Lilian Besson

Passionate teacher in Computer Science, prof. agrégé in MP2I at Lycée Kléber in Strasbourg (France).

📜   Short Bio

Hello ! I am Lilian Besson, teacher-coder at Kléber high school in Strasbourg (France) in the MP2I class, since September 2021. Before starting to work in CPGE (prépa) I taught at ENS Rennes for two years. I am professor "agrégé" in mathematics, and former student in mathematics and computer science from ENS de Cachan. I am a passionate programmer, ecologist and open-source enthusiast, and currently professor in computer science in classes préparatoires MP2I in 2021 in France. I also love to cook and meet people, to travel and exchange, to bike or hike, and to play Magic: the Gathering (really awesome Trading Cards Game).

From August 2019 to August 2021, I was a junior professor (agrégé) at ENS Rennes, in charge of the class preparing the "agrégation" national exam, with a major in mathematics and a minor in computer science, level M2, and in charge of lectures for introduction and advanced algorithms.

Between October 2016 and November 2019:

Fake picture of me

📰   News

🔬   Research

For my Ph.D., my research is in applied machine learning, focused on low-cost online learning algorithm with limited feedback (bandit feedback), mainly applied to cognitive radio problems for Opportunistic Spectrum Access and setting up reliable network access protocol for the future Internet of Things networks. By studying and applying classical and recent Multi-Armed Bandit algorithms to carefully designed radio models, we are able to prove some performance guarantees, both numerically in simulations and theoretically with statistical proofs.
I'll continue to do research after my PhD, I've joined in September 2019 the PANAMA team at IRISA laboratory in Rennes!

  • 14 Research talks and posters
    (since 2017)
  • 10 Research articles
    (9 published, 1 sent)
  • 2 PhD advisors
    and collaboration with 2 PhD student and postdoc
  • 2 Higher Education institutes where I teach in Rennes
  • 1 Research software
    (and many personal projects!)

📚   Publications

Orcid.org logo Orcid arXiv DBLP DBLP IdHAL Google Scholar HALtools
List of the PDF of my articles

Journal papers

  1. L. Besson, E. Kaufmann, O-A Maillard & Julien Seznec.
    Efficient Change-Point Detection for Tackling Piecewise-Stationary Bandits.
    Accepted in the Journal of Machine Learning Research volume 23, in 2022.
    [ PDF ] • [ Mobile version ] • [ Slides ] • [ Citations: 15 ] • [ arXiv ] • [ HAL ] • [ BibTeX ] • [ Code ]
  2. C. Moy & L. Besson & G. Delbarre & L. Toutain.
    Decentralized Spectrum Learning for Radio Collision Mitigation in Ultra-Dense IoT Networks: LoRaWAN Case Study and Measurements.
    Accepted to a special volume of the Annals of Telecommunications journal, on "Machine Learning for Intelligent Wireless Communications and Networking", August 2020. [ PDF ] • [ HAL ] • [ BibTeX ] • [ DOI ]

Preprints or in process of submissions

  1. L. Besson & E. Kaufmann.
    What Doubling-Trick Can and Can't Do for Multi-Armed Bandits. September 2018.
    [ PDF ] • [ Mobile version ] • [ Citations: 36 ] • [ arXiv ] • [ HAL ] • [ BibTeX ] • [ Code (LaTeX) ] • [ Code ]
  2. L. Besson. 🔬 🎰 SMPyBandits: an Open-Source Research Framework for Single and Multi-Players Multi-Arms Bandits (MAB) Algorithms in Python. July 2018. PyPI version Documentation Status Build Status GitHub forks GitHub stars GitHub watchers
    [ PDF ] [ PDF (long version) ] • [ Citations: 9 ] • [ HAL ] • [ BibTeX ] • [ Code ] • [ Documentation ]
  3. L. Besson. A Note on the Ei Function and a Useful Sum-Inequality. February 2018.
    [ PDF ] • [ HAL ] • [ BibTeX ] • [ Code ]

Communications in National Conferences (in France)

  1. L. Besson & E. Kaufmann.
    Analyse non asymptotique d'un test séquentiel de détection de ruptures et application aux bandits non stationnaires (in French).
    GRETSI, Lille, France. August 2019.
    [ PDF ] • [ 🇬🇧 Slides ] • [ 🇫🇷 Slides ] • [ HAL ] • [ BibTeX ] • [ Code (LaTeX) ] • [ Code ]

