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20122021
most citedOnline Bandit Learning against an Adaptive Adversary: from Regret to Policy Regret

88 citations · 126 across the 8 of their papers we have counts for

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17 papers · 1 filter

cs.LG2023

From Adaptive Query Release to Machine Unlearning

Enayat Ullah, Raman Arora

We formalize the problem of machine unlearning as design of efficient unlearning algorithms corresponding to learning algorithms which perform a selection of adaptive queries from…

cs.LG20214 cited

Machine Unlearning via Algorithmic Stability

Enayat Ullah, Tung Mai, Anup Rao +2

We study the problem of machine unlearning and identify a notion of algorithmic stability, Total Variation (TV) stability, which we argue, is suitable for the goal of exact unlearn…

cs.LG2020

On Convergence and Generalization of Dropout Training

Poorya Mianjy, Raman Arora

We study dropout in two-layer neural networks with rectified linear unit (ReLU) activations. Under mild overparametrization and assuming that the limiting kernel can separate the d…

cs.LG2020

Adversarial Robustness of Supervised Sparse Coding

Jeremias Sulam, Ramchandran Muthukumar, Raman Arora

Several recent results provide theoretical insights into the phenomena of adversarial examples. Existing results, however, are often limited due to a gap between the simplicity of…

cs.LG2020

Corralling Stochastic Bandit Algorithms

Raman Arora, Teodor V. Marinov, Mehryar Mohri

We study the problem of corralling stochastic bandit algorithms, that is combining multiple bandit algorithms designed for a stochastic environment, with the goal of devising a cor…

cs.LG2020

Dropout: Explicit Forms and Capacity Control

Raman Arora, Peter Bartlett, Poorya Mianjy +1

We investigate the capacity control provided by dropout in various machine learning problems. First, we study dropout for matrix completion, where it induces a data-dependent regul…