218 citations · 265 across the 23 of their papers we have counts for
6 papers · 1 filter
Principled Approaches for Learning to Defer with Multiple Experts
Anqi Mao, Mehryar Mohri, Yutao Zhong
We present a study of surrogate losses and algorithms for the general problem of learning to defer with multiple experts. We first introduce a new family of surrogate losses specif…
Predictor-Rejector Multi-Class Abstention: Theoretical Analysis and Algorithms
Anqi Mao, Mehryar Mohri, Yutao Zhong
We study the key framework of learning with abstention in the multi-class classification setting. In this setting, the learner can choose to abstain from making a prediction with s…
Theoretically Grounded Loss Functions and Algorithms for Score-Based Multi-Class Abstention
Anqi Mao, Mehryar Mohri, Yutao Zhong
Learning with abstention is a key scenario where the learner can abstain from making a prediction at some cost. In this paper, we analyze the score-based formulation of learning wi…
Ranking with Abstention
Anqi Mao, Mehryar Mohri, Yutao Zhong
We introduce a novel framework of ranking with abstention, where the learner can abstain from making prediction at some limited cost . We present a extensive theoretical analysi…
Differentially Private Domain Adaptation with Theoretical Guarantees
Raef Bassily, Corinna Cortes, Anqi Mao +1
In many applications, the labeled data at the learner's disposal is subject to privacy constraints and is relatively limited. To derive a more accurate predictor for the target dom…
Cross-Entropy Loss Functions: Theoretical Analysis and Applications
Anqi Mao, Mehryar Mohri, Yutao Zhong
Cross-entropy is a widely used loss function in applications. It coincides with the logistic loss applied to the outputs of a neural network, when the softmax is used. But, what gu…