Communications in International Conferences

  1. C. Moy & L. Besson.
    Decentralized Spectrum Learning for IoT Wireless Networks Collision Mitigation.
    1st ISIoT workshop at the DCOSS conference, Santorini, Greece. May 2019.
    [ PDF ] • [ arXiv ] • [ HAL ] • [ BibTeX ] • [ DOI ]
  2. R. Bonnefoi, L. Besson, J. Manco-Vasquez & C. Moy.
    Upper-Confidence Bound for Channel Selection in LPWA Networks with Retransmissions.
    1st MoTION workshop at WCNC, Marrakech, Morocco. April 2019.
    [ PDF ] • [ Mobile ] • [ arXiv ] • [ HAL ] • [ BibTeX ] • [ Slides ] • [ Code (LaTeX) ] • [ Code (MATLAB) ] • [ DOI ]
  3. L. Besson, R. Bonnefoi & C. Moy.
    GNU Radio Implementation of MALIN: "Multi-Armed bandits Learning for Internet-of-things Network".
    WCNC (Wireless Communication and Networks Conference), Marrakech, Morocco. April 2019.
    [ PDF ] • [ Mobile version ] • [ arXiv ] • [ YouTube Video ] • [ HAL ] • [ BibTeX ] • [ Slides ] • [ Code (LaTeX) ] • [ Code (GNU Radio) ] • [ DOI ]
  4. L. Besson, R. Bonnefoi & C. Moy.
    MALIN: Multi-Arm bandit Learning for Iot Networks with GRC: A TestBed Implementation and Demonstration that Learning Helps.
    Demonstration presented at the ICT (International Conference on Communication), Saint-Malo, France. June 2018.
    [ PDF* ] • [ Poster ] • [ YouTube Video ] • [ Code (GNU Radio) ]
    More details… The demo implements a toy wireless system, with one IoT object communicating with a gateway (or base station) and trying to find the frequency channel which is the less perturbated by a random interfering traffic, by using reinforcement learning. We use three USRP cards by National Instruments for the random traffic, the IoT object and the gateway. It is based on our CROWNCOM 2017 article. See also this document. Here is a 5-minute video I made to explain this demonstration.
  5. L. Besson & E. Kaufmann.
    Multi-Player Bandits Revisited.
    ALT, Lanzarote, Canari Islands, April 2018.
    [ PDF ] • [ Mobile version ] • [ Erratum ] • [ Slides ] • [ Poster ] • [ Citations: 63 ] • [ arXiv ] • [ HAL ] • [ BibTeX ] • [ Code (LaTeX) ] • [ Code ]
    Presented a few times… In 2017 and 2018, I presented different versions of these slides for a SequeL seminar in Lille on December 22th, for the ALT conference in Lanzarote (Spain) on April 8th 2018, and I presented this poster for a Workshop on Multi-Armed Bandits and Learning Algorithms on May 24th 2018 in Rotterdam School of Management (Erasmus University, Netherlands), for the IETR lab "PhD Students Day" on June 15th 2018 (Vannes, France), and one last time for the Workshop Optimization and Learning on September 10th 2018 in University de Toulouse in Toulouse (France).
  6. L. Besson, E. Kaufmann & C. Moy.
    Aggregation of Multi-Armed Bandits learning algorithms for Opportunistic Spectrum Access.
    IEEE WCNC, Barcelona, Spain. April 2018.
    • [ PDF ] • [ Mobile version ] • [ Slides ] • [ HAL ] • [ BibTeX ] • [ Code (LaTeX) ] • [ Code ] • [ DOI ] • [ Proceedings ]
  7. R. Bonnefoi, L. Besson, C. Moy, E. Kaufmann & J. Palicot.
    Multi-Armed Bandit Learning in IoT Networks and non-stationary settings.
    CrownCom, Lisboa, Portugal. September 2017. Best Paper Award!
    [ PDF ] • [ Mobile version ] • [ Slides ] • [ Poster ] • [ Citations: 47 ] • [ arXiv ] • [ HAL ] • [ BibTeX ] • [ Code (LaTeX) ] • [ Code (MATLAB) ] • [ DOI ] • [ Proceedings ]
    Presented a few times… In 2017 I presented different versions of these slides for a SequeL seminar in Lille on September 15th, for the CrownCom conference in Lisbon on September 22th, for this research day organized by the GdR-ISIS in Paris on November 17th, and for the SCEE seminar in Rennes on November 23th.

