Danica J. Sutherlandshe/they
Assistant Professor, UBC Computer Science;
CIFAR AI Chair, Amii
Queer in AI
CAIDA
PIHOT / Kantorovich Initiative
dsuth[a t]cs.ubc.ca
or
djs[a t]djsutherland.ml
CV
orcid
github
crossvalidated
twitter
Prospective students:
Like most North American schools, we only accept applications through the departmental process,
with a deadline in December.
There is no reason to contact me about admissions at this time of year, and I will probably not reply.
Anyone with specific research connections / questions / etc should of course feel free to get in touch at any time,
via email / Twitter DM / whatever.
Trans and gender-expansive or other queer people, also please reach out whenever, about specific things or just saying hi.
Consider using my personal email (the djsutherland.ml one) for privacy reasons;
Twitter DMs or Queer in AI's Slack are also good.
My current research interests include:
- Learning and testing on sets and distributions: two-sample tests, evaluating and training implicit generative models (e.g. GANs), density estimation, distribution regression.
- Learning “deep kernels,” and representation learning more broadly.
- Statistical learning theory in general.
I was previously
a Research Assistant Professor at TTIC,
did a postdoc
with Arthur Gretton
at the Gatsby Computational Neuroscience Unit, University College London,
and I did my Ph.D. at Carnegie Mellon University
with Jeff Schneider.
Publications and selected talks are listed below.
I previously published under a different name, and not all papers are corrected yet, though they should be. If you're citing my papers (please do!), please use the name "Danica J. Sutherland".
Note that Google Scholar's bib entries often deadname me (and most trans people), even on papers whose official versions have been updated; please correct them if you must use them (or use the more accurate bib entries from here).
Courses
- CPSC 340 Machine Learning and Data Mining: 2021-22 W1 (with Mike Gelbart)
- CPSC 532S Modern Statistical Learning Theory: 2021-22 W2
Publications
Below, ** denotes equal contribution,
and this colour one of my students.
Also available as a .bib file,
and most of these are on
Semantic Scholar.
If you must (but I'd rather you didn't),
here's Google Scholar.
Coauthor filters:
(show)
(hide)
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Michael Arbel (3)
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Wonho Bae (3)
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Mikołaj Bińkowski (2)
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Seth Flaxman (3)
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Roman Garnett (2)
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Arthur Gretton (7)
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Milad Jalali Asadabadi (2)
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Frederic Koehler (2)
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Ho Chung Leon Law (2)
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Feng Liu (2)
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Jie Lu (2)
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Yifei Ma (2)
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Mohamad Amin Mohamadi (2)
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Jyunhug Noh (2)
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Michelle Ntampaka (3)
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Junier B. Oliva (3)
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Barnabás Póczos (9)
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Jeff Schneider (11)
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Dino Sejdinovic (2)
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Nathan Srebro (4)
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Heiko Strathmann (3)
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Hy Trac (3)
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Liang Xiong (2)
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Wenkai Xu (2)
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Lijia Zhou (3)
Preprints
A Fast, Well-Founded Approximation to the Empirical Neural Tangent Kernel.
Mohamad Amin Mohamadi and Danica J. Sutherland.
Preprint
2022.
Making Look-Ahead Active Learning Strategies Feasible with Neural Tangent Kernels.
Mohamad Amin Mohamadi**,
Wonho Bae**, and
Danica J. Sutherland.
Preprint
2022.
Evaluating Graph Generative Models with Contrastively Learned Features.
Hamed Shirzad,
Kaveh Hassani, and
Danica J. Sutherland.
Preprint
2022.
Differentially Private Data Generation Needs Better Features.
Frederik Harder,
Milad Jalali Asadabadi,
Danica J. Sutherland, and
Mijung Park.
Preprint
2022.
Journal and Low-Acceptance-Rate Conference Papers
Object Discovery via Contrastive Learning for Weakly Supervised Object Detection.
Jinhwan Seo,
Wonho Bae,
Danica J. Sutherland,
Jyunhug Noh, and
Daijin Kim.
European Conference on Computer Vision
(ECCV)
2022.
One Weird Trick to Improve Your Semi-Weakly Supervised Semantic Segmentation Model.
Wonho Bae,
Jyunhug Noh,
Milad Jalali Asadabadi, and
Danica J. Sutherland.
International Joint Conference on Artificial Intelligence
(IJCAI)
2022.
Better Supervisory Signals by Observing Learning Paths.
Yi Ren,
Shangmin Guo, and
Danica J. Sutherland.
International Conference on Learning Representations
(ICLR)
2022.
Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds and Benign Overfitting.
Frederic Koehler**,
Lijia Zhou**,
Danica J. Sutherland, and
Nathan Srebro.
Neural Information Processing Systems
(NeurIPS)
2021.
Selected for oral presentation.
Self-Supervised Learning with Kernel Dependence Maximization.
Yazhe Li**,
Roman Pogodin**,
Danica J. Sutherland, and
Arthur Gretton.
Neural Information Processing Systems
(NeurIPS)
2021.
POT: Python Optimal Transport.
Rémi Flamary,
Nicolas Courty,
Alexandre Gramfort,
Mokhtar Z. Alaya,
Aurélie Boisbunon,
Stanislas Chambon,
Laetitia Chapel,
Adrien Corenflos,
Kilian Fatras,
Nemo Fournier,
Léo Gautheron,
Nathalie T.H. Gayraud,
Hicham Janati,
Alain Rakotomamonjy,
Ievgen Redko,
Antoine Rolet,
Antony Schutz,
Vivien Seguy,
Danica J. Sutherland,
Romain Tavenard,
Alexander Tong, and
Titouan Vayer.
