Number of documents

12

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.

Since september 2016 and until fall 2019 :

I am 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.
And I am teaching about 80 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), and at Lycée Chateaubriand (for practical sessions of OCaml and computer science).

Conference papers5 documents

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-02049824v1⟩
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. ⟨hal-02006825⟩
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

Lilian Besson. Multi-Player Bandits Revisited. Séminaire « IETR : Interagir Evaluer Transmettre Réunir », Jun 2018, Vannes, France. ⟨hal-02013847⟩
Remi Bonnefoi, Lilian Besson, Christophe Moy. Multi-Armed bandit Learning in Iot Networks (MALIN). ICT2018 - 25th International Conference on Telecommunications, Jun 2018, Saint-Malo, France. ⟨http://ict-2018.org/demos/⟩. ⟨hal-02013866⟩
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. The Generalized Likelihood Ratio Test meets klUCB: an Improved Algorithm for Piece-Wise Non-Stationary Bandits. 2019. ⟨hal-02006471⟩
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⟩