Zero-sum games can be solved using online learning dynamics, where a classical technique involves simulating two no-regret algorithms that play against each other and, afterT rounds, the average iterate is guaranteed to solve the original optimization problem with error decaying asO(logT/T). In this paper we show that the technique can be enhanced to a rate ofO(1/T2) by extending recent work [22, 25] that leverages optimistic learning to speed upequilibrium computation.

Conversely, adaptive first order methods are very popular in Machine Learning, with AdaGrad, [12],beingthemostprominent methodamongthisclass. AdaGrad isanonlinelearning algorithm which adapts its learning rate using the feedback (gradients) received through the optimization process, and is known to successfully handle noisy feedback.

Weintroduce asimple butgeneral online learning frameworkinwhich alearner plays against an adversary in a vector-valued game that changes every round. Even though the learner'sobjectiveis not convex-concave(and so the minimax theorem does not apply), we giveasimple algorithm that can compete with the setting in which the adversary must announce their action first, with optimally diminishing regret.

To this end, we propose DrugRec, a causal inference baseddrugrecommendationmodel.

However, the resulting methods often suffer from high computational complexity which has reduced their practical applicability. For example, in the case of multiclass logistic regression, the aggregating forecaster (Foster et al. (2018)) achievesaregret ofO(log(Bn))whereas Online Newton Step achieves O(eBlog(n))obtaining adouble exponential gaininB (aboundonthenormof comparativefunctions).

Aconceptually appealing approach forlearning Extensive-FormGames (EFGs) is to convert them to Normal-Form Games (NFGs).

Yet, their method calledSTORM crucially relies on the knowledge of thesmoothness, aswellaboundonthegradient norms.

The difference is significant, sincesp(h) Dand the gap between the twocanbearbitrarily large.

Our result shed light on the difference between Adam and (stochastic) gradient descent from a theoretical perspective.