most citedRevisiting Meta-Learning with Noisy Labels: Reweighting Dynamics and Theoretical Guarantees

1 citations · 1 across the 2 of their papers we have counts for

collaborators
Showing cs.LGShow all

2 papers · 1 filter

cs.LG2025★ 1 cited

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

OT Score: An OT based Confidence Score for Prototype-Assisted Source Free Unsupervised Domain Adaptation

Yiming Zhang, Sitong Liu, Alex Cloninger

We address the computational and theoretical limitations of current distributional alignment methods for source-free unsupervised domain adaptation (SFUDA) using source class-mean…