Danica J. Sutherlandshe
Assistant Professor, UBC Computer Science
Canada CIFAR AI Chair, Amii
UBC Machine Learning
AML-TN
MILD (ML theory)
CAIDA (AI)
PIHOT/Kantorovich Initiative (optimal transport)
Queer in AI
Name Change Policy Working Group
dsuth[a t]cs.ubc.ca
or
djs[a t]djsutherland.ml
CV
orcid
github
crossvalidated
twitter
mastodon
Prospective students:
Like most North American schools, we only accept applications through the departmental process,
deadline December 15th.
I am likely looking for one or two new students this year.
You might also be interested in applying to the ELLIS PhD program (deadline November 15th); if you want me to be your primary advisor, you must also apply to UBC.
Note that the split between master's and PhD programs is different in Canada from the US or Europe. I am unlikely to seriously consider PhD applicants who don't already have significant research experience in machine learning or statistics (ideally, something close to my areas of interest), but will certainly look at promising master's candidates without that.
Machine learning graduate programs are currently extremely competitive.
Make sure you apply to more than one program.
If relevant to you, the Queer in AI grad app aid program provides both advice and financial support for queer applicants; a bunch of advice is also collated here, among many other places.
Also see this list of fee waivers (including UBC's for applicants from the UN's list of 50 least developed countries).
There is no need to email me about admissions; due to the volume of emails, I will probably not reply.
Anyone with specific research connections / questions / etc should 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.
I was previously at
TTIC (non-tenure-track faculty, affiliated with Nati Srebro),
Gatsby (postdoc with Arthur Gretton),
and CMU (PhD with Jeff Schneider).
Publications and selected talks are listed below.
You may come across various old items
referring to me with a different first name.
Please only use the name Danica to cite or refer to me,
and check that your old .bib entries are correct,
e.g. by replacing them with the entries here.
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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Kamil Adamczewski (2)
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Michael Arbel (3)
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Wonho Bae (8)
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Mikołaj Bińkowski (2)
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Namrata Deka (4)
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Seth Flaxman (3)
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Roman Garnett (2)
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Arthur Gretton (9)
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Shangmin Guo (3)
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Milad Jalali Asadabadi (2)
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Frederic Koehler (3)
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Ho Chung Leon Law (2)
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Xiaoxiao Li (2)
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Yazhe Li (3)
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Zhiyuan Li (2)
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Honghao Lin (2)
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Feng Liu (3)
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Jie Lu (2)
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Yifei Ma (2)
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Mohamad Amin Mohamadi (4)
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Jyunhug Noh (3)
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Michelle Ntampaka (3)
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Junier B. Oliva (3)
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Mijung Park (3)
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Roman Pogodin (3)
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A Pranav (2)
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Barnabás Póczos (9)
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Organizers of QueerInAI (2)
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Yi Ren (7)
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Jeff Schneider (11)
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Dino Sejdinovic (2)
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Hamed Shirzad (6)
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Luca Soldaini (2)
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Nathan Srebro (5)
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Heiko Strathmann (3)
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Arjun Subramonian (2)
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Hy Trac (3)
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Ameya Velingker (4)
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Balaji Venkatachalam (4)
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David P. Woodruff (3)
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Lei Wu (2)
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Liang Xiong (2)
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Pan Xu (2)
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Wenkai Xu (2)
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Yilin Yang (2)
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Lijia Zhou (4)
Preprints
Learning Deep Kernels for Non-Parametric Independence Testing.
Nathaniel Xu,
Feng Liu, and
Danica J. Sutherland.
Preprint
2024.
Learning Dynamics of LLM Finetuning.
Yi Ren and Danica J. Sutherland.
Preprint
2024.
Journal and Low-Acceptance-Rate Conference Papers
Language Model Evolution: An Iterated Learning Perspective.
Yi Ren,
Shangmin Guo,
Linlu Qiu,
Bailin Wang, and
Danica J. Sutherland.
Neural Information Processing Systems
(NeurIPS)
2024.
Generalized Coverage for More Robust Low-Budget Active Learning.
Wonho Bae,
Jyunhug Noh, and
Danica J. Sutherland.
European Conference on Computer Vision
(ECCV)
2024.
Differentially Private Neural Tangent Kernels (DP-NTK) for Privacy-Preserving Data Generation.
Yilin Yang,
Kamil Adamczewski,
Xiaoxiao Li,
Danica J. Sutherland, and
Mijung Park.
Journal of Artifical Intelligence Research
(JAIR)
2024.
AdaFlood: Adaptive Flood Regularization.
Wonho Bae,
Yi Ren,
Mohamad Osama Ahmed,
Frederick Tung,
Danica J. Sutherland, and
Gabriel Oliveira.
Transactions on Machine Learning Research
(TMLR)
2024.
Improving Compositional Generalization using Iterated Learning and Simplicial Embeddings.
Yi Ren,
Samuel Lavoie,
Mikhail Galkin,
Danica J. Sutherland, and
Aaron Courville.
Neural Information Processing Systems
(NeurIPS)
2023.
Exphormer: Scaling Graph Transformers with Expander Graphs.
Hamed Shirzad**,
Ameya Velingker**,
Balaji Venkatachalam**,
Danica J. Sutherland, and
Ali Kemal Sinop.
International Conference on Machine Learning
(ICML)
2023.
