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, ...
Communication and collaboration is difficult in geographically distributed settings. As a result of globalization, merges and acquisition, and scarce skills, software development...
Recommender systems are an important component of many websites. Two of the most popular approaches are based on matrix factorization (MF) and Markov chains (MC). MF methods learn...
Steffen Rendle, Christoph Freudenthaler, Lars Schm...
ELeGI, the European Learning Grid Infrastructure, has the ambitious goal of fostering effective learning and knowledge construction through the dynamic provision of service-based ...
A standard method for approximating averages in probabilistic models is to construct a Markov chain in the product space of the random variables with the desired equilibrium distr...