We study the problem of best-arm identification with fixed budget in
sto...
We consider the problem of recovering hidden communities in the Labeled
...
In this paper, we address the problem of computing equilibria in monoton...
Learning in games considers how multiple agents maximize their own rewar...
Bandit algorithms for online learning to rank (OLTR) problems often aim ...
Repeated games consider a situation where multiple agents are motivated ...
The theory of learning in games is prominent in the AI community, motiva...
We consider the fixed-budget best arm identification problem in the
mult...
We consider Bayesian best arm identification in the multi-armed bandit
p...
Adaptive experimental design for efficient decision-making is an importa...
We study the best-arm identification problem with fixed confidence when
...
Off-policy evaluation (OPE) is the problem of estimating the value of a
...
This paper proposes a theoretical analysis of recommendation systems in ...
In this paper, we revisit sparse stochastic contextual linear bandits. I...
We study the problem of recovering clusters from binary user feedback. I...