Danica J. Sutherland

96 papers A* 25A 6Journal 58Unranked 7

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Learning Representations for Independence Testing. Trans. Mach. Learn. Res. Nathaniel Xu, Feng Liu, Danica J. Sutherland
2025	J	`jnl`	DUAL: Learning Diverse Kernels for Aggregated Two-sample and Independence Testing. CoRR Zhijian Zhou, Xunye Tian, Liuhua Peng, Chao Lei, Antonin Schrab, Danica J. Sutherland, Feng Liu
2025	J	`jnl`	Diffusion-Driven Two-Stage Active Learning for Low-Budget Semantic Segmentation. CoRR Jeongin Kim, Wonho Bae, YouLee Han, Giyeong Oh, Youngjae Yu, Danica J. Sutherland, Junhyug Noh
2025	J	`jnl`	Efficient kernelized bandit algorithms via exploration distributions. CoRR Bingshan Hu, Zheng He, Danica J. Sutherland
2025	A*	`conf`	Learning Dynamics of LLM Finetuning. ICLR Yi Ren, Danica J. Sutherland
2025	J	`jnl`	Maximum Mean Discrepancy with Unequal Sample Sizes via Generalized U-Statistics. CoRR Aaron Wei, Milad Jalali, Danica J. Sutherland
2025	J	`jnl`	On the Effect of Negative Gradient in Group Relative Deep Reinforcement Optimization. CoRR Wenlong Deng, Yi Ren, Muchen Li, Danica J. Sutherland, Xiaoxiao Li, Christos Thrampoulidis
2025	J	`jnl`	On the Hardness of Conditional Independence Testing In Practice. CoRR Zheng He, Roman Pogodin, Yazhe Li, Namrata Deka, Arthur Gretton, Danica J. Sutherland
2025	J	`jnl`	Token Hidden Reward: Steering Exploration-Exploitation in Group Relative Deep Reinforcement Learning. CoRR Wenlong Deng, Yi Ren, Yushu Li, Boying Gong, Danica J. Sutherland, Xiaoxiao Li, Christos Thrampoulidis
2025	A*	`conf`	Uncertainty Herding: One Active Learning Method for All Label Budgets. ICLR Wonho Bae, Danica J. Sutherland, Gabriel L. Oliveira
2024	J	`jnl`	A Theory for Compressibility of Graph Transformers for Transductive Learning. CoRR Hamed Shirzad, Honghao Lin, Ameya Velingker, Balaji Venkatachalam, David P. Woodruff, Danica J. Sutherland
2024	J	`jnl`	AdaFlood: Adaptive Flood Regularization. Trans. Mach. Learn. Res. Wonho Bae, Yi Ren, Mohamed Osama Ahmed, Frederick Tung, Danica J. Sutherland, Gabriel L. Oliveira
2024	A*	`conf`	Bias Amplification in Language Model Evolution: An Iterated Learning Perspective. NeurIPS Yi Ren, Shangmin Guo, Linlu Qiu, Bailin Wang, Danica J. Sutherland
2024	J	`jnl`	Differentially Private Neural Tangent Kernels (DP-NTK) for Privacy-Preserving Data Generation. J. Artif. Intell. Res. Yi Yang, Kamil Adamczewski, Xiaoxiao Li, Danica J. Sutherland, Mijung Park
2024	A*	`conf`	Even Sparser Graph Transformers. NeurIPS Hamed Shirzad, Honghao Lin, Balaji Venkatachalam, Ameya Velingker, David P. Woodruff, Danica J. Sutherland
2024	J	`jnl`	Even Sparser Graph Transformers. CoRR Hamed Shirzad, Honghao Lin, Balaji Venkatachalam, Ameya Velingker, David P. Woodruff, Danica J. Sutherland
2024	—	`conf`	Exploring Active Learning in Meta-learning: Enhancing Context Set Labeling. ECCV (89) Wonho Bae, Jing Wang, Danica J. Sutherland
2024	—	`conf`	Generalized Coverage for More Robust Low-Budget Active Learning. ECCV (83) Wonho Bae, Junhyug Noh, Danica J. Sutherland
2024	J	`jnl`	Generalized Coverage for More Robust Low-Budget Active Learning. CoRR Wonho Bae, Junhyug Noh, Danica J. Sutherland
2024	J	`jnl`	Language Model Evolution: An Iterated Learning Perspective. CoRR Yi Ren, Shangmin Guo, Linlu Qiu, Bailin Wang, Danica J. Sutherland
2024	J	`jnl`	Learning Deep Kernels for Non-Parametric Independence Testing. CoRR Nathaniel Xu, Feng Liu, Danica J. Sutherland
2024	J	`jnl`	Learning Dynamics of LLM Finetuning. CoRR Yi Ren, Danica J. Sutherland
