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16

Résumé HAL : Lilian Besson

I am Lilian Besson, a former student in Mathematics and Computer Science at ENS de Cachan. I am a passionnate programmer, open-source enthusiast and young researcher in machine learning, learning theory and cognitive radio.

From August 2019, I am now 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.

I am also an associate researcher with the PANAMA team at IRISA and INRIA Rennes. My research interests lie between sequential learning, tensor decomposition, cognitive radio, Python and open source software, and other directions.

Between september 2016 and fall 2019 :

  • I was pursuing my PhD at CentraleSupélec (SCEE team, IETR lab) in Rennes (France), with Christophe Moy and Emilie Kaufmann (Inria, SequeL team, CRIStAL lab, at Lille). My PhD is on multi-players multi-arms bandits models applied to radio-telecommunication, especially Internet-of-Things problems. I defended in November, and I now hold a PhD in telecommunications.
  • And I have been teaching 64 hours by year, in theoretical computer science at ENS Rennes (for a class preparing for the "agrégation" national exam, with a major in mathematics and a minor in computer science, level M2), at ENSAI (complexity and calculabilty, level L3).

Journal articles1 document

  • Christophe Moy, Lilian Besson, Guillaume Delbarre, Laurent Toutain. Decentralized spectrum learning for radio collision mitigation in ultra-dense IoT networks: LoRaWAN case study and experiments. Annals of Telecommunications - annales des télécommunications, Springer, 2020, 75 (11-12), pp.711-727. ⟨10.1007/s12243-020-00795-y⟩. ⟨hal-02956350⟩

Conference papers7 documents

  • Lilian Besson, Emilie Kaufmann. Analyse non asymptotique d'un test séquentiel de détection de rupture et application aux bandits non stationnaires. GRETSI 2019 - XXVIIème Colloque francophone de traitement du signal et des images, Aug 2019, Lille, France. ⟨hal-02152243⟩
  • Remi Bonnefoi, Lilian Besson, Julio Manco-Vasquez, Christophe Moy. Upper-Confidence Bound for Channel Selection in LPWA Networks with Retransmissions. The 1st International Workshop on Mathematical Tools and technologies for IoT and mMTC Networks Modeling, Philippe Mary, Samir Perlaza, Petar Popovski, Apr 2019, Marrakech, Morocco. ⟨hal-02049824v2⟩
  • Lilian Besson, Remi Bonnefoi, Christophe Moy. GNU Radio Implementation of MALIN: "Multi-Armed bandits Learning for Internet-of-things Networks". IEEE WCNC 2019 - IEEE Wireless Communications and Networking Conference, Apr 2019, Marrakech, Morocco. ⟨10.1109/WCNC.2019.8885841⟩. ⟨hal-02006825⟩
  • Christophe Moy, Lilian Besson. Decentralized Spectrum Learning for IoT Wireless Networks Collision Mitigation. ISIoT 2019 - 1st International Workshop on Intelligent Systems for the Internet of Things, May 2019, Santorin, Greece. ⟨hal-02144465⟩
  • Lilian Besson, Emilie Kaufmann, Christophe Moy. Aggregation of Multi-Armed Bandits Learning Algorithms for Opportunistic Spectrum Access. IEEE WCNC - IEEE Wireless Communications and Networking Conference, Apr 2018, Barcelona, Spain. ⟨10.1109/wcnc.2018.8377070⟩. ⟨hal-01705292⟩
  • Lilian Besson, Emilie Kaufmann. Multi-Player Bandits Revisited. Algorithmic Learning Theory, Mehryar Mohri; Karthik Sridharan, Apr 2018, Lanzarote, Spain. ⟨hal-01629733v2⟩
  • Rémi Bonnefoi, Lilian Besson, Christophe Moy, Emilie Kaufmann, Jacques Palicot. Multi-Armed Bandit Learning in IoT Networks: Learning helps even in non-stationary settings. CROWNCOM 2017 - 12th EAI International Conference on Cognitive Radio Oriented Wireless Networks, Sep 2017, Lisbon, Portugal. pp.173-185, ⟨10.1007/978-3-319-76207-4_15⟩. ⟨hal-01575419v2⟩

Poster communications3 documents

  • Remi Bonnefoi, Lilian Besson, Christophe Moy. Multi-Armed bandit Learning in Iot Networks (MALIN). ICT 2018 - 25th International Conference on Telecommunications, Jun 2018, Saint-Malo, France. ⟨hal-02013866⟩
  • Lilian Besson. Multi-Player Bandits Revisited. Séminaire « IETR : Interagir Evaluer Transmettre Réunir », Jun 2018, Vannes, France. ⟨hal-02013847⟩
  • Remi Bonnefoi, Lilian Besson. Multi-Armed Bandit Learning in IoT Networks. Journée des Doctorants de l'IETR, Jul 2017, Rennes, France. ⟨hal-02013839⟩

Preprints, Working Papers, ...3 documents

  • Lilian Besson, Emilie Kaufmann, Odalric-Ambrym Maillard, Julien Seznec. Efficient Change-Point Detection for Tackling Piecewise-Stationary Bandits. 2020. ⟨hal-02006471v2⟩
  • Lilian Besson, Emilie Kaufmann. What Doubling Tricks Can and Can't Do for Multi-Armed Bandits. 2018. ⟨hal-01736357⟩
  • Lilian Besson. SMPyBandits: an Experimental Framework for Single and Multi-Players Multi-Arms Bandits Algorithms in Python. 2018. ⟨hal-01840022⟩

Theses1 document

Lectures1 document

  • Pierre Haessig, Lilian Besson. Julia, my new friend for computing and optimization?. Master. France. 2018. ⟨cel-01830248⟩