Danica J. Sutherland

According to our database, Danica J. Sutherland authored at least 47 papers between 2012 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Practical Kernel Tests of Conditional Independence.

Roman Pogodin, Antonin Schrab, Yazhe Li, Danica J. Sutherland, Arthur Gretton

CoRR, 2024

2023

Pre-trained Perceptual Features Improve Differentially Private Image Generation.

Frederik Harder, Milad Jalali, Danica J. Sutherland, Mijung Park

Trans. Mach. Learn. Res., 2023

AdaFlood: Adaptive Flood Regularization.

Wonho Bae, Yi Ren, Mohamed Osama Ahmed, Frederick Tung, Danica J. Sutherland, Gabriel L. Oliveira

CoRR, 2023

Exploring Active Learning in Meta-Learning: Enhancing Context Set Labeling.

Wonho Bae, Jing Wang, Danica J. Sutherland

CoRR, 2023

Queer In AI: A Case Study in Community-Led Participatory AI.

Anaelia Ovalle, Arjun Subramonian, Ashwin Singh, Claas Voelcker, Danica J. Sutherland, Davide Locatelli, Eva Breznik, Filip Klubicka, Hang Yuan, Hetvi Jethwani, Huan Zhang, Jaidev Shriram, Kruno Lehman, Luca Soldaini, Maarten Sap, Marc Peter Deisenroth, Maria Leonor Pacheco, Maria Ryskina, Martin Mundt, Milind Agarwal, Nyx McLean, Pan Xu, Pranav A, 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 Dong, Jackie Kay, Manu Saraswat, Nikhil Vytla, Luke Stark

CoRR, 2023

Differentially Private Neural Tangent Kernels for Privacy-Preserving Data Generation.

Yilin Yang, Kamil Adamczewski, Danica J. Sutherland, Xiaoxiao Li, Mijung Park

CoRR, 2023

Improving Compositional Generalization using Iterated Learning and Simplicial Embeddings.

Yi Ren, Samuel Lavoie, Michael Galkin, Danica J. Sutherland, Aaron C. Courville

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Exphormer: Sparse Transformers for Graphs.

Hamed Shirzad, Ameya Velingker, Balaji Venkatachalam, Danica J. Sutherland, Ali Kemal Sinop

Proceedings of the International Conference on Machine Learning, 2023

A Fast, Well-Founded Approximation to the Empirical Neural Tangent Kernel.

Mohamad Amin Mohamadi, Wonho Bae, Danica J. Sutherland

Proceedings of the International Conference on Machine Learning, 2023

How to prepare your task head for finetuning.

Yi Ren, Shangmin Guo, Wonho Bae, Danica J. Sutherland

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Efficient Conditionally Invariant Representation Learning.

Roman Pogodin, Namrata Deka, Yazhe Li, Danica J. Sutherland, Victor Veitch, Arthur Gretton

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Queer In AI: A Case Study in Community-Led Participatory AI.

Organizers Of QueerInAI, Anaelia Ovalle, Arjun Subramonian, Ashwin Singh, Claas Voelcker, Danica J. Sutherland, Davide Locatelli, Eva Breznik, Filip Klubicka, Hang Yuan, Hetvi Jethwani, Huan Zhang, Jaidev Shriram, Kruno Lehman, Luca Soldaini, Maarten Sap, Marc Peter Deisenroth, Maria Leonor Pacheco, Maria Ryskina, Martin Mundt, Milind Agarwal, Nyx McLean, Pan Xu, Pranav A, 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 Dong, Jackie Kay, Manu Saraswat, Nikhil Vytla, Luke Stark

Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 2023

MMD-B-Fair: Learning Fair Representations with Statistical Testing.

Namrata Deka, Danica J. Sutherland

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

A Fast, Well-Founded Approximation to the Empirical Neural Tangent Kernel.

Mohamad Amin Mohamadi, Danica J. Sutherland

CoRR, 2022

Differentially Private Data Generation Needs Better Features.

Frederik Harder, Milad Jalali Asadabadi, Danica J. Sutherland, Mijung Park

CoRR, 2022

A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models.

Lijia Zhou, Frederic Koehler, Pragya Sur, Danica J. Sutherland, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Evaluating Graph Generative Models with Contrastively Learned Features.

Hamed Shirzad, Kaveh Hassani, Danica J. Sutherland

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Making Look-Ahead Active Learning Strategies Feasible with Neural Tangent Kernels.

Mohamad Amin Mohamadi, Wonho Bae, Danica J. Sutherland

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

One Weird Trick to Improve Your Semi-Weakly Supervised Semantic Segmentation Model.

