Danica J. Sutherland

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 70

Top Topics

Data Generation

Uniform Convergence

Conditional Independence

Top Venues

Publications

Yi Ren, Danica J. Sutherland
Learning Dynamics of LLM Finetuning. CoRR (2024)
Yi Ren, Shangmin Guo, Linlu Qiu, Bailin Wang, Danica J. Sutherland
Language Model Evolution: An Iterated Learning Perspective. CoRR (2024)
Roman Pogodin, Antonin Schrab, Yazhe Li, Danica J. Sutherland, Arthur Gretton
Practical Kernel Tests of Conditional Independence. CoRR (2024)
Wonho Bae, Junhyug Noh, Danica J. Sutherland
Generalized Coverage for More Robust Low-Budget Active Learning. CoRR (2024)
Mohamad Amin Mohamadi, Zhiyuan Li, Lei Wu, Danica J. Sutherland
Why Do You Grok? A Theoretical Analysis of Grokking Modular Addition. CoRR (2024)
Frederik Harder, Milad Jalali, Danica J. Sutherland, Mijung Park
Pre-trained Perceptual Features Improve Differentially Private Image Generation. Trans. Mach. Learn. Res. 2023 (2023)
Wonho Bae, Jing Wang, Danica J. Sutherland
Exploring Active Learning in Meta-Learning: Enhancing Context Set Labeling. CoRR (2023)
Yilin Yang, Kamil Adamczewski, Danica J. Sutherland, Xiaoxiao Li, Mijung Park
Differentially Private Neural Tangent Kernels for Privacy-Preserving Data Generation. CoRR (2023)
Yi Ren, Samuel Lavoie, Mikhail Galkin, Danica J. Sutherland, Aaron C. Courville
Improving Compositional Generalization Using Iterated Learning and Simplicial Embeddings. CoRR (2023)
Yi Ren, Samuel Lavoie, Michael Galkin, Danica J. Sutherland, Aaron C. Courville
Improving Compositional Generalization using Iterated Learning and Simplicial Embeddings. NeurIPS (2023)
Hamed Shirzad, Ameya Velingker, Balaji Venkatachalam, Danica J. Sutherland, Ali Kemal Sinop
Exphormer: Sparse Transformers for Graphs. ICML (2023)
Wonho Bae, Yi Ren, Mohamed Osama Ahmed, Frederick Tung, Danica J. Sutherland, Gabriel L. Oliveira
AdaFlood: Adaptive Flood Regularization. CoRR (2023)
Namrata Deka, Danica J. Sutherland
MMD-B-Fair: Learning Fair Representations with Statistical Testing. AISTATS (2023)
Hamed Shirzad, Ameya Velingker, Balaji Venkatachalam, Danica J. Sutherland, Ali Kemal Sinop
Exphormer: Sparse Transformers for Graphs. CoRR (2023)
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, 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 Dong, Jackie Kay, Manu Saraswat, Nikhil Vytla, Luke Stark
Queer In AI: A Case Study in Community-Led Participatory AI. FAccT (2023)
Mohamad Amin Mohamadi, Wonho Bae, Danica J. Sutherland
A Fast, Well-Founded Approximation to the Empirical Neural Tangent Kernel. ICML (2023)
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, 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 Dong, Jackie Kay, Manu Saraswat, Nikhil Vytla, Luke Stark
Queer In AI: A Case Study in Community-Led Participatory AI. CoRR (2023)
Yi Ren, Shangmin Guo, Wonho Bae, Danica J. Sutherland
How to prepare your task head for finetuning. CoRR (2023)
Yi Ren, Shangmin Guo, Wonho Bae, Danica J. Sutherland
How to prepare your task head for finetuning. ICLR (2023)
Roman Pogodin, Namrata Deka, Yazhe Li, Danica J. Sutherland, Victor Veitch, Arthur Gretton
Efficient Conditionally Invariant Representation Learning. ICLR (2023)
Hamed Shirzad, Kaveh Hassani, Danica J. Sutherland
Evaluating Graph Generative Models with Contrastively Learned Features. NeurIPS (2022)
Roman Pogodin, Namrata Deka, Yazhe Li, Danica J. Sutherland, Victor Veitch, Arthur Gretton
Efficient Conditionally Invariant Representation Learning. CoRR (2022)
