- 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 2019 – “Learning 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 2019 – “A 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]
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]
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 Chapter – Information 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]
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]
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.
- New PhD student – Mathilde Fekom, previously a Master student at CMLA, will continue with us to the PhD adventure (starting Sep. 2017), after obtaining a highly competitive scholarship from Université Paris Saclay.
- New students – We welcome three new students in the ML on Networks theme of MLMDA group, Mathilde Fekom (MSc thesis), Matteo Neri (MSc thesis), and Marin Scalbert (MSc internship), that are going to be with us at CMLA this semester.
- Invited Talk – A two-hours seminar talk on “Epidemics, Competition and Resource Management” was given in Paris at a Working Group on Machine Learning and Big Data of the French Ministry of Social Affairs and Health (organizer: Magali Beffy). The group is formed by scientists and researchers working on various organizations/institutions (Ministry of Health, INSEE, IRDES, Dauphine) – 25/1/2017.
- MECO 2017 – Part of the ongoing work on behavioral epidemics and dynamic control done with Stefano Sarao, Kevin Scaman, and Nicolas Vayatis is going to be presented in a poster format by Stefano at the 42nd Middle European Cooperation in Statistical Physics (MECO), Feb 2017, Lyon, France.
- ΑΑΑΙ 2017 – “Multivariate Hawkes Processes for Large-scale Inference“, co-authored with Rémi Lemonnier and Kevin Scaman, will appear in the 31st AAAI Conference on Artificial Intelligence (AAAI-17), San Francisco, CA, US. [638 papers accepted / 2,590 submissions : 24.63% rate] – [pdf][supplementary][poster].
In this paper, we present a framework for fitting multivariate Hawkes processes for large-scale problems both in the number of events in the observed history n and the number of event types d (i.e. dimensions). The proposed Low-Rank Hawkes Process (SLRHP) framework introduces a low-rank approximation of the kernel matrix that allows to perform the nonparametric learning of the d^2 triggering kernels using at most O(ndr^2) operations, where r is the rank of the approximation (r << d, n). This comes as a major improvement to the existing state-of-the-art inference algorithms that are in O(nd^2). Furthermore, the low-rank approximation allows SLRHP to learn representative patterns of interaction between event types, which is usually for the analysis of complex processes in real-world networks.
- Jury member in PhD defense – Kevin Scaman successfully defended in October his PhD thesis at CMLA, ENS Cachan. I am glad that I have been co-supervisor and collaborator to Kevin for three years, and finally a jury member in his defense.
- MORANE project – The project that I will be primarily been involved in the next year has been launched on Sep. 2016 and is in collaboration with SNCF, the French National Railway Company (read more).
- CCS 2016 – Part of our work with Stefano Sarao, Kevin Scaman, and Nicolas Vayatis, appeared at the Conference on Complex Systems 2016 at Amsterdam as a full oral presentation entitled “Dynamic control of social diffusion using extensions of the SIS model” (on a working paper).
- PC Member – 2nd International Workshop on “Data Science for Social Media and Risk“, part of IEEE ICDM 2016.
- Journal paper – “Suppressing Epidemics in Networks using Priority-Planning”, co-authored with K. Scaman, and N. Vayatis, published in IEEE Transactions on Network Science and Engineering (TNSE) [pdf][bib]. Abstract
In this paper, we analyze a large class of dynamic resource allocation (DRA) strategies, named priority planning, that aim to suppress SIS epidemics taking place in a network. This is performed by distributing treatments of limited efficiency to its infected nodes, according to a priority-order precomputed offline. Under this perspective, an efficient DRA strategy for a given network can be designed by learning a proper linear arrangement of its nodes. In our theoretical analysis, we derive upper and lower bounds for the extinction time of the diffusion process that reveal the role of the maxcut of the considered linear arrangement. Accordingly, we highlight that the cutwidth, which is the minimum maxcut of all possible linear arrangements for a network, is a fundamental network property that determines the resource budget required to suppress the epidemic under priority planning. Finally, by making direct use of our theoretical results, we propose a novel and efficient DRA strategy, called maxcut minimization (MCM), which outperforms other competing strategies in our simulations, while offering desirable robustness under various noise profiles.
- Invited Talk – “Dynamic suppression of epidemics on networks“, at the Complex Networks research group of LIP6, Paris 6 (Jussieu), France, 13/6/2016.
- Invited Talk – “Epidemics in the new socio-economic era: challenges and applications“, at the Department of Computer Science and Engineering, University of Ioannina, Greece, 25/5/2016.