Academic publications

  1. L. Besson.
    Multi-players Bandit Algorithms for Internet of Things Networks.
    PhD thesis. Under supervision of Prof. Christophe Moy and Dr Emilie Kaufmann. Team SCEE at CentraleSupélec (campus de Rennes) and IETR, Rennes, and team SequeL at Inria Lille Nord Europe, Lille. October 2016 - November 2019.
    [ PDF ] • [ bibTeX ] • [ Code (thesis) ] • [ Slides ] • [ Code (slides) ] • [ Code (simulations) ]
    Summary of my PhD thesis In this PhD thesis, we study wireless networks and reconfigurable end-devices that can access Cognitive Radio networks, in unlicensed bands and without central control. We focus on Internet of Things networks (IoT), with the objective of extending the devices' battery life, by equipping them with low-cost but efficient machine learning algorithms, in order to let them automatically improve the efficiency of their wireless communications. We propose different models of IoT networks, and we show empirically on both numerical simulations and real-world validation the possible gain of our methods, that use Reinforcement Learning. The different network access problems are modeled as Multi-Armed Bandits (MAB), but we found that analyzing the realistic models was intractable, because proving the convergence of many IoT devices playing a collaborative game, without communication nor coordination is hard, when they all follow random active pattern. The rest of this manuscript thus studies two restricted models, first multi-players bandits in stationary problems, then non-stationary single-player bandits. We also detail another contribution, SMPyBandits, our open-source Python library for numerical MAB simulations, that covers all the studied models and more. Keywords: Internet of Things (IoT), Cognitive Radio, Learning Theory, Collision Mitigation Sequential Learning, Reinforcement Learning, Multi-Armed Bandits (MAB), Decentralized Learning, Multi-Player Multi-Armed Bandits, Change Point Detection, Non-Stationary Multi-Armed Bandits.
  2. L. Besson, J. Fageot & M. Unser.
    A Theoretical Study Of Steerable Homogeneous Operators, And Applications.
    Research Internship Report – Master MVA. Internship with Prof.Dr. Michael Unser and Dr. Julien Fageot. BIG team at EPFL, Lausanne (Switzerland). August 2016.
    [ PDF ] • [ Slides ] • [ Code ] • [ 🏅 Rank: 1st & 🎓 Grade: 18.43/20 ]

🎓   Teaching

My wish is to keep doing both teaching and research, in Computer Science and/or Maths. Ideally, I will be the happiest man if I could spend the next 35 years teaching introductory Computer Science with Python, data structures and algorithmic, computer architecture etc. I like to share my passion for practical computer science and mathematics, and how both can be combined and used for research and modelisation problems, for instance for analysis of data coming from our daily life (e.g., my text messages, quotes from the Kaamelott TV show, or data about the national result for the maths agrégation exam, etc).
I am still teaching after my PhD, and even much more, as I joined since September 2019 the Computer Science department de at ENS de Rennes (as a junior professor), see this page.

💻   Code

I love to program and write code and documentation , especially in Python for science, GNU Bash for desktop automation, and HTML/CSS/JS for the web. Everything I did and do is open-source and published (using Git), on my GitHub or my Bitbucket profiles.

Here are a few things I did since 2012:

  • 2021 : Peut-on-coder-avec-OCaml-Python-et-C-par-SMS 🇫🇷 I wished to answer the following question: Can we code with OCaml, Python and C by simply sending an old-school SMS message? Spoiler alert: Yes we can! GitHub stars GitHub watchers
  • 2020 : Generateur-attestation-de-sortie-automatique-COVID-19-confinement-en-France 🇫🇷 Un script IPython qui génère automatiquement une attestation de sortie toute les 55 minutes, pour le confinement 2.0 en France face au COVID 19. Expérimental et pour le plaisir. GitHub stars GitHub watchers
  • 2018 : ParcourSup.py 🇫🇷 A clone of the code used by French government to decide the affectation between highschool students and universities (ParcoursSup), written in 🐍 Python 3. Aimed at being didactic. Documentation here (in French), and an interactive notebook here (in French). Documentation Status Build Status Binder GitHub stars GitHub watchers
  • 2018 : Gym-NES-Mario-Bros 🐍 🏋 OpenAI GYM for Nintendo NES emulator FCEUX and 1983 game Mario Bros. + Double Q Learning for mastering the game with reinforcement learning. GitHub forks GitHub stars GitHub watchers
  • 2017 : Jupyter-NBConvert-OCaml , custom Jupyter 📓 NBConvert exporter for the 🐫 OCaml language (and ocaml-jupyter kernel) GitHub stars GitHub watchers
  • 2017 : small Python 🐍 (Lempel-Ziv_Complexity) and Julia (LempelZiv.jl) libraries to efficiently compute the Lempel-Ziv complexity between two binary strings. Published on Pypi and METADATA.jl PyPI version GitHub stars GitHub stars
  • 2016 : uLogMe , self-monitoring software for GNU/Linux, with zero CPU overhead and outstanding visualizations. GitHub forks GitHub stars
  • 2013 : StrapDown.js , An awesome Javascript tool to quickly publish nice-looking webpages from raw Markdown, no server-side compilation. Light script Only 32 Kb. GitHub forks GitHub stars GitHub watchers
  • Since 2012 : My résumé 🎓, is open-source and publicly available, self hosted.