28 citations · 174 across the 52 of their papers we have counts for
67 papers
Vanilla Policy Optimization Is Both Optimal and Differentially Private for Stochastic Contextual Bandits
Idan Attias, Orin Levy, Alexander Ryabchenko +2
Can vanilla policy optimization explore enough to achieve near-optimal regret in stochastic contextual bandits? We show that standard exponential policy updates driven by offline r…
Tight Bounds on the Cost of Adaptivity for the Meyerson Sketch
Edith Cohen, Elena Gribelyuk, Pasin Manurangsi +1
In online facility location, points arrive one at a time, and the algorithm must either open a facility at the arriving point or route the point to an existing facility. The Meyers…
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…
Load Balancing under Adaptive Bin Deletions
Haim Kaplan, Shay Sapir, Uri Stemmer
We analyze a balls-and-bins game against an adaptive adversary that sequentially deletes bins. Starting with balls distributed across bins, the adversary deletes a bin in e…
Protecting the Undeleted in Machine Unlearning
Aloni Cohen, Refael Kohen, Kobbi Nissim +1
Machine unlearning aims to remove specific data points from a trained model, often striving to emulate "perfect retraining", i.e., producing the model that would have been obtained…