Petrarca y los De viris illustribus

Anejos de Veleia, Series Minor, 26. Editado por Vitalino Valcárcel.

La influencia de Hegel sobre Croce y Gentile : un texto inédito de Miguel Angel Virasoro

Fil: Licata, Rosa. Universidad Nacional de Cuyo

La historia di Gineura de gl' Almieri, che fv seppellita per morta, nella città di Fiorenza : nella quale si contiene vn bel caso d'amore : opera veramente degna d'essere letta da ogni gentile spirito.

Ascribed to Agostino Velletti--Cf. BM cat., vol. 4, col. 28.

Through Gentile eyes; a plea for tolerance and good will,

Electronic text and image data

Extracts from the flying roll : being a series of sermons compiled for the Gentile churches of all sects and denominations, and addressed to the lost tribes of the House of Israel /

Mode of access: Internet.

Delle opere di maestro Gentile da Fabriano : memorie pittoriche /

Mode of access: Internet.

Bounded parameter Markov Decision Processes with average reward criterion

Bounded parameter Markov Decision Processes (BMDPs) address the issue of dealing with uncertainty in the parameters of a Markov Decision Process (MDP). Unlike the case of an MDP, the notion of an optimal policy for a BMDP is not entirely straightforward. We consider two notions of optimality based on optimistic and pessimistic criteria. These have been analyzed for discounted BMDPs. Here we provide results for average reward BMDPs. We establish a fundamental relationship between the discounted and the average reward problems, prove the existence of Blackwell optimal policies and, for both notions of optimality, derive algorithms that converge to the optimal value function.

Multitask learning with expert advice

We consider the problem of prediction with expert advice in the setting where a forecaster is presented with several online prediction tasks. Instead of competing against the best expert separately on each task, we assume the tasks are related, and thus we expect that a few experts will perform well on the entire set of tasks. That is, our forecaster would like, on each task, to compete against the best expert chosen from a small set of experts. While we describe the “ideal” algorithm and its performance bound, we show that the computation required for this algorithm is as hard as computation of a matrix permanent. We present an efficient algorithm based on mixing priors, and prove a bound that is nearly as good for the sequential task presentation case. We also consider a harder case where the task may change arbitrarily from round to round, and we develop an efficient approximate randomized algorithm based on Markov chain Monte Carlo techniques.