Jinhwan Seo, Wonho Bae, Danica J. Sutherland, Junhyug Noh, Daijin Kim
Object Discovery via Contrastive Learning for Weakly Supervised Object Detection. CoRR (2022)
Jinhwan Seo, Wonho Bae, Danica J. Sutherland, Junhyug Noh, Daijin Kim
Object Discovery via Contrastive Learning for Weakly Supervised Object Detection. ECCV (31) (2022)
Lijia Zhou, Frederic Koehler, Pragya Sur, Danica J. Sutherland, Nati Srebro
A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models. NeurIPS (2022)
Mohamad Amin Mohamadi, Wonho Bae, Danica J. Sutherland
Making Look-Ahead Active Learning Strategies Feasible with Neural Tangent Kernels. NeurIPS (2022)
Mohamad Amin Mohamadi, Danica J. Sutherland
A Fast, Well-Founded Approximation to the Empirical Neural Tangent Kernel. CoRR (2022)
Yi Ren, Shangmin Guo, Danica J. Sutherland
Better Supervisory Signals by Observing Learning Paths. CoRR (2022)
Wonho Bae, Junhyug Noh, Milad Jalali Asadabadi, Danica J. Sutherland
One Weird Trick to Improve Your Semi-Weakly Supervised Semantic Segmentation Model. IJCAI (2022)
Mohamad Amin Mohamadi, Wonho Bae, Danica J. Sutherland
Making Look-Ahead Active Learning Strategies Feasible with Neural Tangent Kernels. CoRR (2022)
Frederik Harder, Milad Jalali Asadabadi, Danica J. Sutherland, Mijung Park
Differentially Private Data Generation Needs Better Features. CoRR (2022)
Hamed Shirzad, Kaveh Hassani, Danica J. Sutherland
Evaluating Graph Generative Models with Contrastively Learned Features. CoRR (2022)
Yi Ren, Shangmin Guo, Danica J. Sutherland
Better Supervisory Signals by Observing Learning Paths. ICLR (2022)
Namrata Deka, Danica J. Sutherland
MMD-B-Fair: Learning Fair Representations with Statistical Testing. CoRR (2022)
Wonho Bae, Junhyug Noh, Milad Jalali Asadabadi, Danica J. Sutherland
One Weird Trick to Improve Your Semi-Weakly Supervised Semantic Segmentation Model. CoRR (2022)
Lijia Zhou, Frederic Koehler, Pragya Sur, Danica J. Sutherland, Nathan Srebro
A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models. CoRR (2022)
Feng Liu, Wenkai Xu, Jie Lu, Danica J. Sutherland
Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data. NeurIPS (2021)
Pritish Kamath, Akilesh Tangella, Danica J. Sutherland, Nathan Srebro
Does Invariant Risk Minimization Capture Invariance? AISTATS (2021)
Frederic Koehler, Lijia Zhou, Danica J. Sutherland, Nathan Srebro
Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds, and Benign Overfitting. CoRR (2021)
Yazhe Li, Roman Pogodin, Danica J. Sutherland, Arthur Gretton
Self-Supervised Learning with Kernel Dependence Maximization. NeurIPS (2021)
Yazhe Li, Roman Pogodin, Danica J. Sutherland, Arthur Gretton
Self-Supervised Learning with Kernel Dependence Maximization. CoRR (2021)
Pritish Kamath, Akilesh Tangella, Danica J. Sutherland, Nathan Srebro
Does Invariant Risk Minimization Capture Invariance? CoRR (2021)
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
POT: Python Optimal Transport. J. Mach. Learn. Res. 22 (2021)
Frederic Koehler, Lijia Zhou, Danica J. Sutherland, Nathan Srebro
Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds and Benign Overfitting. NeurIPS (2021)
Feng Liu, Wenkai Xu, Jie Lu, Danica J. Sutherland
Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data. CoRR (2021)
Lijia Zhou, Frederic Koehler, Danica J. Sutherland, Nathan Srebro
Optimistic Rates: A Unifying Theory for Interpolation Learning and Regularization in Linear Regression. CoRR (2021)
Feng Liu, Wenkai Xu, Jie Lu, Guangquan Zhang, Arthur Gretton, Danica J. Sutherland
Learning Deep Kernels for Non-Parametric Two-Sample Tests. ICML (2020)
