Publications by Matthias Grossglauser

Discovering Lobby-Parliamentarian Alignments through NLP

Matthias Grossglauser, Victor Kristof, Aswin Suresh

We discover alignments of views between interest groups (lobbies) and members of the European Parliament (MEPs) by automatically analyzing their texts. Specifically, we do so by collecting novel datasets of lobbies’ position papers and MEPs’ speeches, and ...

2024

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

Why the Metric Backbone Preserves Community Structure

Patrick Thiran, Matthias Grossglauser, Maximilien Claude Robert Dreveton

The metric backbone of a weighted graph is the union of all-pairs shortest paths. It is obtained by removing all edges

(u,v)

that are not the shortest path between

u

and

v

. In networks with well-separated communities, the metric backbone tends to pre ...

2024

Fast Interactive Search under a Scale-Free Comparison Oracle

Matthias Grossglauser, Lucas Maystre, Daniyar Chumbalov

A comparison-based search algorithm lets a user find a target item t in a database by answering queries of the form, "Which of items i and j is closer to t?" Instead of formulating an explicit query (such as one or several keywords), the user navigates tow ...

2024

It’s All Relative: Learning Interpretable Models for Scoring Subjective Bias in Documents from Pairwise Comparisons

Matthias Grossglauser, Aswin Suresh, Chi Hsuan Wu

We propose an interpretable model to score the subjective bias present in documents, based only on their textual content. Our model is trained on pairs of revisions of the same Wikipedia article, where one version is more biased than the other. Although pr ...

2024

Mining Effective Strategies for Climate Change Communication

Matthias Grossglauser, Aswin Suresh, Yurui Zhu, Lazar Milikic

With the goal of understanding effective strategies to communicate about climate change, we build interpretable models to rank tweets related to climate change with respect to the engagement they generate. Our models are based on the Bradley-Terry model of ...

2023

Cumulants of Hawkes Processes are Robust to Observation Noise

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

Multivariate Hawkes processes (MHPs) are widely used in a variety of fields to model the occurrence of causally related discrete events in continuous time. Most state-of-the-art approaches address the problem of learning MHPs from perfect traces without no ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2021

A Variational Inference Approach to Learning Multivariate Wold Processes

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

Temporal point-processes are often used for mathematical modeling of sequences of discrete events with asynchronous timestamps. We focus on a class of temporal point-process models called multivariate Wold processes (MWP). These processes are well suited t ...

MICROTOME PUBLISHING2021

War of Words II: Enriched Models of Law-Making Processes

Patrick Thiran, Matthias Grossglauser, Victor Kristof, Aswin Suresh

The European Union law-making process is an instance of a peer- production system. We mine a rich dataset of law edits and intro- duce models predicting their adoption by parliamentary committees. Edits are proposed by parliamentarians, and they can be in ...

ACM2021

Self-Supervised Prototypical Transfer Learning for Few-Shot Classification

Matthias Grossglauser, Arnout Jan J Devos, Carlos Roberto Medina Temme

Recent advances in transfer learning and few-shot learning largely rely on annotated data related to the goal task during (pre-)training. However, collecting sufficiently similar and annotated data is often infeasible. Building on advances in self-supervis ...

2020

Scalable and Efficient Comparison-based Search without Features

Matthias Grossglauser, Lucas Maystre, Daniyar Chumbalov

We consider the problem of finding a target object t using pairwise comparisons, by asking an oracle questions of the form “Which object from the pair (i, j) is more similar to t?”. Objects live in a space of latent features, from which the oracle generate ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2020

War of Words: The Competitive Dynamics of Legislative Processes

Patrick Thiran, Matthias Grossglauser, Victor Kristof

A body of law is an example of a dynamic corpus of text documents that are jointly maintained by a group of editors who compete and collaborate in complex constellations. Our goal is to develop predictive models for this process, thereby shedding light on ...

ACM / IW3C2 (International World Wide Web Conference Committee)2020

MPGM: Scalable and Accurate Multiple Network Alignment

Matthias Grossglauser, Ehsan Kazemi

Protein-protein interaction (PPI) network alignment is a canonical operation to transfer biological knowledge among species. The alignment of PPI-networks has many applications, such as the prediction of protein function, detection of conserved network mot ...

IEEE COMPUTER SOC2020

Sub-Matrix Factorization for Real-Time Vote Prediction

Patrick Thiran, Matthias Grossglauser, Victor Kristof

We address the problem of predicting aggregate vote outcomes (e.g., national) from partial outcomes (e.g., regional) that are revealed sequentially. We combine matrix factorization techniques and generalized linear models (GLMs) to obtain a flexible, effic ...

2020

Pairwise Comparisons with Flexible Time-Dynamics

Matthias Grossglauser, Lucas Maystre, Victor Kristof

Inspired by applications in sports where the skill of players or teams competing against each other varies over time, we propose a probabilistic model of pairwise-comparison outcomes that can capture a wide range of time dynamics. We achieve this by replac ...

ASSOC COMPUTING MACHINERY2019

[Re] Meta learning with differentiable closed-form solvers

Matthias Grossglauser, Sylvain Chatel, Arnout Jan J Devos

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

Learning Hawkes Processes Under Synchronization Noise

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

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

Analysis of a Canonical Labeling Algorithm for the Alignment of Correlated Erdős-Rényi Graphs

Matthias Grossglauser, Osman Emre Dai, Negar Kiyavash

Graph alignment in two correlated random graphs refers to the task of identifying the correspondence between vertex sets of the graphs. Recent results have characterized the exact information-theoretic threshold for graph alignment in correlated Erdös-Rény ...

Association for Computing Machinery (ACM)2019

Learning Hawkes Processes from a Handful of Events

Patrick Thiran, Matthias Grossglauser, William Trouleau, Farnood Salehi

Learning the causal-interaction network of multivariate Hawkes processes is a useful task in many applications. Maximum-likelihood estimation is the most common approach to solve the problem in the presence of long observation sequences. However, when only ...

2019

A User Study of Perceived Carbon Footprint

Patrick Thiran, Matthias Grossglauser, Lucas Maystre, Victor Kristof, Valentin Quelquejay-Leclère, Robin Adrien Zbinden

We propose a statistical model to understand people's perception of their carbon footprint. Driven by the observation that few people think of CO2 impact in absolute terms, we design a system to probe people's perception from simple pairwise comparisons of ...

2019