44 citations · 72 across the 6 of their papers we have counts for
15 papers
The Complexity of Markov Equilibrium in Stochastic Games
Constantinos Daskalakis, Noah Golowich, Kaiqing Zhang
We show that computing approximate stationary Markov coarse correlated equilibria (CCE) in general-sum stochastic games is computationally intractable, even when there are two play…
Smoothed Online Learning is as Easy as Statistical Learning
Adam Block, Yuval Dagan, Noah Golowich +1
Much of modern learning theory has been split between two regimes: the classical offline setting, where data arrive independently, and the online setting, where data arrive adversa…
Can Q-Learning be Improved with Advice?
Noah Golowich, Ankur Moitra
Despite rapid progress in theoretical reinforcement learning (RL) over the last few years, most of the known guarantees are worst-case in nature, failing to take advantage of struc…
Deep Learning with Label Differential Privacy
Badih Ghazi, Noah Golowich, Ravi Kumar +2
The Randomized Response (RR) algorithm is a classical technique to improve robustness in survey aggregation, and has been widely adopted in applications with differential privacy g…
Independent Policy Gradient Methods for Competitive Reinforcement Learning
Constantinos Daskalakis, Dylan J. Foster, Noah Golowich
We obtain global, non-asymptotic convergence guarantees for independent learning algorithms in competitive reinforcement learning settings with two agents (i.e., zero-sum stochasti…
Sample-efficient proper PAC learning with approximate differential privacy
Badih Ghazi, Noah Golowich, Ravi Kumar +1
In this paper we prove that the sample complexity of properly learning a class of Littlestone dimension with approximate differential privacy is , ignoring priva…