Feng Liu, Wenkai Xu, Jie Lu, Guangquan Zhang, Arthur Gretton, Danica J. Sutherland
Learning Deep Kernels for Non-Parametric Two-Sample Tests. CoRR (2020)
Lijia Zhou, Danica J. Sutherland, Nathan Srebro
On Uniform Convergence and Low-Norm Interpolation Learning. CoRR (2020)
Lijia Zhou, Danica J. Sutherland, Nati Srebro
On Uniform Convergence and Low-Norm Interpolation Learning. NeurIPS (2020)
Wenliang Li, Danica J. Sutherland, Heiko Strathmann, Arthur Gretton
Learning deep kernels for exponential family densities. ICML (2019)
Danica J. Sutherland
Unbiased estimators for the variance of MMD estimators. CoRR (2019)
Mikolaj Binkowski, Danica J. Sutherland, Michael Arbel, Arthur Gretton
Demystifying MMD GANs. ICLR (Poster) (2018)
Danica J. Sutherland, Heiko Strathmann, Michael Arbel, Arthur Gretton
Efficient and principled score estimation with Nyström kernel exponential families. AISTATS (2018)
Mikolaj Binkowski, Danica J. Sutherland, Michael Arbel, Arthur Gretton
Demystifying MMD GANs. CoRR (2018)
Michael Arbel, Danica J. Sutherland, Mikolaj Binkowski, Arthur Gretton
On gradient regularizers for MMD GANs. NeurIPS (2018)
Michael Arbel, Danica J. Sutherland, Mikolaj Binkowski, Arthur Gretton
On gradient regularizers for MMD GANs. CoRR (2018)
Wenliang Li, Danica J. Sutherland, Heiko Strathmann, Arthur Gretton
Learning deep kernels for exponential family densities. CoRR (2018)
Ho Chung Leon Law, Danica J. Sutherland, Dino Sejdinovic, Seth R. Flaxman
Bayesian Approaches to Distribution Regression. AISTATS (2018)
Ho Chung Leon Law, Danica J. Sutherland, Dino Sejdinovic, Seth R. Flaxman
Bayesian Distribution Regression. CoRR (2017)
Danica J. Sutherland, Heiko Strathmann, Michael Arbel, Arthur Gretton
Efficient and principled score estimation. CoRR (2017)
Danica J. Sutherland
Fixing an error in Caponnetto and de Vito (2007). CoRR (2017)
Danica J. Sutherland, Hsiao-Yu Tung, Heiko Strathmann, Soumyajit De, Aaditya Ramdas, Alexander J. Smola, Arthur Gretton
Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy. ICLR (Poster) (2017)
Danica J. Sutherland, Junier B. Oliva, Barnabás Póczos, Jeff G. Schneider
Linear-Time Learning on Distributions with Approximate Kernel Embeddings. AAAI (2016)
Danica J. Sutherland, Hsiao-Yu Fish Tung, Heiko Strathmann, Soumyajit De, Aaditya Ramdas, Alexander J. Smola, Arthur Gretton
Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy. CoRR (2016)
Danica J. Sutherland, Junier B. Oliva, Barnabás Póczos, Jeff G. Schneider
Linear-time Learning on Distributions with Approximate Kernel Embeddings. CoRR (2015)
Danica J. Sutherland, Jeff G. Schneider
On the Error of Random Fourier Features. UAI (2015)
Danica J. Sutherland, Jeff G. Schneider
On the Error of Random Fourier Features. CoRR (2015)
Yifei Ma, Danica J. Sutherland, Roman Garnett, Jeff G. Schneider
Active Pointillistic Pattern Search. AISTATS (2015)
Junier B. Oliva, Danica J. Sutherland, Barnabás Póczos, Jeff G. Schneider
Deep Mean Maps. CoRR (2015)
Danica J. Sutherland, Barnabás Póczos, Jeff G. Schneider
Active learning and search on low-rank matrices. KDD (2013)
Andrew Stromme, Danica J. Sutherland, Nicholas Felt, Alex Burka, Benjamin Lipton, Rebecca Roelofs, Daniel-Elia Feist-Alexandrov, Steve Dini, Allen Welkie
Managing User Requests With the Grand Unified Task System (GUTS). LISA (2012)
Barnabás Póczos, Liang Xiong, Danica J. Sutherland, Jeff G. Schneider
Support Distribution Machines CoRR (2012)
Barnabás Póczos, Liang Xiong, Danica J. Sutherland, Jeff G. Schneider
Nonparametric kernel estimators for image classification. CVPR (2012)