1 citations · 1 across the 6 of their papers we have counts for
6 papers
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…
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 ,…
Cardinality-Aware Set Prediction and Top- Classification
Corinna Cortes, Anqi Mao, Christopher Mohri +2
We present a detailed study of cardinality-aware top- classification, a novel approach that aims to learn an accurate top- set predictor while maintaining a low cardinality.…
Top- Classification and Cardinality-Aware Prediction
Anqi Mao, Mehryar Mohri, Yutao Zhong
We present a detailed study of top- classification, the task of predicting the most probable classes for an input, extending beyond single-class prediction. We demonstrate t…
Regression with Multi-Expert Deferral
Anqi Mao, Mehryar Mohri, Yutao Zhong
Learning to defer with multiple experts is a framework where the learner can choose to defer the prediction to several experts. While this problem has received significant attentio…
-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.…