104 citations · 270 across the 46 of their papers we have counts for
6 papers · 1 filter
Fast Rates for Nonparametric Online Learning: From Realizability to Learning in Games
Constantinos Daskalakis, Noah Golowich
We study fast rates of convergence in the setting of nonparametric online regression, namely where regret is defined with respect to an arbitrary function class which has bounded c…
Near-Optimal No-Regret Learning for Correlated Equilibria in Multi-Player General-Sum Games
Ioannis Anagnostides, Constantinos Daskalakis, Gabriele Farina +3
Recently, Daskalakis, Fishelson, and Golowich (DFG) (NeurIPS`21) showed that if all agents in a multi-player general-sum normal-form game employ Optimistic Multiplicative Weights U…
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…
Near-Optimal No-Regret Learning in General Games
Constantinos Daskalakis, Maxwell Fishelson, Noah Golowich
We show that Optimistic Hedge -- a common variant of multiplicative-weights-updates with recency bias -- attains regret in multi-player general-sum games. In p…
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…