no code implementations • 6 Jun 2024 • Maximilien Dreveton, Charbel Chucri, Matthias Grossglauser, Patrick Thiran
In this work, we analyze the metric backbone of a broad class of weighted random graphs with communities, and we formally prove the robustness of the community structure with respect to the deletion of all the edges that are not in the metric backbone.
no code implementations • 23 May 2024 • Amir Mohammad Abouei, Ehsan Mokhtarian, Negar Kiyavash, Matthias Grossglauser
The s-ID problem seeks to compute a causal effect in a specific sub-population from the observational data pertaining to the same sub population (Abouei et al., 2023).
no code implementations • 23 Feb 2024 • Maximilien Dreveton, Alperen Gözeten, Matthias Grossglauser, Patrick Thiran
In such mixtures, we establish that Bregman hard clustering, a variant of Lloyd's algorithm employing a Bregman divergence, is rate optimal.
no code implementations • 15 Nov 2023 • Sadegh Khorasani, Saber Salehkaleybar, Negar Kiyavash, Niao He, Matthias Grossglauser
Policy gradient (PG) is widely used in reinforcement learning due to its scalability and good performance.
no code implementations • 20 Sep 2023 • Aswin Suresh, Lazar Radojevic, Francesco Salvi, Antoine Magron, Victor Kristof, Matthias Grossglauser
In the absence of a ground-truth dataset of such links, we perform an indirect validation by comparing the discovered links with a dataset, which we curate, of retweet links between MEPs and lobbies, and with the publicly disclosed meetings of MEPs.
no code implementations • 16 Jul 2023 • Aswin Suresh, Chi-Hsuan Wu, Matthias Grossglauser
In each case, we demonstrate that the outputs of the model can be explained and validated, even for the two domains that are outside the training-data domain.
no code implementations • 2 Jun 2023 • Daniyar Chumbalov, Lars Klein, Lucas Maystre, Matthias Grossglauser
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 $t$?''
no code implementations • 1 Jun 2023 • Maximilien Dreveton, Daichi Kuroda, Matthias Grossglauser, Patrick Thiran
We also establish that this bottom-up algorithm attains the information-theoretic threshold for exact recovery at intermediate levels of the hierarchy.
2 code implementations • 19 Jun 2020 • Carlos Medina, Arnout Devos, Matthias Grossglauser
Building on these insights and on advances in self-supervised learning, we propose a transfer learning approach which constructs a metric embedding that clusters unlabeled prototypical samples and their augmentations closely together.
no code implementations • 26 Nov 2019 • Victor Kristof, Valentin Quelquejay-Leclère, Robin Zbinden, Lucas Maystre, Matthias Grossglauser, Patrick Thiran
We propose a statistical model to understand people's perception of their carbon footprint.
1 code implementation • NeurIPS 2019 • Farnood Salehi, William Trouleau, Matthias Grossglauser, Patrick Thiran
It is also able to take into account the uncertainty in the model parameters by learning a posterior distribution over them.
1 code implementation • 31 May 2019 • Arnout Devos, Matthias Grossglauser
We propose regression networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each class.
no code implementations • ICML 2020 • Daniyar Chumbalov, Lucas Maystre, Matthias Grossglauser
We consider the problem of finding a target object $t$ using pairwise comparisons, by asking an oracle questions of the form \emph{"Which object from the pair $(i, j)$ is more similar to $t$?"}.
2 code implementations • 18 Mar 2019 • Lucas Maystre, Victor Kristof, Matthias Grossglauser
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.
no code implementations • 25 Apr 2018 • Osman Emre Dai, Daniel Cullina, Negar Kiyavash, Matthias Grossglauser
Graph alignment in two correlated random graphs refers to the task of identifying the correspondence between vertex sets of the graphs.
1 code implementation • 12 Jan 2018 • Ali Batuhan Yardım, Victor Kristof, Lucas Maystre, Matthias Grossglauser
As the number of contributors to online peer-production systems grows, it becomes increasingly important to predict whether the edits that users make will eventually be beneficial to the project.
no code implementations • ICML 2017 • Lucas Maystre, Matthias Grossglauser
We consider a setting where only aggregate node-level traffic is observed and tackle the task of learning edge transition probabilities.
no code implementations • 5 Sep 2016 • Lucas Maystre, Victor Kristof, Antonio J. González Ferrer, Matthias Grossglauser
In this work, we draw attention to a connection between skill-based models of game outcomes and Gaussian process classification models.
no code implementations • NeurIPS 2015 • Lucas Maystre, Matthias Grossglauser
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.
no code implementations • ICML 2017 • Lucas Maystre, Matthias Grossglauser
We address the problem of learning a ranking by using adaptively chosen pairwise comparisons.
1 code implementation • 12 Dec 2012 • Mohamed Kafsi, Matthias Grossglauser, Patrick Thiran
To quantify the randomness of Markov trajectories with fixed initial and final states, Ekroot and Cover proposed a closed-form expression for the entropy of trajectories of an irreducible finite state Markov chain.
Information Theory Information Theory Applications