2 citations · 3 across the 16 of their papers we have counts for
19 papers
Strategic PAC Learnability via Geometric Definability
Yuval Filmus, Shay Moran, Elizaveta Nesterova +2
Strategic classification studies learning settings in which individuals can modify their features, at a cost, in order to influence the classifier's decision. A central question is…
The Sample Complexity of Multiclass and Sparse Contextual Bandits
Liad Erez, Fan Chen, Alon Cohen +4
We study contextual bandits in the stochastic i.i.d.\ setting, where a learner observes contexts drawn from an unknown distribution, selects actions from a finite set , and aims…
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…
Learning from Equivalence Queries, Revisited
Mark Braverman, Roi Livni, Yishay Mansour +2
Modern machine learning systems, such as generative models and recommendation systems, often evolve through a cycle of deployment, user interaction, and periodic model updates. Thi…
Learning Conditional Averages
Marco Bressan, Nataly Brukhim, Nicolo Cesa-Bianchi +4
We introduce the problem of learning conditional averages in the PAC framework. The learner receives a sample labeled by an unknown target concept from a known concept class, as in…
Sample Complexity of Agnostic Multiclass Classification: Natarajan Dimension Strikes Back
Alon Cohen, Liad Erez, Steve Hanneke +4
The fundamental theorem of statistical learning states that binary PAC learning is governed by a single parameter -- the Vapnik-Chervonenkis (VC) dimension -- which determines both…
The Role of Randomness in Stability
Max Hopkins, Shay Moran
Stability is a central property in learning and statistics promising the output of an algorithm does not change substantially when applied to similar datasets and . It…
Of Dice and Games: A Theory of Generalized Boosting
Marco Bressan, Nataly Brukhim, Nicolò Cesa-Bianchi +4
Cost-sensitive loss functions are crucial in many real-world prediction problems, where different types of errors are penalized differently; for example, in medical diagnosis, a fa…
On Differentially Private Linear Algebra
Haim Kaplan, Yishay Mansour, Shay Moran +2
We introduce efficient differentially private (DP) algorithms for several linear algebraic tasks, including solving linear equalities over arbitrary fields, linear inequalities ove…
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…
The Real Price of Bandit Information in Multiclass Classification
Liad Erez, Alon Cohen, Tomer Koren +2
We revisit the classical problem of multiclass classification with bandit feedback (Kakade, Shalev-Shwartz and Tewari, 2008), where each input classifies to one of possible lab…
Fast Rates for Bandit PAC Multiclass Classification
Liad Erez, Alon Cohen, Tomer Koren +2
We study multiclass PAC learning with bandit feedback, where inputs are classified into one of possible labels and feedback is limited to whether or not the predicted labels ar…
A Theory of Interpretable Approximations
Marco Bressan, Nicolò Cesa-Bianchi, Emmanuel Esposito +3
Can a deep neural network be approximated by a small decision tree based on simple features? This question and its variants are behind the growing demand for machine learning model…
Credit Attribution and Stable Compression
Roi Livni, Shay Moran, Kobbi Nissim +1
Credit attribution is crucial across various fields. In academic research, proper citation acknowledges prior work and establishes original contributions. Similarly, in generative…
Data Reconstruction: When You See It and When You Don't
Edith Cohen, Haim Kaplan, Yishay Mansour +4
We revisit the fundamental question of formally defining what constitutes a reconstruction attack. While often clear from the context, our exploration reveals that a precise defini…
Learning-Augmented Algorithms with Explicit Predictors
Marek Elias, Haim Kaplan, Yishay Mansour +1
Recent advances in algorithmic design show how to utilize predictions obtained by machine learning models from past and present data. These approaches have demonstrated an enhancem…
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…