, Brian McWilliams and 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.

  • 2013

  • Correlated random features for fast semi-supervised learning.
    With David Balduzzi and Joachim M. Buhmann. In Advances in Neural Information Processing Systems (NIPS) 26.
  • Pruning random features with correlated kitchen sinks (1 page abstract).
    With David Balduzzi. Signal Processing with Adaptive Sparse Structured Representations (SPARS) 2013. (For poster see NIPS paper above).

  • 2012 and earlier


    Courses I am involved with.

    Fall Semester 2014

    I will be co-lecturing Probabilistic Graphical Models for Image Analysis with Dr. Aurelien Lucchi.

    Spring Semester 2014

    Head TA of Computational Intelligence Laboratory


    The website for the probabilistic graphical models course that Dr. David Balduzzi and I taught at Uni Basel in summer 2013 is located here.


    mcbrian [_at_]

    +41 44 632 82 92

    Dept of Computer Science
    CAB F 63.1
    ETH Zürich
    Universitaetstrasse 6 8092 Zurich