Yutao Zhong

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 $F$ -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 $O (1/ T)$ 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 $F_{β}$ -measure, AM measure, Jaccard similarity coefficient, and weighted accuracy offer mor…

Fundamental Novel Consistency Theory: $H$ -Consistency Bounds

Yutao Zhong

In machine learning, the loss functions optimized during training often differ from the target loss that defines task performance due to computational intractability or lack of dif…

Beyond Tsybakov: Model Margin Noise and $H$ -Consistency Bounds

Mehryar Mohri, Yutao Zhong

We introduce a new low-noise condition for classification, the Model Margin Noise (MM noise) assumption, and derive enhanced $H$ -consistency bounds under this condition.…

Budgeted Multiple-Expert Deferral

Giulia DeSalvo, Clara Mohri, Mehryar Mohri +1

Learning to defer uncertain predictions to costly experts offers a powerful strategy for improving the accuracy and efficiency of machine learning systems. However, standard traini…

Mastering Multiple-Expert Routing: Realizable $H$ -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 $H$ -consistency bounds. We first show that, for the simplest form of multi-lab…

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

Anqi Mao, Mehryar Mohri, Yutao Zhong

We present a comprehensive study of surrogate loss functions for learning to defer. We introduce a broad family of surrogate losses, parameterized by a non-increasing function $Ψ$ ,…

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

Corinna Cortes, Anqi Mao, Christopher Mohri +2

We present a detailed study of cardinality-aware top- $k$ classification, a novel approach that aims to learn an accurate top- $k$ set predictor while maintaining a low cardinality.…

Enhanced $H$ -Consistency Bounds

Anqi Mao, Mehryar Mohri, Yutao Zhong

Recent research has introduced a key notion of $H$ -consistency bounds for surrogate losses. These bounds offer finite-sample guarantees, quantifying the relationship between the ze…

A Universal Growth Rate for Learning with Smooth Surrogate Losses

Anqi Mao, Mehryar Mohri, Yutao Zhong

This paper presents a comprehensive analysis of the growth rate of $H$ -consistency bounds (and excess error bounds) for various surrogate losses used in classification. We prove a…

Top- $k$ Classification and Cardinality-Aware Prediction

Anqi Mao, Mehryar Mohri, Yutao Zhong

We present a detailed study of top- $k$ classification, the task of predicting the $k$ most probable classes for an input, extending beyond single-class prediction. We demonstrate t…

cs.LG2024★ 1 cited

Regression with Multi-Expert Deferral

Anqi Mao, Mehryar Mohri, Yutao Zhong

Learning to defer with multiple experts is a framework where the learner can choose to defer the prediction to several experts. While this problem has received significant attentio…

$H$ -Consistency Guarantees for Regression

Anqi Mao, Mehryar Mohri, Yutao Zhong

We present a detailed study of $H$ -consistency bounds for regression. We first present new theorems that generalize the tools previously given to establish $H$ -consistency bounds.…

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…