124 citations · 547 across the 36 of their papers we have counts for
5 papers · 1 filter
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…
Differentially Private Generalized Linear Models Revisited
Raman Arora, Raef Bassily, Cristóbal Guzmán +2
We study the problem of -differentially private learning of linear predictors with convex losses. We provide results for two subclasses of loss functions. The first case is…