machine learning scientist in SequeL team at Inria

  machine learning, minimal feedback, online/sequential learning, graph-based methods, semi-supervised learning

News

News: new Brownian motion optimization accepted to NIPS 2018! See you in Montréal!
News: new I am giving an invited talk on September 10-13th, 2018 at International Workshop on Optimization and Machine Learning at CIMI, Toulouse.
News: new A paper on optimistic optimization accepted to EWRL 2018.
News: new A paper on scattering for deep learning accepted to ECCV 2018.
News: new Starting October 1st, 2018, I will be teaching a graduate course on Graphs in Machine Learning in MVA Master at ENS Paris-Saclay!
News: A paper on distributed graph sparsification accepted to ICML 2018. See you in Stockholm!
News: A bandit paper on best of both worlds accepted to COLT 2018. See you in Stockholm!
News: Received Inria award for scientific excellence for 2018 - 2021: Prime d'excellence scientifique
News: Congrats to Daniele Calandriello for winning the prize for the Best AI Thesis from France in 2018. inriaCP inriaCP cnrs lille1 actu lavoixdunord newstank
News: I am serving as an area chair for NIPS 2018.

Bio

Michal is a junior scientist in SequeL team at Inria Lille - Nord Europe, France, lead by Philippe Preux and Rémi Munos. He also teaches the course Graphs in Machine Learning at l'ENS Paris-Saclay. Michal is primarily interested in designing algorithms that would require as little human supervision as possible. This means 1) reducing the “intelligence” that humans need to input into the system and 2) minimising the data that humans need spend inspecting, classifying, or “tuning” the algorithms. Another important feature of machine learning algorithms should be the ability to adapt to changing environments. That is why he is working in domains that are able to deal with minimal feedback, such as bandit algorithms, semi-supervised learning, and anomaly detection. Most recently he has worked on sequential algorithms with structured decisions where exploiting the structure can lead to provably faster learning. In the past the common thread of Michal's work has been adaptive graph-based learning and its application to the real world applications such as recommender systems, medical error detection, and face recognition. His industrial collaborators include Adobe, Intel, Technicolor, and Microsoft Research. He received his PhD in 2011 from University of Pittsburgh under the supervision of Miloš Hauskrecht and after was a postdoc of Rémi Munos.

• 1-page resume
• long CV

Collaborative Projects

• CompLACS (EU FP7) - COMposing Learning for Artificial Cognitive Systems, 2011 - 2015 (with J. Shawe-Taylor)
• DELTA (EU CHIST-ERA) - PC - Dynamically Evolving Long-Term Autonomy, 2018 - 2021 (with A. Jonsson)
• BoB (ANR) - Bayesian statistics for expensive models and tall data, 2016 - 2020 (with R. Bardenet)
• LeLivreScolaire.fr - Sequential Learning for Educational Systems, 2017-2012 (PI)
• Inria/CWI – Sequential prediction & Understanding Deep RL, postdoc funding (PC, 2016-2018)
• Extra-Learn (ANR) - PI - EXtraction and TRAnsfer of knowledge in reinforcement LEARNing, 2014 - 2018 (with A. Lazaric)
• EduBand - coPI - Educational Bandits project with Carnegie Mellon, 2015 - 2018 (with A. Lazaric and E. Brunskill)
• Allocate - PI - Adaptive allocation of resources for recommender systems with U. Potsdam, 2017 - 2019 (with A. Carpentier)
• INTEL/Inria - PI - Algorithmic Determination of IoT Edge Analytics Requirements, 2013 - 2014

Students and postdocs

• Guillaume Gautier, 2017 - 2020, PhD student, with Rémi Bardenet
• Pierre Perrault, 2017 - 2020, PhD student, with Vianney Perchet
• Julien Seznec, 2017 - 2020, PhD student, with Alessandro Lazaric and Jonathan Banon
• Xuedong Shang, 2017 - 2020, PhD student, with Emilie Kaufmann
• Omar Darwiche Domingues, 2018 - 2021, PhD student, with Emilie Kaufmann
• Édouard Oyallon, 2017 - 2018, postdoc ↝ École Centrale de Paris

• Tomáš Kocák, 2013 - 2016, PhD student, with Rémi Munos
• Daniele Calandriello, 2014 - 2017, PhD student, AFIA, 1st prize, with Alessandro Lazaric ↝ IIT
• Jean-Bastien Grill, 2014 - 2018, PhD student, with Rémi Munos ↝ DeepMind

• Axel Elaldi, 2017-2018, master student, École Centrale de Lille
• Xuedong Shang, 2017, master student, ENS Rennes, with Emilie Kaufmann ↝ Inria
• Guillaume Gautier, 2016, master student, École Normale Supérieure, Paris-Saclay, with Rémi Bardenet ↝ Inria/CNRS
• Andrea Locatelli, 2015-2016, ENSAM/ENS Paris-Saclay, with Alexandra Carpentier ↝ Universität Potsdam
• Souhail Toumdi, 2015 - 2016, master student, École Centrale de Lille, with Rémi Bardenet ↝ ENS Paris-Saclay/MVA
• Akram Erraqabi, 2015, master student, École Polytechnique, Paris ↝ Université de Montréal
• Mastane Achab, 2015, master student, École Polytechnique, Paris, with G. Neu ↝ l'ENS Paris-Saclay ↝ Télécom ParisTech
• Jean-Bastien Grill, 2014, master student, École Normale Supérieure, Paris, with Rémi Munos ↝ Inria
• Alexandre Dubus, 2012-2013, master student, Université Lille1 - Sciences et Technologies ↝ Inria
• Karim Jedda, 2012-2013, master student, École Centrale de Lille ↝ ProSiebenSat.1
• Alexis Wehrli, 2012-2013, master student, École Centrale de Lille ↝ ERDF

Contact

• Inria Lille - Nord Europe, equipe SequeL (bureau: A05)
• Parc Scientifique de la Haute Borne
• 40 avenue Halley
• 59650 Villeneuve d'Ascq, France
• office phone: +33 3 59 57 7801