Michal Valko

Michal Valko, machine learning scientist in SequeL team at Inria

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

News

News: new DPPy: Sampling determinantal point processes with Python released!
News: new I will be on the program committee for COLT 2019.
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.

older news

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.

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
previous projects

Students and postdocs

  • 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

Selected Publications

  • Jean-Bastien Grill, Michal Valko, Rémi Munos: Optimistic optimization of a Brownian, in Neural Information Processing Systems (NIPS 2018) bibtex abstract abstract
  • Edouard Oyallon, Eugene Belilovsky, Sergey Zagoruyko, Michal Valko: Compressing the input for CNNs with the first-order scattering transform, in European Conference on Computer Vision (ECCV 2018) bibtex abstract abstract poster
  • Daniele Calandriello, Ioannis Koutis, Alessandro Lazaric, Michal Valko: Improved large-scale graph learning through ridge spectral sparsification, in International Conference on Machine Learning (ICML 2018) bibtex abstract abstract talk poster
  • Yasin Abbasi-Yadkori, Peter Bartlett, Victor Gabillon, Alan Malek, Michal Valko: Best of both worlds: Stochastic & adversarial best-arm identification, Conference on Learning Theory (COLT 2018) bibtex abstract abstract talk poster
  • Daniele Calandriello, Alessandro Lazaric, Michal Valko: Efficient second-order online kernel learning with adaptive embedding, in Neural Information Processing Systems (NIPS 2017) bibtex abstract abstract talk poster
  • Zheng Wen, Branislav Kveton , Michal Valko, Sharan Vaswani: Online influence maximization under independent cascade model with semi-bandit feedback, in Neural Information Processing Systems (NIPS 2017) bibtex abstract abstract
  • Guillaume Gautier, Rémi Bardenet, Michal Valko: Zonotope hit-and-run for efficient sampling from projection DPPs, in International Conference on Machine Learning (ICML 2017) bibtex abstract abstract talk poster
  • Daniele Calandriello, Alessandro Lazaric, Michal Valko: Distributed sequential sampling for kernel matrix approximation, in International Conference on Artificial Intelligence and Statistics (AISTATS 2017) and (ICML 2017 - LL) bibtex abstract abstract talk code poster
  • Akram Erraqabi, Alessandro Lazaric, Michal Valko, Emma Brunskill, Yu-En Liu: Trading off rewards and errors in multi-armed bandits, in International Conference on Artificial Intelligence and Statistics (AISTATS 2017) bibtex abstract abstract poster,
  • Tomáš Kocák, Rémi Munos, Branislav Kveton, Shipra Agrawal, Michal Valko: Spectral Bandits, accepted for publication to Journal of Machine Learning Research (JMLR 2017)
  • Branislav Kveton, Zheng Wen, Azin Ashkan, Michal Valko: Learning to Act Greedily: Polymatroid Semi-Bandits, accepted for publication to Journal of Machine Learning Research (JMLR 2017) bibtex abstract abstract arXiv preprint
  • Michal Valko: Bandits on graphs and structures, habilitation thesis, École normale supérieure de Cachan (ENS Cachan 2016) bibtex abstract abstract
  • Jean-Bastien Grill, Michal Valko, Rémi Munos: Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning, in Neural Information Processing Systems (NIPS 2016) bibtex abstract abstract talk poster [full oral presentation - 1.8% acceptance rate]
  • Mohammad Ghavamzadeh, Yaakov Engel, Michal Valko: Bayesian policy gradient and actor-critic algorithms, Journal of Machine Learning Research (JMLR 2016) bibtex abstract abstract code
  • Tomáš Kocák, Gergely Neu, Michal Valko: Online learning with noisy side observations, in International Conference on Artificial Intelligence and Statistics (AISTATS 2016) bibtex abstract abstract talk poster [full oral presentation - 6% acceptance rate]
  • Alexandra Carpentier, Michal Valko: Revealing graph bandits for maximizing local influence, in International Conference on Artificial Intelligence and Statistics (AISTATS 2016) bibtex abstract abstract poster
  • Jean-Bastien Grill, Michal Valko, Rémi Munos: Black-box optimization of noisy functions with unknown smoothness, in Neural Information Processing Systems (NIPS 2015) bibtex abstract abstract code, code in R poster
  • Alexandra Carpentier, Michal Valko: Simple regret for infinitely many armed bandits, in International Conference on Machine Learning (ICML 2015) bibtex abstract abstract talk poster arXiv
  • Tomáš Kocák, Gergely Neu, Michal Valko, Rémi Munos: Efficient Learning by Implicit Exploration in Bandit Problems with Side Observations, in Neural Information Processing Systems (NIPS 2014) bibtex abstract abstracttalk poster
  • Alexandra Carpentier, Michal Valko: Extreme Bandits, in Neural Information Processing Systems (NIPS 2014) bibtex abstract abstractposter
  • Gergely Neu, Michal Valko: Online Combinatorial Optimization with Stochastic Decision Sets and Adversarial Losses, in Neural Information Processing Systems (NIPS 2014) bibtex abstract abstracttalk poster
  • Michal Valko, Rémi Munos, Branislav Kveton, Tomáš Kocák: Spectral Bandits for Smooth Graph Functions, in International Conference on Machine Learning (ICML 2014) bibtex abstract abstractslides poster
  • Michal Valko, Branislav Kveton, Ling Huang, Daniel Ting: Online Semi-Supervised Learning on Quantized Graphs in Uncertainty in Artificial Intelligence (UAI 2010) bibtex abstract abstract Video: Adaptation, Video: OfficeSpace, spotlight poster
  • Milos Hauskrecht, Michal Valko, Shyam Visweswaram, Iyad Batal, Gilles Clermont, Gregory Cooper: Conditional Outlier Detection for Clinical Alerting in Annual American Medical Informatics Association conference (AMIA 2010) bibtex abstract abstract [Homer Warner Best Paper Award]

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


20-Sep-2018