Michal Valko

Michal Valko, machine learning scientist DeepMind, Inria, and a lecturer at MVA/ENS PS.

  machine learning, sequential learning, Monte-Carlo tree search, active learning, graph methods, bandit theory


News: new Congrats to Omar and Guillaume for their NeuriPS 2019 travel awards!
News: new Four papers accepted to NeurIPS 2019. See you in Vancouver and Whistler!
News: new I am giving an invited course on reinforcement learning during February 19-22th, 2020 in winter school, Sochi, Russia.
News: new I am serving as an area chair for NeurIPS 2019.
News: new I am giving an invited talk during October 16-18th, 2019 at GIF 2019, Yerevan, Armenia.
News: new I am giving a talk during September 25-26, 2019, at Recent developments in kernel methods, UCL,London, UK.
News: new I am giving an invited talk during September 26-27, 2019, 2019 at Lancaster and Deepmind Bandit Workshop, London, UK.
News: new I am giving an invited talk on July 23th, 2019 for Cisco in Kraków, Poland.
News: new BOLD (ANR) project accepted for 2019 - 2023 (PI: V. Perchet)
News: I am giving an invited talk Yandex HQ on July 5th, 2019 at Yandex HQ 2019, Moscow, Russia.
News: I am giving an invited talk during July 3-8th, 2019 at RAAI Summer School 2019, Moscow Institute of Physics and Technology.
News: We are organizing Reinforcement Learning Summer SCOOL on 1-12 July 2019 in Lille, France.

older news


Michal is a machine learning scientist in DeepMind Paris, SequeL team at Inria, and the lecturer of the master 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) minimizing the data that humans need to 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 online learning, bandit algorithms, semi-supervised learning, and anomaly detection. Most recently he has worked on sequential algorithms with structured decisions where exploiting the structure leads to provably faster learning. Structured learning requires more time and space resources and therefore the most recent work of Michal includes efficient approximations such as graph and matrix sketching with learning guarantees. In past, the common thread of Michal's work has been adaptive graph-based learning and its application to 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 Ph.D. in 2011 from the University of Pittsburgh under the supervision of Miloš Hauskrecht and after was a postdoc of Rémi Munos before taking a permanent position at Inria in 2012.

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)
  • PGMO-IRMO grant of Fondation Mathématique Jacques Hadamard: Theoretically grounded efficient algorithms for high-dimensional and continuous reinforcement learning, 2018 - 2020 (with M. Pirotta)
  • BoB (ANR) - Bayesian statistics for expensive models and tall data, 2016 - 2020 (with R. Bardenet)
  • LeLivreScolaire.fr - Sequential Learning for Educational Systems, 2017-2020 (PI)
  • BOLD (ANR) - PI - Beyond Online Learning for better Decision making, 2019 - 2023 (with V. Perchet)
  • 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
previous projects

Students and postdocs

  • Édouard Oyallon, 2017 - 2018, ENS Rennes/ENS Ulm, postdoc ↝ École Centrale de Paris
  • Rianne de Heide, 2019, CWI/Leiden University, visiting PhD student, with Emilie Kaufmann
  • Tomáš Kocák, 2013 - 2016, Comenius University, PhD student, with Rémi Munos ↝ ENS Lyon
  • Daniele Calandriello, 2014 - 2017, Polimi, PhD student, AFIA, 1st prize, with Alessandro Lazaric ↝ IIT
  • Axel Elaldi, 2017-2018, master student, École Centrale de Lille ↝ ENS Paris-Saclay/MVA
  • 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
  • Côme Fiegel, 2019, ENS Ulm, L3 student, with Victor Gabillon


  • DeepMind Paris (bureau: FR-PAR-14L-6-623E)
  • 14 Rue de Londres
  • 75009 Paris
  • 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
  • CMLA, ENS Paris-Saclay (bureau: vacataires)
  • 61 avenue du président Wilson
  • 40 avenue Halley
  • 94235 Cachan cedex