Hi, bonjour, yeia

Welcome to my personal web-page. I am Argyris Kalogeratos (Αργύρης Καλογεράτος), I come from Patras, Greece and my background is on Computer Science. My PhD was on the field of Machine Learning (Dept. of Computer Science and Engineering, University of Ioannina, Greece – 2013), under the supervision of Aristidis Likas.

Currently

Position: I am a permanent researcher at the Centre de Mathématiques et de Leurs Applications (CMLA), Ecole Normale Supérieure Paris-Saclay (ENS Paris Saclay), France, and member of the Machine Learning and Massive Data Analysis (MLMDA) research group (head: Nicolas Vayatis).

Research focus: Principal researcher on ML on Graphs.

Research initiatives: I participate to the research activities of the ML Industrial Big Data Chair (CMLA team: Julien Audiffren, Mathilde Mougeot, Nicolas Vayatis). Among our industrial partners, there are ATOSWordline, SNCF, CEA, Michelin, Bertin Tech.

 

 

Past funding: My research position at CMLA has been funded in the past by the MORANE project (in collaboration with SNCF railway company) and the SODATECH project  (funded by the French state) for which I was the research coordinator and responsible for CMLA’s part. See more details at the “Projects” section.

Research opportunities

Contact me or another member of our ML group to know more about opportunities for join us at CMLA for an internship or a thesis.

News

  • ICTAI 2019 – “Optimal Multiple Stopping Rule for Warm-Starting Sequential Selection, co-authored with Mathilde Fekom and Nicolas Vayatis, will appear at the IEEE International Conference on Tools with Artificial Intelligence (ICTAI), to be held at Portland, Oregon, US, on Nov. 4 – 6, 2019. [pdf available soon]
  • CDC 2019 – “Sequential Dynamic Resource Allocation for Epidemic Control, co-authored with Mathilde Fekom and Nicolas Vayatis, will appear at the IEEE Conference on Decision and Control 2019 (CDC), to be held at Nice, France, on Dec. 11 – 13, 2019. [pdf available soon]
  • TechReport – A new paper was released, as technical report, entitled
    “Revealing posturographic features associated with the risk of falling in patients with Parkinsonian syndromes via machine learning”. The paper is co-authored with I. Bargiotas, M. Limnios, P.–P. Vidal, D. Ricard, and N. Vayatis, July 2018. [pdf][arXiv link] Abstract

    Falling in Parkinsonian syndromes (PS) is associated with postural instability and consists a common cause of disability among PS patients. Current posturographic practices record the body’s center-of-pressure displacement (statokinesigram) while the patient stands on a force platform. Statokinesigrams, after appropriate signal processing, can offer numerous posturographic features, which however challenges the efforts for valid statistics via standard univariate approaches. In this work, we present the ts-AUC, a non-parametric multivariate two-sample test, which we employ to analyze statokinesigram differences among PS patients that are fallers (PSf) and non-fallers (PSNF). We included 123 PS patients who were classified into PSF or PSNF based on clinical assessment and underwent simple Romberg Test (eyes open/eyes closed). We analyzed posturographic features using both multiple testing with p-value adjustment and the ts-AUC. While the ts-AUC showed significant difference between groups (p-value = 0.01), multiple testing did not show any such difference. Interestingly, significant difference between the two groups was found only using the open-eyes protocol. PSF showed significantly increased antero-posterior movements as well as increased posturographic area, compared to PSNF. Our study demonstrates the superiority of the ts-AUC test compared to standard statistical tools in distinguishing PSF and PSNF in the multidimensional feature space. This result highlights more generally the fact that machine learning-based statistical tests can be seen as a natural extension of classical statistical approaches and should be considered, especially when dealing with multifactorial assessments.

  • Workshop Talk – Mathilde Fekom will present our work (joint work with me and N. Vayatis) on the “Warm-starting Sequential Selection Problem at the 7th International Workshop in Sequential Methodologies 2019 (IWSM), Jun 18 – 21, 2019, State University of New York at Binghamton, US.
  • New PhD student: Congratulations to Alejandro David De La Concha Duarte, who is currently doing his MVA Master internship with us, and has been selected as MathInnov PhD laureate 2019-2022 to do his PhD on our research proposal on Machine Learning on Networks (supervisors: A. Kalogeratos, N. Vayatis).
  • Dataset release: The Sigfox dataset is released, containing communication activity recorded in a real IoT network.
  • New interns:
    • Alejandro de la Concha (M2 MVA, Apr – Sep) will work on multivariate signal segmentation.
    • Anthea Mérida (M2, Apr – Oct) will work on ensemble methods and random forests.
    • Amel Addala (M2, Mar – Aug) will work on a problem related to EDF’s power network.
    • Rafael Gromit and Blandine Galiay (L3 ENS Paris-Saclay, Apr – Jul) will work on sequential selection processes over graphs.
    • Vincent Laheurte and Ferdinand Campos (L3 ENS Paris-Saclay, Apr – Jul) will work on statistical homogeneity testing.
  • ICASSP 2019Learning Laplacian Matrix from Bandlimited Graph Signals, co-authored with Batiste Le Bars, Pierre Humbert, and Laurent Oudre, will appear in the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 12-17 May, 2019, Brighton, UK. [pdf available soon]
  • INFOCOM 2019A Probabilistic Framework to Node-level Anomaly Detection in Communication Networks, co-authored with Batiste Le Bars, will appear in the IEEE International Conference on Computer Communications 2019 (INFOCOM), 29 Apr – 2 May 2019, Paris, France. [288 papers accepted / 1464 submissions: 19.7% rate] [pdf][bib] Abstract

