Dougal J. Sutherland

I'm a postdoc at the Gatsby Computational Neuroscience Unit at University College London, with Arthur Gretton. Contact: . ORCID, Google Scholar.

My research interests include:

Before Gatsby, I did my Ph.D. at Carnegie Mellon University, working with Jeff Schneider on machine learning. See also: various code on github, my crossvalidated/stackoverflow profiles, and my Swarthmore page older stuff from undergrad.


Below, ** denotes equal contribution.


Understanding the 2016 US Presidential Election using ecological inference and distribution regression with census microdata. Seth Flaxmann, Dougal J. Sutherland, Yu-Xiang Wang, and Yee Whye Teh.

Journal and Low-Acceptance-Rate Conference Papers

Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy. Dougal J. Sutherland, Hsiao-Yu Tung, Heiko Strathmann, Soumyajit De, Aaditya Ramdas, Alex Smola, and Arthur Gretton. ICLR 2017.
Dynamical Mass Measurements of Contaminated Galaxy Clusters Using Machine Learning. Michelle Ntampaka, Hy Trac, Dougal J. Sutherland, Sebastian Fromenteau, Barnabás Póczos, and Jeff Schneider. The Astrophysical Journal (ApJ), 831, 135 (2016).
Linear-time Learning on Distributions with Approximate Kernel Embeddings. Dougal J. Sutherland**, Junier B. Oliva**, Barnabás Póczos, and Jeff Schneider. AAAI 2016.
On the Error of Random Fourier Features. Dougal J. Sutherland and Jeff Schneider. UAI 2015. (Note that Chapter 3 / Section 4.1 of my thesis supercedes this paper, fixing a few errors in constants and providing more results.)
Active Pointillistic Pattern Search. Yifei Ma**, Dougal J. Sutherland**, Roman Garnett, and Jeff Schneider. AISTATS 2015.
A Machine Learning Approach for Dynamical Mass Measurements of Galaxy Clusters. Michelle Ntampaka, Hy Trac, Dougal J. Sutherland, Nicholas Battaglia, Barnabás Póczos, and Jeff Schneider. The Astrophysical Journal (ApJ), 803, 50 (2015).
Active learning and search on low-rank matrices. Dougal J. Sutherland, Barnabás Póczos, and Jeff Schneider. KDD 2013.

Nonparametric kernel estimators for image classification. Barnabás Póczos, Liang Xiong, Dougal J. Sutherland, and Jeff Schneider. CVPR 2012.
Managing User Requests with the Grand Unified Task System (GUTS). Andrew Stromme, Dougal J. Sutherland, Alexander Burka, Benjamin Lipton, Nicholas Felt, Rebecca Roelofs, Daniel-Elia Feist-Alexandrov, Steve Dini, and Allen Welkie (the Swarthmore College Computer Society). LISA 2012.

Ph.D. thesis

Scalable, Flexible, and Active Learning on Distributions. Computer Science Department, Carnegie Mellon University. September 2016. Committee: Jeff Schneider (chair), Barnabás Póczos, Nina Balcan, Arthur Gretton.

Technical Reports, Posters, etc.

List Mode Regression for Low Count Detection. Jay Jin, Kyle Miller, Dougal J. Sutherland, Simon Labov, Karl Nelson, and Artur Dubrawski. IEEE NSS/MIC 2016.
Deep Mean Maps. Junier B. Oliva**, Dougal J. Sutherland**, Barnabás Póczos, and Jeff Schneider.
Finding Representative Objects with Sparse Modeling. Junier B. Oliva, Dougal J. Sutherland, and Yifei Ma. CMU 10-725 Optimization course project. (Best poster award.)
Kernels on Sample Sets via Nonparametric Divergence Estimates. Dougal J. Sutherland, Liang Xiong, Barnabás Póczos, and Jeff Schneider, 2012.
Grounding Conceptual Knowledge with Spatio-Temporal Multi-Dimensional Relational Framework Trees. Matthew Bodenhamer, Thomas Palmer, Dougal J. Sutherland, and Andrew H. Fagg. University of Oklahoma Artificial Intelligence and Robotics Technical Report #1138 (2012).
Integrating Human Knowledge into a Relational Learning System. Dougal J. Sutherland. Swarthmore College B.A. thesis.