Wonho Bae, Junhyug Noh, Milad Jalali Asadabadi, Danica J. Sutherland

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

Better Supervisory Signals by Observing Learning Paths.

Yi Ren, Shangmin Guo, Danica J. Sutherland

Proceedings of the Tenth International Conference on Learning Representations, 2022

Object Discovery via Contrastive Learning for Weakly Supervised Object Detection.

Jinhwan Seo, Wonho Bae, Danica J. Sutherland, Junhyug Noh, Daijin Kim

Proceedings of the Computer Vision - ECCV 2022, 2022

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, Titouan Vayer

J. Mach. Learn. Res., 2021

Optimistic Rates: A Unifying Theory for Interpolation Learning and Regularization in Linear Regression.

Lijia Zhou, Frederic Koehler, Danica J. Sutherland, Nathan Srebro

CoRR, 2021

Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data.

Feng Liu, Wenkai Xu, Jie Lu, Danica J. Sutherland

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Self-Supervised Learning with Kernel Dependence Maximization.

Yazhe Li, Roman Pogodin, Danica J. Sutherland, Arthur Gretton

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds and Benign Overfitting.

Frederic Koehler, Lijia Zhou, Danica J. Sutherland, Nathan Srebro

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Does Invariant Risk Minimization Capture Invariance?

Pritish Kamath, Akilesh Tangella, Danica J. Sutherland, Nathan Srebro

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

On Uniform Convergence and Low-Norm Interpolation Learning.

Lijia Zhou, Danica J. Sutherland, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Learning Deep Kernels for Non-Parametric Two-Sample Tests.

Feng Liu, Wenkai Xu, Jie Lu, Guangquan Zhang, Arthur Gretton, Danica J. Sutherland

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Unbiased estimators for the variance of MMD estimators.

Danica J. Sutherland

CoRR, 2019

Learning deep kernels for exponential family densities.

Wenliang Li, Danica J. Sutherland, Heiko Strathmann, Arthur Gretton

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

On gradient regularizers for MMD GANs.

Michael Arbel, Danica J. Sutherland, Mikolaj Binkowski, Arthur Gretton

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Demystifying MMD GANs.

Mikolaj Binkowski, Danica J. Sutherland, Michael Arbel, Arthur Gretton

Proceedings of the 6th International Conference on Learning Representations, 2018

Efficient and principled score estimation with Nyström kernel exponential families.

Danica J. Sutherland, Heiko Strathmann, Michael Arbel, Arthur Gretton

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

Bayesian Approaches to Distribution Regression.

Ho Chung Leon Law, Danica J. Sutherland, Dino Sejdinovic, Seth R. Flaxman

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Efficient and principled score estimation.

Danica J. Sutherland, Heiko Strathmann, Michael Arbel, Arthur Gretton

CoRR, 2017

Fixing an error in Caponnetto and de Vito (2007).

Danica J. Sutherland

CoRR, 2017

Bayesian Distribution Regression.

Ho Chung Leon Law, Danica J. Sutherland, Dino Sejdinovic, Seth R. Flaxman

CoRR, 2017

Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy.

Danica J. Sutherland, Hsiao-Yu Tung, Heiko Strathmann, Soumyajit De, Aaditya Ramdas, Alexander J. Smola, Arthur Gretton

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

Linear-Time Learning on Distributions with Approximate Kernel Embeddings.

Danica J. Sutherland, Junier B. Oliva, Barnabás Póczos, Jeff G. Schneider

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015

Deep Mean Maps.

Junier B. Oliva, Danica J. Sutherland, Barnabás Póczos, Jeff G. Schneider

CoRR, 2015

On the Error of Random Fourier Features.

Danica J. Sutherland, Jeff G. Schneider

Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

Active Pointillistic Pattern Search.

Yifei Ma, Danica J. Sutherland, Roman Garnett, Jeff G. Schneider

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2013

Active learning and search on low-rank matrices.

Danica J. Sutherland, Barnabás Póczos, Jeff G. Schneider

Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013

2012

Support Distribution Machines

Barnabás Póczos, Liang Xiong, Danica J. Sutherland, Jeff G. Schneider

CoRR, 2012

Managing User Requests With the Grand Unified Task System (GUTS).

Andrew Stromme, Danica J. Sutherland, Nicholas Felt, Alex Burka, Benjamin Lipton, Rebecca Roelofs, Daniel-Elia Feist-Alexandrov, Steve Dini, Allen Welkie

Proceedings of the Strategies, 2012

Nonparametric kernel estimators for image classification.

Barnabás Póczos, Liang Xiong, Danica J. Sutherland, Jeff G. Schneider

Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012