- Invited Talk – “Suppressing epidemics on arbitrary networks using treatment resources of limited efficiency“, at INRA Research Center at Jouy-en-Josas, Paris area, 4/3/2016.
- TechReport – A technical report under the title “Multivariate Hawkes Processes for Large-Scale Inference“, co-authored with R. Lemonnier and K. Scaman was released, Feb 26, 2016. [arXiv link][pdf][bib].
In this paper, we present a framework for fitting multivariate Hawkes processes for large-scale problems both in the number of events in the observed history n and the number of event types d (i.e. dimensions). The proposed Low-Rank Hawkes Process (LRHP) framework introduces a low-rank approximation of the kernel matrix that allows to perform the nonparametric learning of the d^2 triggering kernels using at most O(ndr^2) operations, where r is the rank of the approximation (r << d, n). This comes as a major improvement to the existing state-of-the-art inference algorithms that are in O(nd^2). Furthermore, the low-rank approximation allows LRHP to learn representative patterns of interaction between event types, which may be valuable for the analysis of such complex processes in real world datasets. The efficiency and scalability of our approach is illustrated with numerical experiments on simulated as well as real datasets.
- SETN 2016 – Accepted paper “Improving Text Stream Clustering using Term Burstiness and Co-burstiness“, co-authored with P. Zagorisios and A. Likas, at the Hellenic Conference of Artificial Intelligence (SETN), May 18-20, 2016. [pdf][slides][bib]. Abstract
In text streams, documents appear over time and their timestamps can be used to improve typical approaches for text representation and clustering. A way to exploit temporal information is through the detection of bursty terms in such streams, i.e. terms that appear in many documents during short time period. Research efforts so far have shown that utilizing the burst information in the text representation can improve the performance of text clustering algorithms. However, most attempts take into account the bursty terms individually, without investigating the relation between them.
In this work, we take advantage of the fact that most of the important documents of a topic are published during the period in which the `main’ topic terms are bursty. Therefore, we focus on both term burstiness and co-burstiness by determining groups of terms that are simultaneously bursty at a time period and also co-occurring in the same documents. Next, the documents that contain co-bursty terms from those groups, are considered as important for the respective topics and are used to construct robust synthetic prototypes following an agglomerative process. These prototypes are finally used to initialize in a deterministic fashion the spherical k-means clustering algorithm. Experimental results validate empirically the quality of the solutions provided by the proposed approach which seems to efficiently overcome the initialization problem of spherical k-means.
- NIPS Networks 2015 – A workshop paper with the title “Learning to Suppress SIS Epidemics in Networks“, co-authored with K. Scaman and N. Vayatis, will appear in the “Networks in the Social and Information Sciences” workshop in conjunction with 29th Annual Conference on Neural Information Processing Systems (NIPS 2015), November 9-12, 2015. [pdf][poster][bib].
- ICTAI 2015 – A conference paper with the title “A Greedy Approach for Dynamic Control of Diffusion Processes in Networks“, co-authored with K. Scaman and N. Vayatis, will appear in the IEEE International Conference on Tools with Artificial Intelligence (ICTAI), pp. 652-659, November 9-12, 2015. [pdf] — You can find more about this methodology in the related page. Abstract
This paper investigates the control of a diffusion process by utilizing real-time information. More specifically, we allow the network administrator to adjust the allocation of control resources, a set of treatments that increase the recovery rate of infected nodes, according to the evolution of the diffusion process. We first present a novel framework for describing a large class of dynamic control strategies. These strategies rely on sorting the nodes according to a priority score in order to treat more sensitive regions first. Then, we propose the Largest Reduction in Infectious Edges (LRIE) control strategy which is based on a greedy minimization of the cost associated to the undesired diffusion, and has the benefits of being efficient and easy to implement. Our simulations, which were conducted using a software package that we developed and made available to the community, show that the LRIE strategy substantially outperforms its competitors in a wide range of scenarios.
- FET proposal – A research proposal has been submitted on Sep 2015 for evaluation and consideration for getting funded in the frame of E.C. Horizon 2020 – Research and Innovation Framework Program.
- Invited Summit Talk – “Efficient algorithms for the suppression of diffusion processes on networks with application in epidemiology and marketing“, summit: “Big data and public policies for the transportation” organized by the General Direction of Infrastructures, Transportation, and the General Commissioner on Durable Development of the Ministry of Ecology, Durable Development and Energy, along with ENS Cachan and PSE-School of Economics of Paris (15/10/2015) [agenda][presentation].