3 papers
cs.LG2024
Multi-Label Learning with Stronger Consistency Guarantees
Anqi Mao, Mehryar Mohri, Yutao Zhong
We present a detailed study of surrogate losses and algorithms for multi-label learning, supported by -consistency bounds. We first show that, for the simplest form of multi-lab…
cs.LG2024
Realizable -Consistent and Bayes-Consistent Loss Functions for Learning to Defer
Anqi Mao, Mehryar Mohri, Yutao Zhong
We present a comprehensive study of surrogate loss functions for learning to defer. We introduce a broad family of surrogate losses, parameterized by a non-increasing function ,…
cs.LG2024
-Consistency Guarantees for Regression
Anqi Mao, Mehryar Mohri, Yutao Zhong
We present a detailed study of -consistency bounds for regression. We first present new theorems that generalize the tools previously given to establish -consistency bounds.…