124 citations · 547 across the 36 of their papers we have counts for
39 papers
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…
VIPeR: Provably Efficient Algorithm for Offline RL with Neural Function Approximation
Thanh Nguyen-Tang, Raman Arora
We propose a novel algorithm for offline reinforcement learning called Value Iteration with Perturbed Rewards (VIPeR), which amalgamates the pessimism principle with random perturb…
On Instance-Dependent Bounds for Offline Reinforcement Learning with Linear Function Approximation
Thanh Nguyen-Tang, Ming Yin, Sunil Gupta +2
Sample-efficient offline reinforcement learning (RL) with linear function approximation has recently been studied extensively. Much of prior work has yielded the minimax-optimal bo…
A Risk-Sensitive Approach to Policy Optimization
Jared Markowitz, Ryan W. Gardner, Ashley Llorens +2
Standard deep reinforcement learning (DRL) aims to maximize expected reward, considering collected experiences equally in formulating a policy. This differs from human decision-mak…
Adversarial Robustness is at Odds with Lazy Training
Yunjuan Wang, Enayat Ullah, Poorya Mianjy +1
Recent works show that adversarial examples exist for random neural networks [Daniely and Schacham, 2020] and that these examples can be found using a single step of gradient ascen…
Faster Rates of Convergence to Stationary Points in Differentially Private Optimization
Raman Arora, Raef Bassily, Tomás González +3
We study the problem of approximating stationary points of Lipschitz and smooth functions under -differential privacy (DP) in both the finite-sum and stochastic se…