3 papers
math.ST2026
Robust boundary detection and density estimation using doubly stochastic scaling of the Gaussian kernel
Dhruv Kohli, Jesse He, Chester Holtz +2
This paper addresses the problem of detecting boundary points and estimating the sampling density of a dataset derived from a compact manifold with boundary, potentially in the pre…
cs.LG2025
Revisiting Meta-Learning with Noisy Labels: Reweighting Dynamics and Theoretical Guarantees
Yiming Zhang, Chester Holtz, Gal Mishne +1
Learning with noisy labels remains challenging because over-parameterized networks memorize corrupted supervision. Meta-learning-based sample reweighting mitigates this by using a…
cs.LG2025
Robust Graph-Based Semi-Supervised Learning via -Conductances
Sawyer Jack Robertson, Chester Holtz, Zhengchao Wan +2
We study the problem of semi-supervised learning on graphs in the regime where data labels are scarce or possibly corrupted. We propose an approach called -conductance learning…