12 papers
Principled Algorithms for Optimizing Generalized Metrics in Multi-Label Learning
Mehryar Mohri, Yutao Zhong
Many real-world classification tasks require predicting multiple labels per instance, necessitating the optimization of complex evaluation metrics such as the -measure and Jacca…
Generalized Distributional Alignment Games for Unbiased Answer-Level Fine-Tuning
Mehryar Mohri, Jon Schneider, Yutao Zhong
The Distributional Alignment Game framework provides a powerful variational perspective on Answer-Level Fine-Tuning (ALFT). However, standard algorithms for these games rely on est…
Linear-Core Surrogates: Smooth Loss Functions with Linear Rates for Classification and Structured Prediction
Mehryar Mohri, Yutao Zhong
A fundamental dichotomy in the theory of classification sets smoothness against statistical efficiency: smooth surrogate losses such as the logistic loss enable fast optim…
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…
Improved Balanced Classification with Theoretically Grounded Loss Functions
Corinna Cortes, Mehryar Mohri, Yutao Zhong
The balanced loss is a widely adopted objective for multi-class classification under class imbalance. By assigning equal importance to all classes, regardless of their frequency, i…
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 mo…