A Fast, Well-Founded Approximation to the Empirical Neural Tangent Kernel.
Mohamad Amin Mohamadi,
Wonho Bae, and
Danica J. Sutherland.
International Conference on Machine Learning
(ICML)
2023.
Queer in AI: A Case Study in Community-Led Participatory AI.
Organizers of QueerInAI,
Analeia Ovalle,
Arjun Subramonian,
Ashwin Singh,
Claas Voelcker,
Danica J. Sutherland,
Davide Locatelli,
Eva Breznik,
Filip Klubička,
Hang Yuan,
Hetvi J,
Huan Zhang,
Jaidev Shriram,
Kruno Lehamn,
Luca Soldaini,
Maarten Sap,
Marc Peter Deisenroth,
Maria Leonor Pacheco,
Maria Ryskina,
Martin Mundt,
Melvin Selim Atay,
Milind Agarwal,
Nyx McLean,
Pan Xu,
A Pranav,
Raj Korpan,
Ruchira Ray,
Sarah Mathew,
Sarthak Arora,
ST John,
Tanvi Anand,
Vishakha Agrawal,
William Agnew,
Yanan Long,
Zijie J. Wang,
Zeerak Talat,
Avijit Ghosh,
Nathaniel Dennler,
Michael Noseworthy,
Sharvani Jha,
Emi Baylor,
Aditya Joshi,
Natalia Y. Bilenko,
Andrew McNamara,
Raphael Gontijo-Lopes,
Alex Markham,
Evyn Dǒng,
Jackie Kay,
Manu Saraswat,
Nikhil Vytla, and
Luke Stark.
ACM Conference on Fairness, Accountability, and Transparency
(FAccT)
2023.
Best Paper award.
How to prepare your task head for finetuning.
Yi Ren,
Shangmin Guo,
Wonho Bae, and
Danica J. Sutherland.
International Conference on Learning Representations
(ICLR)
2023.
Optimistic Rates: A Unifying Theory for Interpolation Learning and Regularization in Linear Regression.
Lijia Zhou**,
Frederic Koehler**,
Danica J. Sutherland, and
Nathan Srebro.
ACM/IMS Journal of Data Science
(JDS)
2023.
MMD-B-Fair: Learning Fair Representations with Statistical Testing.
Namrata Deka and Danica J. Sutherland.
Artificial Intelligence and Statistics
(AISTATS)
2023.
Pre-trained Perceptual Features Improve Differentially Private Image Generation.
Frederik Harder,
Milad Jalali Asadabadi,
Danica J. Sutherland, and
Mijung Park.
Transactions on Machine Learning Research
(TMLR)
2023.
Making Look-Ahead Active Learning Strategies Feasible with Neural Tangent Kernels.
Mohamad Amin Mohamadi**,
Wonho Bae**, and
Danica J. Sutherland.
Neural Information Processing Systems
(NeurIPS)
2022.
Evaluating Graph Generative Models with Contrastively Learned Features.
Hamed Shirzad,
Kaveh Hassani, and
Danica J. Sutherland.
Neural Information Processing Systems
(NeurIPS)
2022.
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.
Differentially Private Neural Tangent Kernels for Privacy-Preserving Data Generation.
Yilin Yang,
Kamil Adamczewski,
Danica J. Sutherland,
Xiaoxiao Li, and
Mijung Park.
Privacy-Preserving Artificial Intelligence
(PPAI-24), AAAI
2024.
Learning Privacy-Preserving Deep Kernels with Known Demographics.
Namrata Deka and Danica J. Sutherland.
Privacy-Preserving Artificial Intelligence
(PPAI-22), AAAI
2022.
Unbiased estimators for the variance of MMD estimators.
Danica J. Sutherland and Namrata Deka.
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.
Scaling Graph Transformers with Expander Graphs.
June 2024.
Simon Fraser University, AI seminar.
Related papers:
ICML-23.
Learning conditionally independent representations with kernel regularizers.
June 2023.
Lifting Inference with Kernel Embeddings
(LIKE23), University of Bern.
Related papers:
ICLR-23.
[Lecture] (Deep) Kernel Mean Embeddings for Representing and Learning on Distributions.
June 2023.
Lifting Inference with Kernel Embeddings
(LIKE23), University of Bern.
Learning conditionally independent representations with kernel regularizers.
June 2023.
Gatsby25.
Related papers:
ICLR-23.
Are these datasets different? Two-sample testing for data scientists.
April 2023.
Pacific Conference on Artificial Intelligence
(PCA).
Related papers:
AISTATS-23, NeurIPS-21, ICML-20, ICLR-17.
Post-Publication Name Change Policies, Why they Matter, and Whether they Work.
March 2023.
Robotics DEI Seminar, University of Michigan.
[Lecture] Modern Kernel Methods in Machine Learning.
October 2022.
Research School on Uncertainty in Scientific Computing, Corsica
(ETICS).
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.
[Lecture] Kernel Methods: From Basics to Modern Applications.
January 2021.
Data Science Summer School
(DS3), École Polytechnique, Paris.
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.
[Lecture] Learning with Positive Definite Kernels: Theory, Algorithms and Applications.
June 2019.
Data Science Summer School
(DS3), École Polytechnique, Paris.
With Bharath Sriperumbudur.
[Lecture] Introduction to Generative Adversarial Networks.
June 2019.
Machine Learning Crash Course
(MLCC), University of Genoa.
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.