We introduce several new black-box reductions that significantly improve the design of
adaptive and parameter-free online learning algorithms by simplifying analysis, improving …

We resolve the long-standing" impossible tuning" issue for the classic expert problem and
show that, it is in fact possible to achieve regret $ O\left (\sqrt {(\ln d)\sum_t\ell_ {t, i}^ 2}\right) …

Online auctions are one of the most fundamental facets of the modern economy and power
an industry generating hundreds of billions of dollars a year in revenue. Auction theory has …

Abstract We investigate online Markov Decision Processes (MDPs) with adversarially
changing loss functions and known transitions. We choose dynamic regret as the …

We study sequential bilateral trade where sellers and buyers valuations are completely
arbitrary ({\sl ie}, determined by an adversary). Sellers and buyers are strategic agents with …

This paper settles the sample complexity of single-parameter revenue maximization by
showing matching upper and lower bounds, up to a poly-logarithmic factor, for all families of …

Designing an incentive compatible auction that maximizes expected revenue is a central
problem in Auction Design. While theoretical approaches to the problem have hit some …

We study the stochastic shortest path problem with adversarial costs and known transition,
and show that the minimax regret is $ O (\sqrt {DT_\star K}) $ and $ O (\sqrt {DT_\star SA K}) …

We consider the design of computationally efficient online learning algorithms in an
adversarial setting in which the learner has access to an offline optimization oracle. We …

Although previous research efforts have developed models to assist contractors in different
bidding decisions, there is a lack of research work that investigates the impact of integrating …