Jesus Cerquides

EAAI Journal 2025 Journal Article

Jekyll institute or Mrs Hyde? gender identification with machine learning

• Arnault Gombert
• Borja Sánchez-López
• Jesus Cerquides

Social media platforms offer an invaluable wealth of data to understand what is taking place in our society. However, social media data hides demographic biases related to characteristics such as gender or age. Therefore, considering social media data as representative of the population can lead to fallacious interpretations. For instance, in France in 2021, women represent 51. 6% of the population 1 1 https: //www. insee. fr/fr/statistiques/6024136. , whereas on Twitter they represent only 33. 5% of French users 2 2 https: //datareportal. com/reports/digital-2021-france. . With such a significant difference between social network user demographics and the actual population, detecting the gender or age before delving into a deeper analysis of social phenomena becomes a priority. In this paper, we tackle the gender detection problem on Twitter. We introduce miniAM2, which is an assemblage model of an enriched distillation with weak-supervised learning. Our contributions are threefold: (i) a novel multilingual model that outperforms existing models in both accuracy and speed, allowing for real-time gender detection and organization status on Twitter based on their name, screen_name, and description, making it lighter and faster than state-of-the-art; (ii) an innovative assemblage multi-language strategy that enriches a distillation process with weak-supervised learning using minimal annotated data, and (iii) a unique method to adapt the model to similar languages without requiring annotated data in the target language, which provides significant advancements in handling resource-poor languages in gender detection tasks. We provide our model on demand so social scientists can use it for their analysis.

AAMAS Conference 2013 Conference Paper

CHAINME: Fast Decentralized Finding of Better Supply Chains

• Toni Penya-Alba
• Jesus Cerquides
• Juan A. Rodriguez-Aguilar
• Meritxell Vinyals

Decentralized Supply Chain Formation (SCF) appears as a highly intricate task because agents only possess local information, have limited knowledge about the capabilities of other agents, and prefer to preserve privacy. State-of-theart decentralized SCF approaches can either: (i) find Supply Chains (SC) of high value at the expense of high resources usage; or (ii) find SCs of low value with low resources usage. This work presents chainme, a novel decentralized SCF algorithm. Our results show that chainme finds SCs with higher value than state-of-the-art decentralized algorithms whilst decreasing the amount of resources required from one up to four orders of magnitude.

AAAI Conference 2012 Conference Paper

A Scalable Message-Passing Algorithm for Supply Chain Formation

• Toni Penya-Alba
• Meritxell Vinyals
• Jesus Cerquides
• Juan Rodriguez-Aguilar

Supply Chain Formation (SCF) is the process of determining the participants in a supply chain, who will exchange what with whom, and the terms of the exchanges. Decentralized SCF appears as a highly intricate task because agents only possess local information and have limited knowledge about the capabilities of other agents. The decentralized SCF problem has been recently cast as an optimization problem that can be efficiently approximated using max-sum loopy belief propagation. Along this direction, in this paper we propose a novel encoding of the problem into a binary factor graph (containing only binary variables) as well as an alternative algorithm. We empirically show that our approach allows to significantly increase scalability, hence allowing to form supply chains in market scenarios with a large number of participants and high competition.

AAMAS Conference 2012 Conference Paper

Scalable decentralized supply chain formation through binarized belief propagation

• Toni Penya-Alba
• Jesus Cerquides
• Juan Antonio Rodriguez-Aguilar
• Meritxell Vinyals

Supply Chain Formation (SCF) is the process of determining the participants in a supply chain, who will exchange what with whom, and the terms of the exchanges. Decentralized SCF appears as a highly intricate task because agents only possess local information, have limited knowledge about the capabilities of other agents, and prefer to preserve privacy. Very recently, the decentralized SCF problem has been cast as an optimization problem that can be efficiently approximated using max-sum loopy belief propagation. Unfortunately, the memory and communication requirements of this approach largely hinder its scalability. This paper presents a novel encoding of the problem into a binary factor graph (containing only binary variables) along with an alternative algorithm. These allow to scale up to form supply chains in markets with higher degrees of competition.

AAMAS Conference 2011 Conference Paper

Communication-Constrained DCOPs: Message Approximation in GDL with Function Filtering

• Marc Pujol-Gonzalez
• Jesus Cerquides
• Pedro Meseguer
• Juan Antonio Rodriguez-Aguilar

In this paper we focus on solving DCOPs in communication constrained scenarios. The GDL algorithm optimally solves DCOP problems, but requires the exchange of exponentially large messages which makes it impractical in such settings. Function filtering is a technique that alleviates this high communication requirement while maintaining optimality. Function filtering involves calculating approximations of the exact cost functions exchanged by GDL. In this work, we explore different ways to compute such approximations, providing a novel method that empirically achieves significant communication savings.

AAMAS Conference 2011 Conference Paper

Quality Guarantees for Region Optimal DCOP Algorithms

• Meritxell Vinyals
• Eric Shieh
• Jesus Cerquides
• Juan Antonio Rodriguez-Aguilar
• Zhengyu Yin
• Milind Tambe
• Emma Bowring

k - and t -optimality algorithms provide solutions to DCOPs that are optimal in regions characterized by its size and distance respectively. Moreover, they provide quality guarantees on their solutions. Here we generalise the k - and t -optimal framework to introduce C -optimality, a flexible framework that provides reward-independent quality guarantees for optima in regions characterised by any arbitrary criterion. Therefore, C -optimality allows us to explore the space of criteria (beyond size and distance) looking for those that lead to better solution qualities. We benefit from this larger space of criteria to propose a new criterion, the socalled size-bounded-distance criterion, which outperforms k - and t -optimality.

AAMAS Conference 2008 Conference Paper

Computationally-efficient Winner Determination for Mixed Multi-Unit Combinatorial Auctions

• Andrea Giovannucci
• Meritxell Vinyals
• Juan A. Rodriguez-Aguilar
• Jesus Cerquides

AAMAS Conference 2007 Conference Paper

Winner Determination for Mixed Multi-unit Combinatorial Auctions via Petri Nets

• Andrea Giovannucci
• J. A. Rodriguez-Aguilar
• Jesus Cerquides
• Ulle Endriss

Mixed Multi-Unit Combinatorial Auctions (MMUCAs) allow agents to bid for bundles of goods to buy, goods to sell, and transformations of goods. In particular, MMUCAs offer a high potential to be employed for the automated assembly of supply chains of agents offering goods and services, and in general MMUCAs extend and generalise several types of combinatorial auctions. Here we provide a formalism, based on an extension of Petri Nets, with which MMUCAs, and therefore all auction types subsumed by MMUCAs –and in particular combinatorial auctions for supply chain formation (SCF)–, can be formally analysed. As a second direct benefit, consequence of the provided mapping to Petri Nets, we manage to dramatically reduce the number of decision variables involved in the optimisation problem posed by MMUCAs from quadratic to linear for a wide class of MMUCA Winner Determination Problems (WDPs). Hence, we also make headway in the practical application of MMUCAs, and in particular to SCF.

AAMAS Conference 2006 Conference Paper

Auctioning Transformable Goods

• Andrea Giovannucci
• Juan Antonio Rodriguez-Aguilar
• Jesus Cerquides