In this work, we aim to characterize the statistical complexity of realizable regression both in
the PAC learning setting and the online learning setting. Previous work had established the …

We prove novel algorithmic guarantees for several online problems in the smoothed
analysis model. In this model, at each time step an adversary chooses an input distribution …

We study repeated bilateral trade where an adaptive $\sigma $-smooth adversary generates
the valuations of sellers and buyers. We provide a complete characterization of the regret …

In order to circumvent statistical and computational hardness results in sequential decision-
making, recent work has considered smoothed online learning, where the distribution of …

Exploration is a major challenge in reinforcement learning, especially for high-dimensional
domains that require function approximation. We propose exploration objectives--policy …

In the literature on game-theoretic equilibrium finding, focus has mainly been on solving a
single game in isolation. In practice, however, strategic interactions--ranging from routing …

We study the problem of regret minimization for a single bidder in a sequence of first-price
auctions where the bidder discovers the item's value only if the auction is won. Our main …

We initiate the study of smoothed analysis for the sequential probability assignment problem
with contexts. We study information-theoretically optimal minmax rates as well as a …

In the experts problem, on each of T days, an agent needs to follow the advice of one of n
“experts”. After each day, the loss associated with each expert's advice is revealed. A …

Online learning of revenue-optimal auctions is a fundamental problem in mechanism design
without priors. Nevertheless, all the existing positive results assume that the auctioneer …