Search Results for author: Hugo Gilbert

Found 12 papers, 1 papers with code

Robust Ordinal Regression for Subsets Comparisons with Interactions

no code implementations7 Aug 2023 Hugo Gilbert, Mohamed Ouaguenouni, Meltem Ozturk, Olivier Spanjaard

This paper is dedicated to a robust ordinal method for learning the preferences of a decision maker between subsets.

regression Relation

Thou Shalt not Pick all Items if Thou are First: of Strategyproof and Fair Picking Sequences

no code implementations11 Jan 2023 Sylvain Bouveret, Hugo Gilbert, Jérôme Lang, Guillaume Méroué

When allocating indivisible items to agents, it is known that the only strategyproof mechanisms that satisfy a set of rather mild conditions are constrained serial dictatorships: given a fixed order over agents, at each step the designated agent chooses a given number of items (depending on her position in the sequence).

Measuring a Priori Voting Power -- Taking Delegations Seriously

no code implementations6 Jan 2023 Rachael Colley, Théo Delemazure, Hugo Gilbert

We introduce new power indices to measure the a priori voting power of voters in liquid democracy elections where an underlying network restricts delegations.

Cautious Learning of Multiattribute Preferences

no code implementations15 Jun 2022 Hugo Gilbert, Mohamed Ouaguenouni, Meltem Ozturk, Olivier Spanjaard

This paper is dedicated to a cautious learning methodology for predicting preferences between alternatives characterized by binary attributes (formally, each alternative is seen as a subset of attributes).

Attribute Relation

Computation and Bribery of Voting Power in Delegative Simple Games

no code implementations8 Apr 2021 Gianlorenzo D'Angelo, Esmaeil Delfaraz, Hugo Gilbert

Following Zhang and Grossi~(AAAI 2021), we study in more depth a variant of weighted voting games in which agents' weights are induced by a transitive support structure.

When Can Liquid Democracy Unveil the Truth?

no code implementations5 Apr 2021 Ruben Becker, Gianlorenzo D'Angelo, Esmaeil Delfaraz, Hugo Gilbert

Interestingly, under some hypothesis on either the accuracies of voters or the connectivity of the network, we obtain a polynomial-time $1/2$-approximation algorithm.


Fairness in Influence Maximization through Randomization

1 code implementation7 Oct 2020 Ruben Becker, Gianlorenzo D'Angelo, Sajjad Ghobadi, Hugo Gilbert

Maybe non-surprisingly, we show that the ex-ante values of the computed probabilistic strategies are significantly larger than the (ex-post) fairness values of previous methods.

Fairness Social and Information Networks Data Structures and Algorithms Computer Science and Game Theory

Beyond Pairwise Comparisons in Social Choice: A Setwise Kemeny Aggregation Problem

no code implementations14 Nov 2019 Hugo Gilbert, Tom Portoleau, Olivier Spanjaard

In this paper, we advocate the use of setwise contests for aggregating a set of input rankings into an output ranking.

The Convergence of Iterative Delegations in Liquid Democracy in a Social Network

no code implementations10 Apr 2019 Bruno Escoffier, Hugo Gilbert, Adèle Pass-Lanneau

We answer several questions on the equilibria of this process in any social network or in social networks that correspond to restricted types of graphs.

Decision Making

Iterative Delegations in Liquid Democracy with Restricted Preferences

no code implementations12 Sep 2018 Bruno Escoffier, Hugo Gilbert, Adèle Pass-Lanneau

In this paper, we study liquid democracy, a collective decision making paradigm which lies between direct and representative democracy.

Decision Making

Optimizing Quantiles in Preference-based Markov Decision Processes

no code implementations1 Dec 2016 Hugo Gilbert, Paul Weng, Yan Xu

In the Markov decision process model, policies are usually evaluated by expected cumulative rewards.

Quantile Reinforcement Learning

no code implementations3 Nov 2016 Hugo Gilbert, Paul Weng

In reinforcement learning, the standard criterion to evaluate policies in a state is the expectation of (discounted) sum of rewards.

reinforcement-learning Reinforcement Learning (RL)

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