Dougal J. Sutherland
I'm a postdoc at the Gatsby Computational Neuroscience Unit at University College London, with Arthur Gretton.
ORCID, Google Scholar.
My research interests include:
- Learning and testing on sets and distributions: two-sample tests, distribution regression/classification/outlier detection.
- Approximate kernel embeddings, e.g. random Fourier features.
- Active learning, especially in nonstandard settings like searching for large-scale patterns.
I did my Ph.D. at Carnegie Mellon University,
working with Jeff Schneider
on machine learning.
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.
Journal and Low-Acceptance-Rate Conference Papers
Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy.
Dynamical Mass Measurements of Contaminated Galaxy Clusters Using Machine Learning.
The Astrophysical Journal (ApJ), 831, 135 (2016).
Linear-time Learning on Distributions with Approximate Kernel Embeddings.
On the Error of Random Fourier Features.
(Note that Chapter 3 / Section 4.1 of my thesis
supercedes this paper, fixing a few errors in constants and providing more results.)
A Machine Learning Approach for Dynamical Mass Measurements of Galaxy Clusters.
The Astrophysical Journal (ApJ), 803, 50 (2015).
Active learning and search on low-rank matrices.
Nonparametric kernel estimators for image classification.
Managing User Requests with the Grand Unified Task System (GUTS).
Scalable, Flexible, and Active Learning on Distributions.
Computer Science Department, Carnegie Mellon University. September 2016.
Technical Reports, Posters, etc.
Finding Representative Objects with Sparse Modeling.
CMU 10-725 Optimization course project. (Best poster award.)
Kernels on Sample Sets via Nonparametric Divergence Estimates.
Grounding Conceptual Knowledge with Spatio-Temporal Multi-Dimensional Relational Framework Trees.
University of Oklahoma Artificial Intelligence and Robotics Technical Report #1138 (2012).
Integrating Human Knowledge into a Relational Learning System.
Swarthmore College B.A. thesis.