Abstract. We consider an upper confidence bound algorithm for Markov decision processes (MDPs) with deterministic transitions. For this algorithm we derive upper bounds on the onl...
We present a competitive analysis of Bayesian learning algorithms in the online learning setting and show that many simple Bayesian algorithms (such as Gaussian linear regression ...
A self-stabilizing algorithm, regardless of the initial system state, converges in finite time to a set of states that satisfy a legitimacy predicate without the need for explici...
Abstract Weighted Voting Superposition (WeVoS) is a novel summarization algorithm for the results of an ensemble of Self-Organizing Maps. Its principal aim is to achieve the lowest...
We investigate ways in which an algorithm can improve its expected performance by fine-tuning itself automatically with respect to an arbitrary, unknown input distribution. We gi...
Nir Ailon, Bernard Chazelle, Kenneth L. Clarkson, ...