Nonparametric kernel estimators for image classification. - Barnabás Póczos; Liang Xiong; Danica J. Sutherland; Jeff G. Schneider (CVPR, 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 (LISA, 2012)

Support Distribution Machines - Barnabás Póczos; Liang Xiong; Danica J. Sutherland; Jeff G. Schneider (CoRR, 2012)

Active learning and search on low-rank matrices. - Danica J. Sutherland; Barnabás Póczos; Jeff G. Schneider (KDD, 2013)

Active Pointillistic Pattern Search. - Yifei Ma; Danica J. Sutherland; Roman Garnett; Jeff G. Schneider (AISTATS, 2015)

On the Error of Random Fourier Features. - Danica J. Sutherland; Jeff G. Schneider (UAI, 2015)

Deep Mean Maps. - Junier B. Oliva; Danica J. Sutherland; Barnabás Póczos; Jeff G. Schneider (CoRR, 2015)

Linear-Time Learning on Distributions with Approximate Kernel Embeddings. - Danica J. Sutherland; Junier B. Oliva; Barnabás Póczos; Jeff G. Schneider (AAAI, 2016)

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 (ICLR (Poster), 2017)

Bayesian Distribution Regression. - Ho Chung Leon Law; Danica J. Sutherland; Dino Sejdinovic; Seth R. Flaxman (CoRR, 2017)

Fixing an error in Caponnetto and de Vito (2007). - Danica J. Sutherland (CoRR, 2017)

Efficient and principled score estimation. - Danica J. Sutherland; Heiko Strathmann; Michael Arbel; Arthur Gretton (CoRR, 2017)

Bayesian Approaches to Distribution Regression. - Ho Chung Leon Law; Danica J. Sutherland; Dino Sejdinovic; Seth R. Flaxman (AISTATS, 2018)

Efficient and principled score estimation with Nyström kernel exponential families. - Danica J. Sutherland; Heiko Strathmann; Michael Arbel; Arthur Gretton (AISTATS, 2018)

Demystifying MMD GANs. - Mikolaj Binkowski; Danica J. Sutherland; Michael Arbel; Arthur Gretton (ICLR (Poster), 2018)

On gradient regularizers for MMD GANs. - Michael Arbel; Danica J. Sutherland; Mikolaj Binkowski; Arthur Gretton (NeurIPS, 2018)

Learning deep kernels for exponential family densities. - Wenliang Li; Danica J. Sutherland; Heiko Strathmann; Arthur Gretton (ICML, 2019)

Unbiased estimators for the variance of MMD estimators. - Danica J. Sutherland (CoRR, 2019)

Learning Deep Kernels for Non-Parametric Two-Sample Tests. - Feng Liu 003; Wenkai Xu; Jie Lu 001; Guangquan Zhang 001; Arthur Gretton; Danica J. Sutherland (ICML, 2020)

On Uniform Convergence and Low-Norm Interpolation Learning. - Lijia Zhou; Danica J. Sutherland; Nati Srebro (NeurIPS, 2020)

Does Invariant Risk Minimization Capture Invariance? - Pritish Kamath; Akilesh Tangella; Danica J. Sutherland; Nathan Srebro (AISTATS, 2021)

Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds and Benign Overfitting. - Frederic Koehler; Lijia Zhou; Danica J. Sutherland; Nathan Srebro (NeurIPS, 2021)

Self-Supervised Learning with Kernel Dependence Maximization. - Yazhe Li; Roman Pogodin; Danica J. Sutherland; Arthur Gretton (NeurIPS, 2021)

Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data. - Feng Liu 003; Wenkai Xu; Jie Lu 001; Danica J. Sutherland (NeurIPS, 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)

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; et al. (J. Mach. Learn. Res., 2021)

Object Discovery via Contrastive Learning for Weakly Supervised Object Detection. - Jinhwan Seo; Wonho Bae; Danica J. Sutherland; Junhyug Noh; Daijin Kim 001 (ECCV (31), 2022)

Better Supervisory Signals by Observing Learning Paths. - Yi Ren; Shangmin Guo; Danica J. Sutherland (ICLR, 2022)

One Weird Trick to Improve Your Semi-Weakly Supervised Semantic Segmentation Model. - Wonho Bae; Junhyug Noh; Milad Jalali Asadabadi; Danica J. Sutherland (IJCAI, 2022)

Making Look-Ahead Active Learning Strategies Feasible with Neural Tangent Kernels. - Mohamad Amin Mohamadi; Wonho Bae; Danica J. Sutherland (NeurIPS, 2022)

Evaluating Graph Generative Models with Contrastively Learned Features. - Hamed Shirzad; Kaveh Hassani; Danica J. Sutherland (NeurIPS, 2022)

A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models. - Lijia Zhou; Frederic Koehler; Pragya Sur; Danica J. Sutherland; Nati Srebro (NeurIPS, 2022)

Differentially Private Data Generation Needs Better Features. - Frederik Harder; Milad Jalali Asadabadi; Danica J. Sutherland; Mijung Park (CoRR, 2022)

