We give an algorithm for the bandit version of a very general online optimization problem considered by Kalai and Vempala [1], for the case of an adaptive adversary. In this proble...
We present a modification of the algorithm of Dani et al. [8] for the online linear optimization problem in the bandit setting, which with high probability has regret at most O ( ...
Peter L. Bartlett, Varsha Dani, Thomas P. Hayes, S...
We study a general online convex optimization problem. We have a convex set S and an unknown sequence of cost functions c1, c2, . . . , and in each period, we choose a feasible po...
Abraham Flaxman, Adam Tauman Kalai, H. Brendan McM...
We study on-line decision problems where the set of actions that are available to the decision algorithm vary over time. With a few notable exceptions, such problems remained larg...
Robert D. Kleinberg, Alexandru Niculescu-Mizil, Yo...
In an online linear optimization problem, on each period t, an online algorithm chooses st S from a fixed (possibly infinite) set S of feasible decisions. Nature (who may be adve...