7 papers
Learning Representations for Independence Testing
Nathaniel Xu, Feng Liu, Danica J. Sutherland
Many tools exist to detect dependence between random variables, a core question across a wide range of machine learning, statistical, and scientific endeavors. Although several sta…
Diffusion-Driven Two-Stage Active Learning for Low-Budget Semantic Segmentation
Jeongin Kim, Wonho Bae, YouLee Han +4
Semantic segmentation demands dense pixel-level annotations, which can be prohibitively expensive - especially under extremely constrained labeling budgets. In this paper, we addre…
DUAL: Learning Diverse Kernels for Aggregated Two-sample and Independence Testing
Zhijian Zhou, Xunye Tian, Liuhua Peng +4
To adapt kernel two-sample and independence testing to complex structured data, aggregation of multiple kernels is frequently employed to boost testing power compared to single-ker…
Practical Kernel Tests of Conditional Independence
Roman Pogodin, Antonin Schrab, Yazhe Li +2
We describe a data-efficient, kernel-based approach to statistical testing of conditional independence. A major challenge of conditional independence testing is to obtain the corre…
Learning Dynamics of LLM Finetuning
Yi Ren, Danica J. Sutherland
Learning dynamics, which describes how the learning of specific training examples influences the model's predictions on other examples, gives us a powerful tool for understanding t…
Uncertainty Herding: One Active Learning Method for All Label Budgets
Wonho Bae, Gabriel L. Oliveira, Danica J. Sutherland
Most active learning research has focused on methods which perform well when many labels are available, but can be dramatically worse than random selection when label budgets are s…