1,020 research outputs found
Online learning with graph-structured feedback against adaptive adversaries
We derive upper and lower bounds for the policy regret of -round online
learning problems with graph-structured feedback, where the adversary is
nonoblivious but assumed to have a bounded memory. We obtain upper bounds of
and for strongly-observable and
weakly-observable graphs, respectively, based on analyzing a variant of the
Exp3 algorithm. When the adversary is allowed a bounded memory of size 1, we
show that a matching lower bound of is achieved in
the case of full-information feedback. We also study the particular loss
structure of an oblivious adversary with switching costs, and show that in such
a setting, non-revealing strongly-observable feedback graphs achieve a lower
bound of , as well.Comment: This paper has been accepted to ISIT 201
Optimal Allocation Strategies for the Dark Pool Problem
We study the problem of allocating stocks to dark pools. We propose and
analyze an optimal approach for allocations, if continuous-valued allocations
are allowed. We also propose a modification for the case when only
integer-valued allocations are possible. We extend the previous work on this
problem to adversarial scenarios, while also improving on their results in the
iid setup. The resulting algorithms are efficient, and perform well in
simulations under stochastic and adversarial inputs
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