2024	J	`jnl`	Practical Kernel Tests of Conditional Independence. CoRR Roman Pogodin, Antonin Schrab, Yazhe Li, Danica J. Sutherland, Arthur Gretton
2024	J	`jnl`	Uncertainty Herding: One Active Learning Method for All Label Budgets. CoRR Wonho Bae, Gabriel L. Oliveira, Danica J. Sutherland
2024	J	`jnl`	Understanding Simplicity Bias towards Compositional Mappings via Learning Dynamics. CoRR Yi Ren, Danica J. Sutherland
2024	J	`jnl`	Why Do You Grok? A Theoretical Analysis of Grokking Modular Addition. CoRR Mohamad Amin Mohamadi, Zhiyuan Li, Lei Wu, Danica J. Sutherland
2024	A*	`conf`	Why Do You Grok? A Theoretical Analysis on Grokking Modular Addition. ICML Mohamad Amin Mohamadi, Zhiyuan Li, Lei Wu, Danica J. Sutherland
2023	A*	`conf`	A Fast, Well-Founded Approximation to the Empirical Neural Tangent Kernel. ICML Mohamad Amin Mohamadi, Wonho Bae, Danica J. Sutherland
2023	J	`jnl`	AdaFlood: Adaptive Flood Regularization. CoRR Wonho Bae, Yi Ren, Mohamed Osama Ahmed, Frederick Tung, Danica J. Sutherland, Gabriel L. Oliveira
2023	J	`jnl`	Differentially Private Neural Tangent Kernels for Privacy-Preserving Data Generation. CoRR Yilin Yang, Kamil Adamczewski, Danica J. Sutherland, Xiaoxiao Li, Mijung Park
2023	A*	`conf`	Efficient Conditionally Invariant Representation Learning. ICLR Roman Pogodin, Namrata Deka, Yazhe Li, Danica J. Sutherland, Victor Veitch, Arthur Gretton
2023	A*	`conf`	Exphormer: Sparse Transformers for Graphs. ICML Hamed Shirzad, Ameya Velingker, Balaji Venkatachalam, Danica J. Sutherland, Ali Kemal Sinop
2023	J	`jnl`	Exphormer: Sparse Transformers for Graphs. CoRR Hamed Shirzad, Ameya Velingker, Balaji Venkatachalam, Danica J. Sutherland, Ali Kemal Sinop
2023	J	`jnl`	Exploring Active Learning in Meta-Learning: Enhancing Context Set Labeling. CoRR Wonho Bae, Jing Wang, Danica J. Sutherland
2023	A*	`conf`	How to prepare your task head for finetuning. ICLR Yi Ren, Shangmin Guo, Wonho Bae, Danica J. Sutherland
2023	J	`jnl`	How to prepare your task head for finetuning. CoRR Yi Ren, Shangmin Guo, Wonho Bae, Danica J. Sutherland
2023	J	`jnl`	Improving Compositional Generalization Using Iterated Learning and Simplicial Embeddings. CoRR Yi Ren, Samuel Lavoie, Mikhail Galkin, Danica J. Sutherland, Aaron C. Courville
2023	A*	`conf`	Improving Compositional Generalization using Iterated Learning and Simplicial Embeddings. NeurIPS Yi Ren, Samuel Lavoie, Michael Galkin, Danica J. Sutherland, Aaron C. Courville
2023	A	`conf`	MMD-B-Fair: Learning Fair Representations with Statistical Testing. AISTATS Namrata Deka, Danica J. Sutherland
2023	J	`jnl`	Pre-trained Perceptual Features Improve Differentially Private Image Generation. Trans. Mach. Learn. Res. Frederik Harder, Milad Jalali, Danica J. Sutherland, Mijung Park
2023	—	`conf`	Queer In AI: A Case Study in Community-Led Participatory AI. FAccT 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
2023	J	`jnl`	Queer In AI: A Case Study in Community-Led Participatory AI. CoRR 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
2022	J	`jnl`	A Fast, Well-Founded Approximation to the Empirical Neural Tangent Kernel. CoRR Mohamad Amin Mohamadi, Danica J. Sutherland
2022	A*	`conf`	A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models. NeurIPS Lijia Zhou, Frederic Koehler, Pragya Sur, Danica J. Sutherland, Nati Srebro
2022	J	`jnl`	A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models. CoRR Lijia Zhou, Frederic Koehler, Pragya Sur, Danica J. Sutherland, Nathan Srebro
2022	A*	`conf`	Better Supervisory Signals by Observing Learning Paths. ICLR Yi Ren, Shangmin Guo, Danica J. Sutherland
