218 citations · 219 across the 20 of their papers we have counts for
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Optimized Deferral for Imbalanced Settings
Corinna Cortes, Anqi Mao, Mehryar Mohri +1
Learning algorithms can be significantly improved by routing complex or uncertain inputs to specialized experts, balancing accuracy with computational cost. This approach, known as…
Principled Algorithms for Optimizing Generalized Metrics in Binary Classification
Anqi Mao, Mehryar Mohri, Yutao Zhong
In applications with significant class imbalance or asymmetric costs, metrics such as the -measure, AM measure, Jaccard similarity coefficient, and weighted accuracy offer mor…
Theory and Algorithms for Learning with Multi-Class Abstention and Multi-Expert Deferral
Anqi Mao
Large language models (LLMs) have achieved remarkable performance but face critical challenges: hallucinations and high inference costs. Leveraging multiple experts offers a soluti…
Mastering Multiple-Expert Routing: Realizable -Consistency and Strong Guarantees for Learning to Defer
Anqi Mao, Mehryar Mohri, Yutao Zhong
The problem of learning to defer with multiple experts consists of optimally assigning input instances to experts, balancing the trade-off between their accuracy and computational…
Balancing the Scales: A Theoretical and Algorithmic Framework for Learning from Imbalanced Data
Corinna Cortes, Anqi Mao, Mehryar Mohri +1
Class imbalance remains a major challenge in machine learning, especially in multi-class problems with long-tailed distributions. Existing methods, such as data resampling, cost-se…
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…