Yutao Zhong

Principled Algorithms for Optimizing Generalized Metrics in Binary Classification

Improved Balanced Classification with Theoretically Grounded Loss Functions

Balancing the Scales: A Theoretical and Algorithmic Framework for Learning from Imbalanced Data

Mastering Multiple-Expert Routing: Realizable $H$-Consistency and Strong Guarantees for Learning to Defer

A Universal Growth Rate for Learning with Smooth Surrogate Losses

Realizable $H$-Consistent and Bayes-Consistent Loss Functions for Learning to Defer

Cardinality-Aware Set Prediction and Top-$k$ Classification

Multi-Label Learning with Stronger Consistency Guarantees

Learning to Reject with a Fixed Predictor: Application to Decontextualization