23 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…
Theoretical Foundations and Effective Algorithms for Policy-Aware Simulator Learning
Christoph Dann, Yishay Mansour, Mehryar Mohri
Model-based reinforcement learning (MBRL) agents typically learn world models by minimizing predictive loss. However, powerful RL optimizers inevitably exploit minor model inaccura…
Temper-Then-Tilt: Principled Unlearning for Generative Models through Tempering and Classifier Guidance
Jacob L. Block, Mehryar Mohri, Aryan Mokhtari +1
We study machine unlearning in large generative models by framing the task as density ratio estimation to a target distribution rather than supervised fine-tuning. While classifier…
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…