Journal of Machine Learning Research
(JMLR)
2021.
Machine Learning Open Source Software Paper.
Does Invariant Risk Minimization Capture Invariance?
Pritish Kamath,
Akilesh Tangella,
Danica J. Sutherland, and
Nathan Srebro.
Artificial Intelligence and Statistics
(AISTATS)
2021.
Selected for oral presentation.
On the Error of Random Fourier Features.
Danica J. Sutherland and Jeff Schneider.
Uncertainty in Artificial Intelligence
(UAI)
2015.
Chapter 3 / Section 4.1 of my thesis supersedes this paper, fixing a few errors in constants and providing more results.
Active learning and search on low-rank matrices.
Danica J. Sutherland,
Barnabás Póczos, and
Jeff Schneider.
Knowledge Discovery and Data Mining
(KDD)
2013.
Selected for oral presentation.
Dissertations
Integrating Human Knowledge into a Relational Learning System.
Danica J. Sutherland.
Computer Science Department, Swarthmore College. B.A. thesis,
2011.
Technical Reports, Posters, etc.
Learning Privacy-Preserving Deep Kernels with Known Demographics.
Namrata Deka and Danica J. Sutherland.
Privacy-Preserving Artificial Intelligence
(PPAI-22), AAAI
2022.
How to Make Virtual Conferences Queer-Friendly: A Guide.
Organizers of QueerInAI,
A Pranav,
MaryLena Bleile,
Arjun Subramonian,
Luca Soldaini,
Danica J. Sutherland,
Sabine Weber, and
Pan Xu.
Workshop on Widening NLP
(WiNLP), EMNLP
2021.
Unbiased estimators for the variance of MMD estimators.
Danica J. Sutherland.
Technical report
2019.
The Role of Machine Learning in the Next Decade of Cosmology.
Michelle Ntampaka,
Camille Avestruz,
Steven Boada,
João Caldeira,
Jessi Cisewski-Kehe,
Rosanne Di Stefano,
Cora Dvorkin,
August E. Evrard,
Arya Farahi,
Doug Finkbeiner,
Shy Genel,
Alyssa Goodman,
Andy Goulding,
Shirley Ho,
Arthur Kosowsky,
Paul La Plante,
François Lanusse,
Michelle Lochner,
Rachel Mandelbaum,
Daisuke Nagai,
Jeffrey A. Newman,
Brian Nord,
J. E. G. Peek,
Austin Peel,
Barnabás Póczos,
Markus Michael Rau,
Aneta Siemiginowska,
Danica J. Sutherland,
Hy Trac, and
Benjamin Wandelt.
White paper
2019.
Fixing an error in Caponnetto and de Vito (2007).
Danica J. Sutherland.
Technical report
2017.
Understanding the 2016 US Presidential Election using ecological inference and distribution regression with census microdata.
Seth Flaxman,
Danica J. Sutherland,
Yu-Xiang Wang, and
Yee Whye Teh.
Technical report
2016.
List Mode Regression for Low Count Detection.
Jay Jin,
Kyle Miller,
Danica J. Sutherland,
Simon Labov,
Karl Nelson, and
Artur Dubrawski.
IEEE Nuclear Science Symposium
(IEEE NSS/MIC)
2016.
Grounding Conceptual Knowledge with Spatio-Temporal Multi-Dimensional Relational Framework Trees.
Matthew Bodenhamer,
Thomas Palmer,
Danica J. Sutherland, and
Andrew H. Fagg.
Technical report
2012.
Invited talks
Slides for conference and workshop talks directly for a paper are linked next to the paper above.
Better deep learning (sometimes) by learning kernel mean embeddings.
January 2022.
Lifting Inference with Kernel Embeddings
(LIKE22), University of Bern.
Related papers:
NeurIPS-21, NeurIPS-21.
Can Uniform Convergence Explain Interpolation Learning?
October 2020.
Penn State, Statistics colloquium.
Related papers:
NeurIPS-20.
Tutorial: Interpretable Comparison of Distributions and Models.
December 2019.
Neural Information Processing Systems
(NeurIPS).
Related papers:
ICML-20, ICLR-17.
With Arthur Gretton and Wittawat Jitkrittum.
Better GANs by Using Kernels.
October 2019.
University of Massachusetts Amherst, College of Information and Computer Sciences.
Related papers:
ICLR-18, NeurIPS-18.
Introduction to Generative Adversarial Networks.
June 2019.
Machine Learning Crash Course
(MLCC).
Better GANs by using the MMD.
June 2018.
Facebook AI Research New York.
Related papers:
ICLR-18, NeurIPS-18.
Better GANs by using the MMD.
June 2018.
Machine Learning reading group, Google New York.
Related papers:
ICLR-18, NeurIPS-18.
Better GANs by using the MMD.
June 2018.
Machine Learning reading group, Columbia University.
Related papers:
ICLR-18, NeurIPS-18.
No slides actually used at the talk because of a projector mishap,
but they would have been the same as
the Google talk.
Efficient and principled score estimation with kernel exponential families.
December 2017.
Computational Statistics and Machine Learning seminar, University College London.
Related papers:
AISTATS-18.
Evaluating and Training Implicit Generative Models with Two-Sample Tests.
August 2017.
Implicit Models, ICML.
Related papers:
ICLR-17.
Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy.
February 2017.
Computational Statistics and Machine Learning seminar, Oxford University.
Related papers:
ICLR-17.