9 citations · 25 across the 39 of their papers we have counts for
45 papers
On the Calibration of Crowdsourcing Aggregators
Gabriel Singer, Reda Arab, Maïssa Frikha +4
Calibration has been extensively studied for classifiers, but remains largely unexplored for crowdsourcing aggregation methods, which are increasingly used for making probabilistic…
polyview: A Python package for multi-view machine learning
Gwendal Debaussart-Joniec, Argyris Kalogeratos
Multi-view learning jointly exploits multiple complementary representations of the same data and has become increasingly important in machine learning. However, the Python ecosyste…
A Finslerian Approach for Embedding Directed Data
Gwendal Debaussart-Joniec, Théau Blanchard, Argyris Kalogeratos
Many datasets carry an intrinsic directionality: citations point backward in time, cells differentiate along lineages, and traffic follows preferred routes. Spectral embedding meth…
GraphToolbox: A Configurable Python Framework for Graph Neural Network Forecasting
Eloi Campagne, Yvenn Amara-Ouali, Yannig Goude +1
Electricity forecasting often involves spatially related signals observed over regions, substations, and feeders, and Graph Neural Networks (GNNs) provide a natural way to represen…
Algorithmic collusion under asynchronous price updating
Ivan Conjeaud, Gaspard Abel, Argyris Kalogeratos
This paper investigates the effect of asynchrony in agents' updates in the emergence of algorithmic collusion. We present a continuous-time model for algorithmic collusion in which…
A Model for Imbalanced Label Aggregation: A Focus on Minority-Class Detection
Gabriel Singer, Samuel Gruffaz, Olivier Vo Van +2
We study imbalanced crowdsourcing with a focus on class-dependent annotator accuracy, a setting that, to the best of our knowledge, remains relatively underexplored despite its imp…
GLOBe: A Modular Global Optimization library
Gaëtan Serré, Argyris Kalogeratos, Nicolas Vayatis
Open-source libraries are have a catalytic role in research pipelines, where new methods must be compared against up-to-date baselines. We present the GLobal Optimization Benchmark…
Dual-Criterion Curriculum Learning: Application to Temporal Data
Gaspard Abel, Eloi Campagne, Mohamed Benloughmari +1
Curriculum Learning (CL) is a meta-learning paradigm that trains a model by feeding the data instances incrementally according to a schedule, which is based on difficulty progressi…
Enhancing LIME using Neural Decision Trees
Mohamed Aymen Bouyahia, Argyris Kalogeratos
Interpreting complex machine learning models is a critical challenge, especially for tabular data where model transparency is paramount. Local Interpretable Model-Agnostic Explanat…
Enhancing Exploration in Global Optimization by Noise Injection in the Probability Measures Space
Gaëtan Serré, Pierre Germain, Samuel Gruffaz +1
McKean-Vlasov (MKV) systems provide a unifying framework for recent state-of-the-art particlebased methods for global optimization. While individual particles follow stochastic tra…
Optimal Fair Aggregation of Crowdsourced Noisy Labels using Demographic Parity Constraints
Gabriel Singer, Samuel Gruffaz, Olivier Vo Van +2
As acquiring reliable ground-truth labels is usually costly, or infeasible, crowdsourcing and aggregation of noisy human annotations is the typical resort. Aggregating subjective l…
Cascaded Transfer: Learning Many Tasks under Budget Constraints
Eloi Campagne, Yvenn Amara-Ouali, Yannig Goude +2
In distributed applications, such as energy demand forecasting at the substation level or federated learning, a large number of related tasks must be learned by different models, w…
Multi-view diffusion geometry using intertwined diffusion trajectories
Gwendal Debaussart-Joniec, Argyris Kalogeratos
This paper introduces a comprehensive unified framework for constructing multi-view diffusion geometries through intertwined multi-view diffusion trajectories (MDTs), a class of in…
A Unifying Framework for Global Optimization: From Theory to Formalization
Gaëtan Serré, Argyris Kalogeratos, Nicolas Vayatis
We introduce an abstract measure___theoretic framework that serves as a tool to rigorously study stochastic iterative global optimization algorithms as a unified class. The framewo…
Graph Neural Networks for Electricity Load Forecasting
Eloi Campagne, Yvenn Amara-Ouali, Yannig Goude +2
Forecasting electricity demand is increasingly challenging as energy systems become more decentralized and intertwined with renewable sources. Graph Neural Networks (GNNs) have rec…
Uncovering Social Network Activity Using Joint User and Topic Interaction
Gaspard Abel, Argyris Kalogeratos, Jean-Pierre Nadal +1
The emergence of online social platforms, such as social networks and social media, has drastically affected the way people apprehend the information flows to which they are expose…
Leveraging Graph Neural Networks to Forecast Electricity Consumption
Eloi Campagne, Yvenn Amara-Ouali, Yannig Goude +1
Accurate electricity demand forecasting is essential for several reasons, especially as the integration of renewable energy sources and the transition to a decentralized network pa…
LIPO+: Frugal Global Optimization for Lipschitz Functions
Gaëtan Serré, Perceval Beja-Battais, Sophia Chirrane +2
In this paper, we propose simple yet effective empirical improvements to the algorithms of the LIPO family, introduced in [Malherbe2017], that we call LIPO+ and AdaLIPO+. We compar…
Collaborative non-parametric two-sample testing
Alejandro de la Concha, Nicolas Vayatis, Argyris Kalogeratos
This paper addresses the multiple two-sample test problem in a graph-structured setting, which is a common scenario in fields such as Spatial Statistics and Neuroscience. Each node…
Stein Boltzmann Sampling: A Variational Approach for Global Optimization
Gaëtan Serré, Argyris Kalogeratos, Nicolas Vayatis
In this paper, we present a flow-based method for global optimization of continuous Sobolev functions, called Stein Boltzmann Sampling (SBS). SBS initializes uniformly a number of…
UniForCE: The Unimodality Forest Method for Clustering and Estimation of the Number of Clusters
Georgios Vardakas, Argyris Kalogeratos, Aristidis Likas
Estimating the number of clusters k while clustering the data is a challenging task. An incorrect cluster assumption indicates that the number of clusters k gets wrongly estimated.…
Online non-parametric likelihood-ratio estimation by Pearson-divergence functional minimization
Alejandro de la Concha, Nicolas Vayatis, Argyris Kalogeratos
Quantifying the difference between two probability density functions, and , using available data, is a fundamental problem in Statistics and Machine Learning. A usual approa…
A framework for paired-sample hypothesis testing for high-dimensional data
Ioannis Bargiotas, Argyris Kalogeratos, Nicolas Vayatis
The standard paired-sample testing approach in the multidimensional setting applies multiple univariate tests on the individual features, followed by p-value adjustments. Such an a…
Online Centralized Non-parametric Change-point Detection via Graph-based Likelihood-ratio Estimation
Alejandro de la Concha, Argyris Kalogeratos, Nicolas Vayatis
Consider each node of a graph to be generating a data stream that is synchronized and observed at near real-time. At a change-point , a change occurs at a subset of nodes , w…