2 citations · 3 across the 16 of their papers we have counts for
6 papers · 1 filter
Machine Unlearning as Private Retroactive Algorithms
Haim Kaplan, Refael Kohen, Yishay Mansour +2
Machine unlearning typically aims to emulate retraining from scratch: upon a deletion request, the unlearning algorithm should produce an outcome that would have been obtained had…
Is Randomness Necessary for Adaptive Data Analysis?
Edith Cohen, Haim Kaplan, Yishay Mansour +2
The Adaptive Data Analysis (ADA) problem formalizes the challenge of preventing false discovery and overfitting when a dataset is repeatedly reused. Formally, our input is a datase…
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…
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…