1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2023
Distributionally Robust Post-hoc Classifiers under Prior Shifts
Jiaheng Wei, Harikrishna Narasimhan, Ehsan Amid +3
The generalization ability of machine learning models degrades significantly when the test distribution shifts away from the training distribution. We investigate the problem of tr…
cs.LG2022
To Aggregate or Not? Learning with Separate Noisy Labels
Jiaheng Wei, Zhaowei Zhu, Tianyi Luo +3
The rawly collected training data often comes with separate noisy labels collected from multiple imperfect annotators (e.g., via crowdsourcing). A typical way of using these separa…
cs.LG2021★ 1 cited
Constrained Instance and Class Reweighting for Robust Learning under Label Noise
Abhishek Kumar, Ehsan Amid
Deep neural networks have shown impressive performance in supervised learning, enabled by their ability to fit well to the provided training data. However, their performance is lar…