Many classification problems require decisions among a large number of competing classes. These tasks, however, are not handled well by general purpose learning methods and are usually addressed in an ad-hoc fashion. We suggest a general approach – a sequential learning model that utilizes classifiers to sequentially restrict the number of competing classes while maintaining, with high probabil...

We consider a crowdsourcing platform where workers’ responses to questions posed by a crowdsourcer are used to determine the hidden state of a multi-class labeling problem. As workers may be unreliable, we propose to perform sequential questioning in which the questions posed to the workers are designed based on previous questions and answers. We propose a Partially-Observable Markov Decision P...

Many relevant industrial optimization tasks feature more than just one quality criterion. State-of-the-art multi-criteria optimization algorithms require a relatively large number of function evaluations (usually more than 10) to approximate Pareto fronts. Due to high cost or time consumption this large amount of function evaluations is not always available. Therefore, it is obvious to combine ...

cache, performance evaluations, prefetching As processor clock rates increase the memory hierarchy is hard pressed to keep up. One way of mitigating the increasing gap between processor and memory is by prefetching items from memory before they are requested by the processor. Various algorithms perform better or worse depending on how accurately they predict the needed items and in how timely a...

The problem of multi-task learning (MTL) is considered for sequential data, such as that typically modeled via a hidden Markov model (HMM). A given task is composed of a set of sequential data, for which an HMM is to be learned, and MTL is employed to learn the multiple task-dependent HMMs jointly, through appropriate sharing of data. The HMM-MTL formulation is implemented in a Bayesian setting...

We introduce the study of sequential information elicitation in strategic multi-agent systems. In an information elicitation setup a center attempts to compute the value of a function based on private information (a-ka secrets) accessible to a set of agents. We consider the classical multi-party computation setup where each agent is interested in knowing the result of the function. However, in ...

Al~a'aet. We describe an extension of the single-frame visual field reconstruction problem in which we consider how to efficiently and optimally fuse multiple frames of measurements obtained from images arriving sequentially over time. Specifically we extend the notion of spatial coherence constraints, used to regularize single-frame problems, to the time axis yielding temporal coherence constr...

We define a fairness solution criterion for multi-agent decision-making problems, where agents have local interests. This new criterion aims to maximize the worst performance of agents with a consideration on the overall performance. We develop a simple linear programming approach and a more scalable game-theoretic approach for computing an optimal fairness policy. This game-theoretic approach ...

Given a multiset of n positive integers, the NP-complete problem of number partitioning is to assign each integer to one of k subsets, such that the largest sum of the integers assigned to any subset is minimized. Last year, three different papers on optimally solving this problem appeared in the literature, two from the first two authors, and one from the third author. We resolve here competin...

For Business-To-Business integration (B2Bi) scenarios, the application of choreography and orchestration technology has become a core technique for resolving discrepancies between the interaction logic of individual partners and the intended overall message flow. While orchestrations govern the message exchanges of each single partner, choreographies define constraints and requirements for the ...