2 citations · 3 across the 15 of their papers we have counts for
15 papers · 1 filter
PAC Learning with Query Feedback
Chase Hutton, Adam Melrod, Han Shao
We study realizable multiclass PAC learning with query feedback, where each training example provides a single binary response indicating whether its true label belongs to a chosen…
How Many Repeated Pairwise Comparisons Are Needed for Ranking under Heterogeneity?
Shashaank Aiyer, Han Shao
We study ranking models by population-average utility from pairwise comparisons when preferences vary across users and tasks. Prior work shows that a single comparison per user can…
Scale-Sensitive Shattering: Learnability and Evaluability at Optimal Scale
Shashaank Aiyer, Yishay Mansour, Shay Moran +2
We study the optimal scale at which real-valued function classes exhibit uniform convergence and learnability. Our main result establishes a scale-sensitive generalization of the f…
Online Set Learning from Precision and Recall Feedback
Lee Cohen, Yishay Mansour, Shay Moran +1
We consider the problem of learning an unknown subset of a domain in an online setting. In each round , the learner predicts a set of items and receive…
A Theoretical Framework for Statistical Evaluability of Generative Models
Shashaank Aiyer, Yishay Mansour, Shay Moran +1
Statistical evaluation aims to estimate the generalization performance of a model using held-out i.i.d. test data sampled from the ground-truth distribution. In supervised learning…
Equitable Evaluation via Elicitation
Elbert Du, Cynthia Dwork, Lunjia Hu +3
Individuals with similar qualifications and skills may vary in their demeanor, or outward manner: some tend toward self-promotion while others are modest to the point of omitting c…
On Randomized Algorithms in Online Strategic Classification
Chase Hutton, Adam Melrod, Han Shao
Online strategic classification studies settings in which agents strategically modify their features to obtain favorable predictions. For example, given a classifier that determine…
COPO: Consistency-Aware Policy Optimization
Jinghang Han, Jiawei Chen, Hang Shao +7
Reinforcement learning has significantly enhanced the reasoning capabilities of Large Language Models (LLMs) in complex problem-solving tasks. Recently, the introduction of DeepSee…
How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension
Cynthia Dwork, Lunjia Hu, Han Shao
We study a fundamental question of domain generalization: given a family of domains (i.e., data distributions), how many randomly sampled domains do we need to collect data from in…
A Machine Learning Theory Perspective on Strategic Litigation
Melissa Dutz, Han Shao, Avrim Blum +1
Strategic litigation involves bringing a case to court with the goal of having an impact beyond resolving the particular dispute at hand. In a common law system, one way a case may…
Probably Approximately Precision and Recall Learning
Lee Cohen, Yishay Mansour, Shay Moran +1
Precision and Recall are fundamental metrics in machine learning tasks where both accurate predictions and comprehensive coverage are essential, such as in multi-label learning, la…
Transformation-Invariant Learning and Theoretical Guarantees for OOD Generalization
Omar Montasser, Han Shao, Emmanuel Abbe
Learning with identical train and test distributions has been extensively investigated both practically and theoretically. Much remains to be understood, however, in statistical le…
Trustworthy Machine Learning under Social and Adversarial Data Sources
Han Shao
Machine learning has witnessed remarkable breakthroughs in recent years. As machine learning permeates various aspects of daily life, individuals and organizations increasingly int…
Learnability Gaps of Strategic Classification
Lee Cohen, Yishay Mansour, Shay Moran +1
In contrast with standard classification tasks, strategic classification involves agents strategically modifying their features in an effort to receive favorable predictions. For i…
On the Effect of Defections in Federated Learning and How to Prevent Them
Minbiao Han, Kumar Kshitij Patel, Han Shao +1
Federated learning is a machine learning protocol that enables a large population of agents to collaborate over multiple rounds to produce a single consensus model. There are sever…