Sutherland, Danica J.

47 publications

TMLR 2026 Learning Representations for Independence Testing Nathaniel Xu, Feng Liu, Danica J. Sutherland

ICLR 2026 Token Hidden Reward: Steering Exploration-Exploitation in Group Relative Deep Reinforcement Learning Wenlong Deng, Yi Ren, Yushu Li, Boying Gong, Danica J. Sutherland, Xiaoxiao Li, Christos Thrampoulidis

NeurIPS 2025 DUAL: Learning Diverse Kernels for Aggregated Two-Sample and Independence Testing Zhijian Zhou, Xunye Tian, Liuhua Peng, Chao Lei, Antonin Schrab, Danica J. Sutherland, Feng Liu

NeurIPS 2025 Diffusion-Driven Two-Stage Active Learning for Low-Budget Semantic Segmentation Jeongin Kim, Wonho Bae, YouLee Han, Giyeong Oh, Youngjae Yu, Danica J. Sutherland, Junhyug Noh

ICLR 2025 Learning Dynamics of LLM Finetuning Yi Ren, Danica J. Sutherland

NeurIPS 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

NeurIPS 2025 On the Hardness of Conditional Independence Testing in Practice Zheng He, Roman Pogodin, Yazhe Li, Namrata Deka, Arthur Gretton, Danica J. Sutherland

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

NeurIPSW 2024 A Theory for Compressibility of Graph Transformers for Transductive Learning Hamed Shirzad, Honghao Lin, Ameya Velingker, Balaji Venkatachalam, David Woodruff, Danica J. Sutherland

TMLR 2024 AdaFlood: Adaptive Flood Regularization Wonho Bae, Yi Ren, Mohamed Osama Ahmed, Frederick Tung, Danica J. Sutherland, Gabriel L. Oliveira

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

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

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

ECCV 2024 Exploring Active Learning in Meta-Learning: Enhancing Context Set Labeling Wonho Bae, Jing Wang, Danica J. Sutherland

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

NeurIPSW 2024 Normalization Matters for Optimization Performance on Graph Neural Networks Alan Milligan, Frederik Kunstner, Hamed Shirzad, Mark Schmidt, Danica J. Sutherland

NeurIPSW 2024 Understanding Simplicity Bias Towards Compositional Mappings via Learning Dynamics Yi Ren, Danica J. Sutherland

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

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

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

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

ICLR 2023 How to Prepare Your Task Head for Finetuning Yi Ren, Shangmin Guo, Wonho Bae, Danica J. Sutherland

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

AISTATS 2023 MMD-B-Fair: Learning Fair Representations with Statistical Testing Namrata Deka, Danica J. Sutherland

TMLR 2023 Pre-Trained Perceptual Features Improve Differentially Private Image Generation Frederik Harder, Milad Jalali, Danica J. Sutherland, Mijung Park

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

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

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

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

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

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

NeurIPS 2021 Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data Feng Liu, Wenkai Xu, Jie Lu, Danica J. Sutherland

MLOSS 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

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

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

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

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

ICML 2019 Learning Deep Kernels for Exponential Family Densities Li Wenliang, Danica J. Sutherland, Heiko Strathmann, Arthur Gretton

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

ICLR 2018 Demystifying MMD GANs Mikołaj Bińkowski, Danica J. Sutherland, Michael Arbel, Arthur Gretton

AISTATS 2018 Efficient and Principled Score Estimation with Nyström Kernel Exponential Families Danica J. Sutherland, Heiko Strathmann, Michael Arbel, Arthur Gretton

NeurIPS 2018 On Gradient Regularizers for MMD GANs Michael Arbel, Danica J. Sutherland, Mikołaj Bińkowski, Arthur Gretton

ICLR 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

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

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

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

CVPR 2012 Nonparametric Kernel Estimators for Image Classification Barnabás Póczos, Liang Xiong, Danica J. Sutherland, Jeff G. Schneider