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Matthias Grossglauser

Cette personne a quitté l’EPFL

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Publications associées (58)

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Universal Lower Bounds and Optimal Rates: Achieving Minimax Clustering Error in Sub-Exponential Mixture Models

Patrick Thiran, Matthias Grossglauser, Maximilien Claude Robert Dreveton

Clustering is a pivotal challenge in unsupervised machine learning and is often investigated through the lens of mixture models. The optimal error rate for recovering cluster labels in Gaussian and sub-Gaussian mixture models involves ad hoc signal-to-nois ...

2024

[Re] Meta learning with differentiable closed-form solvers

Matthias Grossglauser, Arnout Jan J Devos, Sylvain Chatel

In this work, we present a reproduction of the paper of Bertinetto et al. ”Meta- learning with differentiable closed-form solvers” as part of the ICLR 2019 Reproducibility Challenge. In successfully reproducing the most crucial part of the paper, we reach ...

2019

Growing a Graph Matching from a Handful of Seeds

Matthias Grossglauser, Seyed Hamed Hassani, Ehsan Kazemi

In many graph–mining problems, two networks from different domains have to be matched. In the absence of reliable node attributes, graph matching has to rely on only the link structures of the two networks, which amounts to a generalization of the classic ...

Assoc Computing Machinery2015

Constrained Tracking on a Road Network

Matthias Grossglauser

Many applications of wireless ad hoc sensor and actuator networks (WSANs) rely on the knowledge of node locations. These are challenging to obtain when nodes are mobile and are not equipped with any expensive positioning hardware. In this paper, we are int ...

Springer Berlin / Heidelberg2006

Valuable Detours: Least-Cost Anypath Routing

Martin Vetterli, Matthias Grossglauser, Henri Julien Dubois-Ferrière

In many networks, it is less costly to transmit a packet to any node in a set of neighbors than to one specific neighbor. This observation was previously exploited by opportunistic routing protocols by using single-path routing metrics to assign to each no ...

2011

Trajectory sampling with unreliable reporting

Matthias Grossglauser

We define and evaluate methods to perform robust network monitoring using trajectory sampling in the presence of report loss. The first challenge is to reconstruct an unambiguous set of packet trajectories from the reports on sampled packets received at a ...

2008

TraNS: Realistic Joint Traffic and Network Simulator for VANETs

Jean-Pierre Hubaux, Matthias Grossglauser, Panagiotis Papadimitratos

Realistic simulation is a necessary tool for the proper evaluation of newly developed protocols for Vehicular Ad Hoc Networks (VANETs). Several recent efforts focus on achieving this goal. Yet, to this date, none of the proposed solutions fulfill all the r ...

2008

Learning Hawkes Processes Under Synchronization Noise

Patrick Thiran, Matthias Grossglauser, Negar Kiyavash, Seyed Jalal Etesami, William Trouleau

Multivariate Hawkes processes (MHP) are widely used in a variety of fields to model the occurrence of discrete events. Prior work on learning MHPs has only focused on inference in the presence of perfect traces without noise. We address the problem of lear ...

PMLR2019

Just Sort It! A Simple and Effective Approach to Active Preference Learning

Matthias Grossglauser, Lucas Maystre

We address the problem of learning a ranking by using adaptively chosen pairwise comparisons. Our goal is to recover the ranking accurately but to sample the comparisons sparingly. If all comparison outcomes are consistent with the ranking, the optimal sol ...

2017

Locating Mobile Nodes with EASE: Learning Efficient Routes from Encounter Histories Alone

Martin Vetterli, Matthias Grossglauser

Routing in large-scale mobile ad hoc networks is challenging because all the nodes are potentially moving. Geographic routing can partially alleviate this problem, as nodes can make local routing decisions based solely on the destinations' geographic coord ...

2006

Fast and Accurate Inference of Plackett-Luce Models

Matthias Grossglauser, Lucas Maystre

We show that the maximum-likelihood (ML) estimate of models derived from Luce’s choice axiom (e.g., the Plackett–Luce model) can be expressed as the stationary distribution of a Markov chain. This conveys insight into several recently proposed spectral inf ...

2015

Mitigating Epidemics through Mobile Micro-measures

Patrick Thiran, Matthias Grossglauser, Lucas Maystre, Mohamed Kafsi, Ehsan Kazemi, Lyudmila Yartseva

2013

On Clustering Phenomenon in Mobile Partitioned Networks

Matthias Grossglauser, Natasa Sarafijanovic-Djukic

According to different kinds of connectivity, we can distinguish three types of mobile ad-hoc networks: dense, sparse and clustered networks. This paper is about modeling mobility in clustered networks, where nodes are concentrated into clusters of dense c ...

ACM2008

On information transmission over a finite buffer channel

Suhas Diggavi, Matthias Grossglauser

2006

Routing in mobile wireless networks

Suhas Diggavi, Matthias Grossglauser

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2008

Island Hopping: Efficient Mobility-Assisted Forwarding in Partitioned Networks

Matthias Grossglauser, Natasa Sarafijanovic-Djukic

Mobile wireless ad hoc and sensor networks can be permanently partitioned in many interesting scenarios. This implies that instantaneous end-to-end routes do not exist. Nevertheless, when nodes are mobile, it is possible to forward messages to their destin ...

2006

Hierarchical Routing over Dynamic Wireless Networks

Suhas Diggavi, Matthias Grossglauser

2008

One-dimensional mobility increases capacity ofwireless adhoc networks

Suhas Diggavi, Matthias Grossglauser

2005