We consider the adversarial combinatorial multi-armed bandit (CMAB) problem, whose decisionset can be exponentially large with respect to the number of given arms. To avoid dealing with suchlarge decision sets directly, we propose an algorithm performed on a zero-suppressed binary decisiondiagram (ZDD), which is a compressed representation of the decision set. The proposed algor...

Harnessing the power of multicore platforms is challenging due to the additional levels of parallelism present. In this paper, we examine the effect of the choice of programming model upon performance and overall memory usage on the Cray XT5. We use detailed time breakdowns to measure the contributions to the total runtime from computation, communication, and OpenMP regions of the applications,...

When people in a society want to make inference about some parameter, each person would potentially want to use data collected by other people. Information (data) exchange in social contexts is usually costly, so to make sound statistical decisions, people need to compromise between benefits and costs of information acquisition. Conflicts of interests and coordination will arise. Classical stat...

We consider the stochastic and adversarial settings of continuum armed bandits where the arms are indexed by [0, 1]. The reward functions r : [0, 1] → R are assumed to intrinsically depend on at most k coordinate variables implying r(x1, . . . , xd) = g(xi1 , . . . , xik ) for distinct and unknown i1, . . . , ik ∈ {1, . . . , d} and some locally Hölder continuous g : [0, 1] → R with exponent α ...

Over the last two decades, using simple radiation models has been an interesting task to estimate daily solar radiation in arid and semiarid deserts such as those in Iran, where the number of solar observation sites is poor. In Iran, most of the models used so far, have been validated for a few specific locations based on short-term solar observations. In this work, three different radiation mo...

We construct a 4-round multi-party computation protocol in the plain model for any functionality, secure against a malicious adversary. Our protocol relies on the sub-exponential hardness of the Learning with Errors (LWE) problem with slightly super-polynomial noise ratio, and on the existence of adaptively secure commitments. Our round complexity matches a lower bound of Garg et al. (EUROCRYPT...

This paper examines the problem of multi-agent learning in N -person non-cooperative games. For concreteness, we focus on the socalled “hedge” variant of the exponential weights (EW) algorithm, one of the most widely studied algorithmic schemes for regret minimization in online learning. In this multi-agent context, we show that a) dominated strategies become extinct (a.s.); and b) in generic g...

In the setting of secure multiparty computation, a set of mutually distrustful parties carry out a joint computation of their inputs, without revealing anything but the output. Over recent years, there has been tremendous progress towards making secure computation practical, with great success in the two-party case. In contrast, in the multiparty case, progress has been much slower, even for th...

We consider a variant of the stochastic multi-armed bandit problem, where multiple players simultaneously choose from the same set of arms and may collide, receiving no reward. This setting has been motivated by problems arising in cognitive radio networks, and is especially challenging under the realistic assumption that communication between players is limited. We provide a communication-free...

A discrete-feature model was used to integrate deterministic and stochastic geoscientific information, to support the Swedish regulatory authorities’ review of SR-Can. The discrete-feature approach was used to replicate the main geometric features of the proponents’ models of the two candidate sites, including: (1) deformation zones on scales of 1 km to 10 km which are treated deterministically...