1 code implementation • 15 Dec 2023 • Waïss Azizian, Guillaume Baudart, Marc Lelarge

Exact Bayesian inference on state-space models~(SSM) is in general untractable, and unfortunately, basic Sequential Monte Carlo~(SMC) methods do not yield correct approximations for complex models.

no code implementations • 1 Dec 2023 • Matthieu Blanke, Marc Lelarge

Machine learning methods can be a valuable aid in the scientific process, but they need to face challenging settings where data come from inhomogeneous experimental conditions.

1 code implementation • 26 Apr 2023 • Matthieu Blanke, Marc Lelarge

Model-based reinforcement learning is a powerful tool, but collecting data to fit an accurate model of the system can be costly.

no code implementations • 19 Jan 2023 • David A. R. Robin, Kevin Scaman, Marc Lelarge

In this paper, we present a new strategy to prove the convergence of deep learning architectures to a zero training (or even testing) loss by gradient flow.

1 code implementation • 13 Apr 2022 • Matthieu Blanke, Marc Lelarge

This work addresses the problem of exploration in an unknown environment.

1 code implementation • 18 Mar 2022 • Eric Daoud, Luca Ganassali, Antoine Baker, Marc Lelarge

In these applications, there is somewhat of an asymmetry between users and items: items are viewed as static points, their embeddings, capacities and locations constraining the allocation.

1 code implementation • 15 Jul 2021 • Luca Ganassali, Laurent Massoulié, Marc Lelarge

We then conjecture that graph alignment is not feasible in polynomial time when the associated tree detection problem is impossible.

no code implementations • 4 Feb 2021 • Luca Ganassali, Laurent Massoulié, Marc Lelarge

Random graph alignment refers to recovering the underlying vertex correspondence between two random graphs with correlated edges.

no code implementations • 1 Jan 2021 • Alexis Galland, Marc Lelarge

Compared to the case of images, it is not trivial to develop a pooling layer on graphs.

no code implementations • 1 Jan 2021 • Alexis Galland, Marc Lelarge

In order to use graph neural networks for graph classification, node embeddings must be aggregated to obtain a graph representation able to discriminate among different graphs (of possibly various sizes).

1 code implementation • 3 Nov 2020 • Stéphane d'Ascoli, Alice Coucke, Francesco Caltagirone, Alexandre Caulier, Marc Lelarge

Scarcity of training data for task-oriented dialogue systems is a well known problem that is usually tackled with costly and time-consuming manual data annotation.

1 code implementation • ICLR 2021 • Waïss Azizian, Marc Lelarge

Various classes of Graph Neural Networks (GNN) have been proposed and shown to be successful in a wide range of applications with graph structured data.

1 code implementation • 9 Nov 2019 • Stéphane d'Ascoli, Alice Coucke, Francesco Caltagirone, Alexandre Caulier, Marc Lelarge

Scarcity of training data for task-oriented dialogue systems is a well known problem that is usually tackled with costly and time-consuming manual data annotation.

1 code implementation • ICML 2019 • Alexis Galland, Marc Lelarge

The graph isomorphism problem tells us that fast representation of graphs is known if we require the representation to be both invariant to nodes permutation and able to discriminate two-isomorphic graphs.

no code implementations • 8 Jul 2019 • Marc Lelarge, Leo Miolane

In this paper, we compute analytically the gap between the best fully-supervised approach using only labeled data and the best semi-supervised approach using both labeled and unlabeled data.

1 code implementation • 28 Sep 2018 • Thomas Bonald, Alexandre Hollocou, Marc Lelarge

We present a novel spectral embedding of graphs that incorporates weights assigned to the nodes, quantifying their relative importance.

1 code implementation • 20 Jun 2018 • Edouard Pineau, Marc Lelarge

This paper describes InfoCatVAE, an extension of the variational autoencoder that enables unsupervised disentangled representation learning.

no code implementations • 26 Mar 2018 • Jean-Baptiste Escudié, Alaa Saade, Alice Coucke, Marc Lelarge

We show how to learn low-dimensional representations (embeddings) of patient visits from the corresponding electronic health record (EHR) where International Classification of Diseases (ICD) diagnosis codes are removed.

1 code implementation • 9 Dec 2017 • Alexandre Hollocou, Julien Maudet, Thomas Bonald, Marc Lelarge

We introduce a novel algorithm to perform graph clustering in the edge streaming setting.

