University of Marne-la-Vallée

About: University of Marne-la-Vallée is a based out in . It is known for research contribution in the topics: Estimator & Context (language use). The organization has 831 authors who have published 1855 publications receiving 55316 citations.

Optimal learning with Q-aggregation

Abstract: We consider a general supervised learning problem with strongly convex and Lipshitz loss and study the problem of model selection aggre- gation. In particular, given a finite dictionary functions (learners) together with the prior, we generalize the results obtained by Dai, Rigollet and Zhang (2012) for Gaussian regression with squared loss and fixed design to this learning setup. Specifically, we prove that the Q-aggregation pro- cedure outputs an estimator that satisfies optimal oracle inequalities both in expectation and with high probability. Our proof techniques somewhat depart from traditional proofs by making most of the standard arguments on the Laplace transform of the empirical process to be controlled. AMS 2000 subject classifications: Primary 62H25; secondary 62F04, 90C22.

Complementation of rational sets on countable scattered linear orderings

Abstract: In a preceding paper (Bruyere and Carton, automata on linear orderings, MFCS'01), automata have been introduced for words indexed by linear orderings. These automata are a generalization of automata for finite, infinite, bi-infinite and even transfinite words studied by Buchi. Kleene's theorem has been generalized to these words. We prove that rational sets of words on countable scattered linear orderings are closed under complementation using an algebraic approach.

PHRHLS: A movement-prediction-based joint routing and Hierarchical Location Service for VANETs

Abstract: Location-based services provide (and maintain) location information used by geographic routing protocols. Routing and location service are widely related, but handled separately in usual studies about Vehicular Ad hoc Network (VANET). In this paper, we propose a hybrid approach, denoted mobility-Prediction-based Hybrid Routing and Hierarchical Location Service (PHRHLS), coupling a VANET routing protocol, the Greedy Perimeter Stateless Routing (GPSR), and the Hierarchical Location Service (HLS) extended with a mobility prediction algorithm. We show that our approach, PHRHLS, reduces the localization overhead and enhances the routing performances. Indeed, our extensive simulations show promising results in terms of end-to-end latency, packet delivery ratio and control message overhead.