    In this paper we consider the task of detecting abnormal communication volume occurring at node-level in communication networks. The signal of the communication activity is modeled by means of a clique stream: each occurring communication event is instantaneous, and activates an undirected subgraph spanning over a set of equally participating nodes. We present a probabilistic framework to model and assess the communication volume observed at any single node. Specifically, we employ non-parametric regression to learn the probability that a node takes part in a certain event knowing the set of other nodes that are involved. On the top of that, we present a concentration inequality around the estimated volume of events in which a node could participate, which in turn allows us to build an efficient and interpretable anomaly scoring function. Finally, the superior performance of the proposed approach is empirically demonstrated in real-world sensor network data, as well as using synthetic communication activity that is in accordance with that latter setting.

  • TechReport – A new paper was released, as Technical Report, introducing and analyzing the “The Multi-Round Sequential Selection Problem. It is co-authored with M. Fekom, and N. Vayatis, September 2018. [pdf][arXiv link] Abstract

    In the Sequential Selection Problem (SSP), immediate and irrevocable decisions need to be made while candidates from a finite set are being examined one-by-one. The goal is to assign a limited number of b available jobs to the best possible candidates. Standard SSP variants begin with an empty selection set (cold-starting) and perform the selection process once (single-round), over a single candidate set. In this paper we introduce the Multi-round Sequential Selection Problem (MSSP) which launches a new round of sequential selection each time a new set of candidates becomes available. Each new round has at hand the output of the previous one, i.e. its b selected employees, and tries to update optimally that selection by reassigning each job at most once. Our setting allows changes to take place between two subsequent selection rounds: resignations of previously selected subjects or/and alterations of the quality score across the population. The challenge for a selection strategy is thus to efficiently adapt to such changes. For this novel problem we adopt a cutoff-based approach, where a precise number of candidates should be rejected first before starting to select. We set a rank-based objective of the process over the final job-to-employee assignment and we investigate analytically the optimal cutoff values with respect to the important parameters of the problem. Finally, we present experimental results that compare the efficiency of different selection strategies, as well as their convergence rates towards the optimal solution in the case of stationary score distributions.

  • Book ChapterInformation Diffusion and Rumor Spreading, A. Kalogeratos, K. Scaman, L. Corinzia, and N. Vayatis, chapter in the book Cooperative and Graph Signal Processing – Principles and Applications, eds. P.M. Djuric and C. Richard, Elsevier, 2018. [pdf][publisher link][bib] Abstract

    This chapter studies information cascades on social networks with a special focus on types of diffusion processes such as rumors and false news. The complex temporal dynamics of information cascades and rapid changes in user interests require flexible mathematical modeling to properly describe the diffusion dynamics. After mentioning the modeling advancements of recent decades, we get to modern models, such as the Information Cascade Model (ICM), that are indeed capable of describing such time-dependent user interests and are thus particularly suited to the analysis of information diffusion. We provide a theoretical analysis of ICM, relating the dynamics of the cascade to structural characteristics of the social network, and then use that analysis to design control policies capable of efficiently reducing the undesired diffusion. The presented framework for activity shaping is generic while enjoying a simple convex relaxation. Finally, we present an algorithm for the control of Continuous-Time Independent Cascades which is evaluated and compared against baseline and state-of-the art approaches through diffusion simulations on real and synthetic social networks.

  • TechReport – A short paper with preliminary results, entitled “Node-level Anomaly Detection in Communication Networks“, co-authored with Batiste Le Bars, will be presented on June at the 3rd Graph Signal Processing Workshop (GSP), 2018. Abstract

    In this paper we consider the task of the detection of abnormal communication volume, occurring at node-level in a communication network. We model the communication by means of a dynamic graph with edges related to an occurring communication event appearing instantaneously. We propose a statistical model for the communication volume recorded at a single node, in order to detect anomalies. Our approach is evaluated in real-world communication data.

  • Workshop Poster – part of our work with Mathilde Fekom on sequential selection processes will be presented as a poster will be presented at the Workshop on Multi-Armed Bandits and Learning Algorithms.
  • Workshop Talk – On the French German summer school on Transfer Learning, June 4-6 2018, ENS-Paris-Saclay.
  • New intern – Thales Loiola Raveli will be working on the modeling of transportation traffic (May – Aug 2018).
  • TechReport – A new paper was released as Technical Report. Its title is “A Spectral Method for Activity Shaping in Continuous-Time Information Cascades” and it is co-authored with K. Scaman, L. Corinzia, and N. Vayatis, Sep 15, 2017. [pdf][arXiv link] Abstract

    The Information Cascades Model captures dynamical properties of user activity in a social network. In this work, we develop a novel framework for activity shaping under the Continuous-Time Information Cascades Model which allows the administrator for local control actions by allocating targeted resources that can alter the spread of the process. Our framework employs the optimization of the spectral radius of the Hazard matrix, a quantity that has been shown to drive the maximum influence in a network, while enjoying a simple convex relaxation when used to minimize the influence of the cascade. In addition, use-cases such as quarantine and node immunization are discussed to highlight the generality of the proposed activity shaping framework. Finally, we present the NetShape influence minimization method which is compared favorably to baseline and state-of-the-art approaches through simulations on real social networks.

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