1 code implementation • 27 Oct 2016 • Alexandre Hollocou, Thomas Bonald, Marc Lelarge

Community detection is a classical problem in the field of graph mining.

Social and Information Networks Physics and Society

no code implementations • 8 Sep 2016 • Lennart Gulikers, Marc Lelarge, Laurent Massoulié

As a result, a clustering positively-correlated with the true communities can be obtained based on the second eigenvector of $B$ in the regime where $\mu_2^2 > \rho.$ In a previous work we obtained that detection is impossible when $\mu_2^2 < \rho,$ meaning that there occurs a phase-transition in the sparse regime of the Degree-Corrected Stochastic Block Model.

no code implementations • 20 May 2016 • Alaa Saade, Florent Krzakala, Marc Lelarge, Lenka Zdeborová

We consider the problem of clustering partially labeled data from a minimal number of randomly chosen pairwise comparisons between the items.

no code implementations • 25 Jan 2016 • Alaa Saade, Marc Lelarge, Florent Krzakala, Lenka Zdeborová

We consider the problem of grouping items into clusters based on few random pairwise comparisons between the items.

no code implementations • NeurIPS 2015 • Se-Young Yun, Marc Lelarge, Alexandre Proutiere

This means that its average mean-square error converges to 0 as $m$ and $n$ grow large (i. e., $\|\hat{M}^{(k)}-M^{(k)} \|_F^2 = o(mn)$ with high probability, where $\hat{M}^{(k)}$ and $M^{(k)}$ denote the output of SLA and the optimal rank $k$ approximation of $M$, respectively).

no code implementations • 2 Nov 2015 • Lennart Gulikers, Marc Lelarge, Laurent Massoulié

We consider the Degree-Corrected Stochastic Block Model (DC-SBM): a random graph on $n$ nodes, having i. i. d.

no code implementations • 29 Jun 2015 • Lennart Gulikers, Marc Lelarge, Laurent Massoulié

In particular, it does not need to know the number of communities.

no code implementations • 12 Jun 2015 • Emilie Kaufmann, Thomas Bonald, Marc Lelarge

This paper presents a novel spectral algorithm with additive clustering designed to identify overlapping communities in networks.

no code implementations • 13 Apr 2015 • Se-Young Yun, Marc Lelarge, Alexandre Proutiere

We propose a streaming algorithm which produces an estimate of the original matrix with a vanishing mean square error, uses memory space scaling linearly with the ambient dimension of the matrix, i. e. the memory required to store the output alone, and spends computations as much as the number of non-zero entries of the input matrix.

no code implementations • 16 Feb 2015 • Rui Wu, Jiaming Xu, R. Srikant, Laurent Massoulié, Marc Lelarge, Bruce Hajek

We propose an efficient algorithm that accurately estimates the individual preferences for almost all users, if there are $r \max \{m, n\}\log m \log^2 n$ pairwise comparisons per type, which is near optimal in sample complexity when $r$ only grows logarithmically with $m$ or $n$.

no code implementations • 11 Feb 2015 • Marc Lelarge, Laurent Massoulié, Jiaming Xu

The labeled stochastic block model is a random graph model representing networks with community structure and interactions of multiple types.

1 code implementation • NeurIPS 2015 • Richard Combes, M. Sadegh Talebi, Alexandre Proutiere, Marc Lelarge

In the adversarial setting under bandit feedback, we propose \textsc{CombEXP}, an algorithm with the same regret scaling as state-of-the-art algorithms, but with lower computational complexity for some combinatorial problems.

no code implementations • 31 Jan 2015 • Alaa Saade, Florent Krzakala, Marc Lelarge, Lenka Zdeborová

We describe two spectral algorithms for this task based on the non-backtracking and the Bethe Hessian operators.

no code implementations • NeurIPS 2014 • Se-Young Yun, Marc Lelarge, Alexandre Proutiere

The first algorithm is {\it offline}, as it needs to store and keep the assignments of nodes to clusters, and requires a memory that scales linearly with the network size.

no code implementations • 26 Jun 2014 • Jiaming Xu, Laurent Massoulié, Marc Lelarge

The classical setting of community detection consists of networks exhibiting a clustered structure.

no code implementations • 27 Feb 2013 • Marc Lelarge, Alexandre Proutiere, M. Sadegh Talebi

We consider the problem of allocating radio channels to links in a wireless network.

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