5 papers
Beyond Binary: Continuous State Optimization with Graph-Structured Objectives
Corinna Cortes, Yishay Mansour, Mehryar Mohri
Large-scale learning systems often face the challenge of balancing multiple, potentially competing objectives, such as fairness, accuracy, and latency. While recent work has formal…
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…
A Theoretical Framework for Modular Learning of Robust Generative Models
Corinna Cortes, Mehryar Mohri, Yutao Zhong
Training large-scale generative models is resource-intensive and relies heavily on heuristic dataset weighting. We address two fundamental questions: Can we train Large Language Mo…
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…
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…