2022	J	`jnl`	Better Supervisory Signals by Observing Learning Paths. CoRR Yi Ren, Shangmin Guo, Danica J. Sutherland
2022	J	`jnl`	Differentially Private Data Generation Needs Better Features. CoRR Frederik Harder, Milad Jalali Asadabadi, Danica J. Sutherland, Mijung Park
2022	J	`jnl`	Efficient Conditionally Invariant Representation Learning. CoRR Roman Pogodin, Namrata Deka, Yazhe Li, Danica J. Sutherland, Victor Veitch, Arthur Gretton
2022	A*	`conf`	Evaluating Graph Generative Models with Contrastively Learned Features. NeurIPS Hamed Shirzad, Kaveh Hassani, Danica J. Sutherland
2022	J	`jnl`	Evaluating Graph Generative Models with Contrastively Learned Features. CoRR Hamed Shirzad, Kaveh Hassani, Danica J. Sutherland
2022	J	`jnl`	MMD-B-Fair: Learning Fair Representations with Statistical Testing. CoRR Namrata Deka, Danica J. Sutherland
2022	A*	`conf`	Making Look-Ahead Active Learning Strategies Feasible with Neural Tangent Kernels. NeurIPS Mohamad Amin Mohamadi, Wonho Bae, Danica J. Sutherland
2022	J	`jnl`	Making Look-Ahead Active Learning Strategies Feasible with Neural Tangent Kernels. CoRR Mohamad Amin Mohamadi, Wonho Bae, Danica J. Sutherland
2022	—	`conf`	Object Discovery via Contrastive Learning for Weakly Supervised Object Detection. ECCV (31) Jinhwan Seo, Wonho Bae, Danica J. Sutherland, Junhyug Noh, Daijin Kim
2022	J	`jnl`	Object Discovery via Contrastive Learning for Weakly Supervised Object Detection. CoRR Jinhwan Seo, Wonho Bae, Danica J. Sutherland, Junhyug Noh, Daijin Kim
2022	A*	`conf`	One Weird Trick to Improve Your Semi-Weakly Supervised Semantic Segmentation Model. IJCAI Wonho Bae, Junhyug Noh, Milad Jalali Asadabadi, Danica J. Sutherland
2022	J	`jnl`	One Weird Trick to Improve Your Semi-Weakly Supervised Semantic Segmentation Model. CoRR Wonho Bae, Junhyug Noh, Milad Jalali Asadabadi, Danica J. Sutherland
2021	A	`conf`	Does Invariant Risk Minimization Capture Invariance? AISTATS Pritish Kamath, Akilesh Tangella, Danica J. Sutherland, Nathan Srebro
2021	J	`jnl`	Does Invariant Risk Minimization Capture Invariance? CoRR Pritish Kamath, Akilesh Tangella, Danica J. Sutherland, Nathan Srebro
2021	A*	`conf`	Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data. NeurIPS Feng Liu, Wenkai Xu, Jie Lu, Danica J. Sutherland
2021	J	`jnl`	Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data. CoRR Feng Liu, Wenkai Xu, Jie Lu, Danica J. Sutherland
2021	J	`jnl`	Optimistic Rates: A Unifying Theory for Interpolation Learning and Regularization in Linear Regression. CoRR Lijia Zhou, Frederic Koehler, Danica J. Sutherland, Nathan Srebro
2021	J	`jnl`	POT: Python Optimal Transport. J. Mach. Learn. Res. 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
2021	A*	`conf`	Self-Supervised Learning with Kernel Dependence Maximization. NeurIPS Yazhe Li, Roman Pogodin, Danica J. Sutherland, Arthur Gretton
2021	J	`jnl`	Self-Supervised Learning with Kernel Dependence Maximization. CoRR Yazhe Li, Roman Pogodin, Danica J. Sutherland, Arthur Gretton
2021	A*	`conf`	Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds and Benign Overfitting. NeurIPS Frederic Koehler, Lijia Zhou, Danica J. Sutherland, Nathan Srebro
2021	J	`jnl`	Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds, and Benign Overfitting. CoRR Frederic Koehler, Lijia Zhou, Danica J. Sutherland, Nathan Srebro
2020	A*	`conf`	Learning Deep Kernels for Non-Parametric Two-Sample Tests. ICML Feng Liu, Wenkai Xu, Jie Lu, Guangquan Zhang, Arthur Gretton, Danica J. Sutherland
2020	J	`jnl`	Learning Deep Kernels for Non-Parametric Two-Sample Tests. CoRR Feng Liu, Wenkai Xu, Jie Lu, Guangquan Zhang, Arthur Gretton, Danica J. Sutherland
