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In this work, we approach the issue of privacy in distributed constraint reasoning by studying how agents compromise solution quality for preserving privacy, using utility and game theory. We propose a utilitarian definition of privacy in the context of distributed constraint reasoning, detail...
Recently, Mahloujifar and Mahmoody (Theory of Cryptography Conference’17) studied attacks against learning algorithms using a special case of Valiant’s malicious noise, called p-tampering, in which the adversary gets to change any training example with independent probability p but is limited to...
In statistical learning theory, numerous works established non-asymptotic bounds assessing the generalization capacity of empirical risk minimizers under a large variety of complexity assumptions for the class of decision rules over which optimization is performed, by means of sharp control of...
Nowadays, energy represents the most important resource; however, we need to face several energy-related rising issues, one main concern is how energy is consumed. In particular, how we can stimulate consumers on a specific behaviour. In this work, we present a model facing energy allocation and...
We study the complexity of fair division of indivisible goods and consider settings where agents can have nonzero utility for the empty bundle. This is a deviation from a common normalization assumption in the literature, and we show that this inconspicuous change can lead to an increase in...
In this paper we combine the theory of probability aggregation with results of machine learning theory concerning the optimality of predictions under expert advice. In probability aggregation theory several characterization results for linear aggregation exist. However, in linear aggregation...
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