We study an extension to the stochastic multiarmed bandit problem where the learner has a budget of K “coins ” it can use in each round. The learner can use the coins to play multiple arms in each round, having the option to “bet ” multiple coins on an arm. At the end of the round, the arms generate a reward that is proportional to the amount of coins invested in them. 1.

Gossip-based aggregation of trust in decentralized

We study the problem of labeling the edges of a social network graph (e.g., acquaintance connections in Facebook) as either positive (i.e., trust, true friendship) or negative (i.e., distrust, possible frenemy) relations. Such signed relations provide much stronger signal in tying the behavior of online users than the unipolar Homophily effect, yet are largely unavailable as most social graphs only contain unsigned edges. We show the surprising fact that it is possible to infer signed social ties with good accuracy solely based on users’ behavior of decision making (or using only a small fraction of supervision information) via unsupervised and semisupervised algorithms. This work hereby makes it possible to turn an unsigned acquaintance network (e.g. Facebook, Myspace) into a signed trust-distrust network (e.g. Epinion, Slashdot). Our results are based on a mixed effects framework that simultaneously captures users ’ behavior, social interactions as well as the interplay between the two. The framework includes a series of latent factor models and it accommodates the principles of balance and status from Social psychology. Experiments on Epinion and Yahoo! Pulse networks illustrate that (1) signed social ties can be predicted with high-accuracy even in fully unsupervised settings, and (2) the predicted signed ties are significantly more useful for social behavior prediction than simple Homophily.