2020	A*	`conf`	On Uniform Convergence and Low-Norm Interpolation Learning. NeurIPS Lijia Zhou, Danica J. Sutherland, Nati Srebro
2020	J	`jnl`	On Uniform Convergence and Low-Norm Interpolation Learning. CoRR Lijia Zhou, Danica J. Sutherland, Nathan Srebro
2019	A*	`conf`	Learning deep kernels for exponential family densities. ICML Wenliang Li, Danica J. Sutherland, Heiko Strathmann, Arthur Gretton
2019	J	`jnl`	Unbiased estimators for the variance of MMD estimators. CoRR Danica J. Sutherland
2018	A	`conf`	Bayesian Approaches to Distribution Regression. AISTATS Ho Chung Leon Law, Danica J. Sutherland, Dino Sejdinovic, Seth R. Flaxman
2018	—	`conf`	Demystifying MMD GANs. ICLR (Poster) Mikolaj Binkowski, Danica J. Sutherland, Michael Arbel, Arthur Gretton
2018	J	`jnl`	Demystifying MMD GANs. CoRR Mikolaj Binkowski, Danica J. Sutherland, Michael Arbel, Arthur Gretton
2018	A	`conf`	Efficient and principled score estimation with Nyström kernel exponential families. AISTATS Danica J. Sutherland, Heiko Strathmann, Michael Arbel, Arthur Gretton
2018	J	`jnl`	Learning deep kernels for exponential family densities. CoRR Wenliang Li, Danica J. Sutherland, Heiko Strathmann, Arthur Gretton
2018	A*	`conf`	On gradient regularizers for MMD GANs. NeurIPS Michael Arbel, Danica J. Sutherland, Mikolaj Binkowski, Arthur Gretton
2018	J	`jnl`	On gradient regularizers for MMD GANs. CoRR Michael Arbel, Danica J. Sutherland, Mikolaj Binkowski, Arthur Gretton
2017	J	`jnl`	Bayesian Distribution Regression. CoRR Ho Chung Leon Law, Danica J. Sutherland, Dino Sejdinovic, Seth R. Flaxman
2017	J	`jnl`	Efficient and principled score estimation. CoRR Danica J. Sutherland, Heiko Strathmann, Michael Arbel, Arthur Gretton
2017	J	`jnl`	Fixing an error in Caponnetto and de Vito (2007). CoRR Danica J. Sutherland
2017	—	`conf`	Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy. ICLR (Poster) Danica J. Sutherland, Hsiao-Yu Tung, Heiko Strathmann, Soumyajit De, Aaditya Ramdas, Alexander J. Smola, Arthur Gretton
2016	J	`jnl`	Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy. CoRR Danica J. Sutherland, Hsiao-Yu Fish Tung, Heiko Strathmann, Soumyajit De, Aaditya Ramdas, Alexander J. Smola, Arthur Gretton
2016	A*	`conf`	Linear-Time Learning on Distributions with Approximate Kernel Embeddings. AAAI Danica J. Sutherland, Junier B. Oliva, Barnabás Póczos, Jeff G. Schneider
2015	A	`conf`	Active Pointillistic Pattern Search. AISTATS Yifei Ma, Danica J. Sutherland, Roman Garnett, Jeff G. Schneider
2015	J	`jnl`	Deep Mean Maps. CoRR Junier B. Oliva, Danica J. Sutherland, Barnabás Póczos, Jeff G. Schneider
2015	J	`jnl`	Linear-time Learning on Distributions with Approximate Kernel Embeddings. CoRR Danica J. Sutherland, Junier B. Oliva, Barnabás Póczos, Jeff G. Schneider
2015	A	`conf`	On the Error of Random Fourier Features. UAI Danica J. Sutherland, Jeff G. Schneider
2015	J	`jnl`	On the Error of Random Fourier Features. CoRR Danica J. Sutherland, Jeff G. Schneider
2013	A*	`conf`	Active learning and search on low-rank matrices. KDD Danica J. Sutherland, Barnabás Póczos, Jeff G. Schneider
2012	—	`conf`	Managing User Requests With the Grand Unified Task System (GUTS). LISA Andrew Stromme, Danica J. Sutherland, Nicholas Felt, Alex Burka, Benjamin Lipton, Rebecca Roelofs, Daniel-Elia Feist-Alexandrov, Steve Dini, Allen Welkie
2012	A*	`conf`	Nonparametric kernel estimators for image classification. CVPR Barnabás Póczos, Liang Xiong, Danica J. Sutherland, Jeff G. Schneider
2012	J	`jnl`	Support Distribution Machines CoRR Barnabás Póczos, Liang Xiong, Danica J. Sutherland, Jeff G. Schneider