A Fast, Well-Founded Approximation to the Empirical Neural Tangent Kernel. - Mohamad Amin Mohamadi; Danica J. Sutherland (CoRR, 2022)

MMD-B-Fair: Learning Fair Representations with Statistical Testing. - Namrata Deka; Danica J. Sutherland (AISTATS, 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 009; et al. (FAccT, 2023)

Efficient Conditionally Invariant Representation Learning. - Roman Pogodin; Namrata Deka; Yazhe Li; Danica J. Sutherland; Victor Veitch; Arthur Gretton (ICLR, 2023)

How to prepare your task head for finetuning. - Yi Ren; Shangmin Guo; Wonho Bae; Danica J. Sutherland (ICLR, 2023)

A Fast, Well-Founded Approximation to the Empirical Neural Tangent Kernel. - Mohamad Amin Mohamadi; Wonho Bae; Danica J. Sutherland (ICML, 2023)

Exphormer: Sparse Transformers for Graphs. - Hamed Shirzad; Ameya Velingker; Balaji Venkatachalam; Danica J. Sutherland; Ali Kemal Sinop (ICML, 2023)

Improving Compositional Generalization using Iterated Learning and Simplicial Embeddings. - Yi Ren; Samuel Lavoie; Michael Galkin; Danica J. Sutherland; Aaron C. Courville (NeurIPS, 2023)

Differentially Private Neural Tangent Kernels for Privacy-Preserving Data Generation. - Yilin Yang; Kamil Adamczewski; Danica J. Sutherland; Xiaoxiao Li; Mijung Park (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 009; Jaidev Shriram; et al. (CoRR, 2023)

Pre-trained Perceptual Features Improve Differentially Private Image Generation. - Frederik Harder; Milad Jalali; Danica J. Sutherland; Mijung Park (Trans. Mach. Learn. Res., 2023)

Generalized Coverage for More Robust Low-Budget Active Learning. - Wonho Bae; Junhyug Noh; Danica J. Sutherland (ECCV (83), 2024)

Exploring Active Learning in Meta-learning: Enhancing Context Set Labeling. - Wonho Bae; Jing Wang 112; Danica J. Sutherland (ECCV (89), 2024)

Why Do You Grok? A Theoretical Analysis on Grokking Modular Addition. - Mohamad Amin Mohamadi; Zhiyuan Li; Lei Wu; Danica J. Sutherland (ICML, 2024)

Bias Amplification in Language Model Evolution: An Iterated Learning Perspective. - Yi Ren; Shangmin Guo; Linlu Qiu; Bailin Wang; Danica J. Sutherland (NeurIPS, 2024)

Even Sparser Graph Transformers. - Hamed Shirzad; Honghao Lin; Balaji Venkatachalam; Ameya Velingker; David P. Woodruff; Danica J. Sutherland (NeurIPS, 2024)

Practical Kernel Tests of Conditional Independence. - Roman Pogodin; Antonin Schrab; Yazhe Li; Danica J. Sutherland; Arthur Gretton (CoRR, 2024)

Language Model Evolution: An Iterated Learning Perspective. - Yi Ren; Shangmin Guo; Linlu Qiu; Bailin Wang; Danica J. Sutherland (CoRR, 2024)

Learning Deep Kernels for Non-Parametric Independence Testing. - Nathaniel Xu; Feng Liu 003; Danica J. Sutherland (CoRR, 2024)

Understanding Simplicity Bias towards Compositional Mappings via Learning Dynamics. - Yi Ren; Danica J. Sutherland (CoRR, 2024)

A Theory for Compressibility of Graph Transformers for Transductive Learning. - Hamed Shirzad; Honghao Lin; Ameya Velingker; Balaji Venkatachalam; David P. Woodruff; Danica J. Sutherland (CoRR, 2024)

Differentially Private Neural Tangent Kernels (DP-NTK) for Privacy-Preserving Data Generation. - Yi Yang 001; Kamil Adamczewski; Xiaoxiao Li; Danica J. Sutherland; Mijung Park (J. Artif. Intell. Res., 2024)

AdaFlood: Adaptive Flood Regularization. - Wonho Bae; Yi Ren; Mohamed Osama Ahmed; Frederick Tung; Danica J. Sutherland; Gabriel L. Oliveira (Trans. Mach. Learn. Res., 2024)

Uncertainty Herding: One Active Learning Method for All Label Budgets. - Wonho Bae; Danica J. Sutherland; Gabriel L. Oliveira (ICLR, 2025)

Learning Dynamics of LLM Finetuning. - Yi Ren; Danica J. Sutherland (ICLR, 2025)

On the Effect of Negative Gradient in Group Relative Deep Reinforcement Optimization. - Wenlong Deng; Yi Ren; Muchen Li; Danica J. Sutherland; Xiaoxiao Li; Christos Thrampoulidis (CoRR, 2025)

Efficient kernelized bandit algorithms via exploration distributions. - Bingshan Hu; Zheng He; Danica J. Sutherland (CoRR, 2025)