no code implementations • 28 Sep 2023 • Brieuc Pinon, Raphaël Jungers, Jean-Charles Delvenne

We prove a fundamental limitation on the efficiency of a wide class of Reinforcement Learning (RL) algorithms.

no code implementations • 24 Aug 2022 • Brieuc Pinon, Jean-Charles Delvenne, Raphaël Jungers

Meta-Reinforcement Learning (meta-RL) methods demonstrate a capability to learn behaviors that efficiently acquire and exploit information in several meta-RL problems.

no code implementations • 23 Mar 2021 • Brieuc Pinon, Raphaël Jungers, Jean-Charles Delvenne

We provide lower and upper bounds on the potential gains in sample efficiency between the MDL applied with Turing machines instead of ANNs.

1 code implementation • 24 Aug 2020 • Nahuel Freitas, Jean-Charles Delvenne, Massimiliano Esposito

We present a general formalism for the construction of thermodynamically consistent stochastic models of non-linear electronic circuits.

Statistical Mechanics

2 code implementations • 4 Jun 2020 • Yun William Yu, Jean-Charles Delvenne, Sophia N. Yaliraki, Mauricio Barahona

A major goal of dynamical systems theory is the search for simplified descriptions of the dynamics of a large number of interacting states.

1 code implementation • 25 Nov 2019 • Leonardo Gutiérrez-Gómez, Alexandre Bovet, Jean-Charles Delvenne

Many social and economic systems can be represented as attributed networks encoding the relations between entities who are themselves described by different node attributes.

Social and Information Networks Physics and Society

1 code implementation • 13 Aug 2019 • Matteo Cinelli, Leto Peel, Antonio Iovanella, Jean-Charles Delvenne

We consider the network constraints on the bounds of the assortativity coefficient, which measures the tendency of nodes with the same attribute values to be interconnected.

Social and Information Networks Data Analysis, Statistics and Probability Physics and Society

1 code implementation • 25 Feb 2019 • Leonardo Gutiérrez-Gómez, Jean-Charles Delvenne

In this work we provide an unsupervised approach to learn embedding representation for a collection of graphs so that it can be used in numerous graph mining tasks.

1 code implementation • 24 Sep 2018 • Yérali Gandica, Adeline Decuyper, Christophe Cloquet, Isabelle Thomas, Jean-Charles Delvenne

Many times the nodes of a complex network, whether deliberately or not, are aggregated for technical, ethical, legal limitations or privacy reasons.

Physics and Society Social and Information Networks

1 code implementation • 14 Sep 2018 • Leonardo Gutierrez Gomez, Jean-Charles Delvenne

Several social, medical, engineering and biological challenges rely on discovering the functionality of networks from their structure and node metadata, when it is available.

2 code implementations • 10 Apr 2018 • Michael T. Schaub, Jean-Charles Delvenne, Renaud Lambiotte, Mauricio Barahona

Complex systems and relational data are often abstracted as dynamical processes on networks.

Social and Information Networks Systems and Control Physics and Society

1 code implementation • 30 Jan 2018 • Alexey N. Medvedev, Jean-Charles Delvenne, Renaud Lambiotte

We compare the efficiency of our approach with previous works and show its superiority for the prediction of the dynamics of discussions.

Social and Information Networks Probability Data Analysis, Statistics and Probability 90B18, 60K35, 60G55, 82C99

1 code implementation • 20 Nov 2017 • Michaël Fanuel, Antoine Aspeel, Jean-Charles Delvenne, Johan A. K. Suykens

In machine learning or statistics, it is often desirable to reduce the dimensionality of a sample of data points in a high dimensional space $\mathbb{R}^d$.

no code implementations • 30 May 2017 • Leonardo Gutierrez Gomez, Benjamin Chiem, Jean-Charles Delvenne

Numerous social, medical, engineering and biological challenges can be framed as graph-based learning tasks.

no code implementations • 24 Dec 2016 • Ernesto Estrada, Jean-Charles Delvenne, Naomichi Hatano, José L. Mateos, Ralf Metzler, Alejandro P. Riascos, Michael T. Schaub

Stated differently, for small parameter values the multi-hopper explores a general graph as fast as possible when compared to a random walker on a full graph.

Physics and Society Statistical Mechanics Social and Information Networks Mathematical Physics Mathematical Physics Probability

no code implementations • 23 Nov 2016 • Michael T. Schaub, Jean-Charles Delvenne, Martin Rosvall, Renaud Lambiotte

Community detection, the decomposition of a graph into essential building blocks, has been a core research topic in network science over the past years.

Social and Information Networks Data Analysis, Statistics and Probability Physics and Society

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