Paris Dauphine University

About: Paris Dauphine University is a education organization based out in Paris, France. It is known for research contribution in the topics: Context (language use) & Population. The organization has 1766 authors who have published 6909 publications receiving 162747 citations. The organization is also known as: Paris Dauphine & Dauphine.

Conditional importance networks: a graphical language for representing ordinal, monotonic preferences over sets of goods

Abstract: While there are several languages for representing combinatorial preferences over sets of alternatives, none of these are well-suited to the representation of ordinal preferences over sets of goods (which are typically required to be monotonic). We propose such a language, taking inspiration from previous work on graphical languages for preference representation, specifically CP-nets, and introduce conditional importance networks (CI-nets). A CI-net includes statements of the form "if I have a set A of goods, and I do not have any of the goods from some other set B, then I prefer the set of goods C over the set of goods D." We investigate expressivity and complexity issues for CI-nets. Then we show that CI-nets are well-suited to the description of fair division problems.

Multi-Resource Allocation for Network Slicing

Abstract: Among the novelties introduced by 5G networks, the formalization of the ‘network slice’ as a resource allocation unit is an important one. In legacy networks, resources such as link bandwidth, spectrum, computing capacity are allocated independently of each other. In 5G environments, a network slice is meant to directly serve end-to-end services, or verticals: behind a network slice demand, a tenant expresses the need to access a precise service type, under a fully qualified set of computing and network requirements. The resource allocation decision encompasses, therefore, a combination of different resources. In this paper, we address the problem of fairly sharing multiple resources between slices, in the critical situation in which the network does not have enough resources to fully satisfy slice demands. We model the problem as a multi-resource allocation problem, proposing a versatile optimization framework based on the Ordered Weighted Average (OWA) operator, that takes into account different fairness approaches. We show how, adapting the OWA utility function, our framework can generalize classical single-resource allocation methods, existing multi-resource allocation solutions at the state of the art, and implement novel multi-resource allocation solutions. We compare analytically and by extensive simulations the different methods in terms of fairness and system efficiency.

Group sparsity with overlapping partition functions

Abstract: This paper introduces a novel and versatile group sparsity prior for denoising and to regularize inverse problems. The sparsity is enforced through arbitrary block-localization operators, such as for instance smooth localized partition functions. The resulting blocks can have an arbitrary overlap, which is important to reduce visual artifacts thanks to the increased translation invariance of the prior. They are moreover not necessarily binary, and allow for non-integer block sizes. We develop two schemes, one primal and another primal-dual, originating from the non-smooth convex optimization realm, to efficiently solve a wide class of inverse problems regularized using this overlapping group sparsity prior. This scheme is flexible enough to handle both penalized and constrained versions of the optimization problems at hand. Numerical results on denoising and compressed sensing are reported and show the improvement brought by the overlap and the smooth partition functions with respect to classical group sparsity.