Matthias Grossglauser

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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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

Matthias Grossglauser

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[Re] Meta learning with differentiable closed-form solvers

Growing a Graph Matching from a Handful of Seeds

Constrained Tracking on a Road Network

Valuable Detours: Least-Cost Anypath Routing

Trajectory sampling with unreliable reporting

TraNS: Realistic Joint Traffic and Network Simulator for VANETs

Learning Hawkes Processes Under Synchronization Noise

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

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

Fast and Accurate Inference of Plackett-Luce Models

Mitigating Epidemics through Mobile Micro-measures

On Clustering Phenomenon in Mobile Partitioned Networks

On information transmission over a finite buffer channel

Routing in mobile wireless networks

Island Hopping: Efficient Mobility-Assisted Forwarding in Partitioned Networks

Hierarchical Routing over Dynamic Wireless Networks

One-dimensional mobility increases capacity ofwireless adhoc networks