Michal Valko

Google DeepMind

Names

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Michal Valko

Emails

Enter email addresses associated with all of your current and historical institutional affiliations, as well as all your previous publications, and the Toronto Paper Matching System. This information is crucial for deduplicating users, and ensuring you see your reviewing assignments.

****@inria.fr
,
****@gmail.com
,
****@google.com
,
****@deepmind.com

Education & Career History

Enter your education and career history. The institution domain is used for conflict of interest detection and institution ranking. For ongoing positions, leave the end field blank.

Staff Research Scientist
Google DeepMind (deepmind.com)
2019Present
 
Lecturer
Ecole Normale Superieure (ens-paris-saclay.fr)
2014Present
 
Experienced Research Scientist
Inria (inria.fr)
2013Present
 
Habilation
Ecole Normale Superieure (ens-paris-saclay.fr)
20152016
 
Research Scientist
Inria (inria.fr)
20122013
 
Postdoc
Inria (inria.fr)
20112012
 
PhD student
University of Pittsburgh (pitt.edu)
20052011
 
MS student
Comenius University (uniba.sk)
20002005
 

Advisors, Relations & Conflicts

Enter all advisors, co-workers, and other people that should be included when detecting conflicts of interest.

PhD Advisee
Jean Tarbouriech
****@gmail.com
20192022
 
PhD Advisee
Omar Darwiche Domingues
****@inria.fr
20182022
 
PhD Advisee
Xuedong Shang
****@gmail.com
20172021
 
Postdoc Advisee
Pierre Ménard
****@gmail.com
20192020
 
PhD Advisee
Julien Seznec
****@gmail.com
20172020
 
PhD Advisee
Guillaume Gautier
****@gmail.com
20172020
 
PhD Advisee
Jean-Bastien Grill
****@google.com
20142019
 
Postdoc Advisee
Edouard Oyallon
****@gmail.com
20172018
 
PhD Advisee
Daniele Calandriello
****@google.com
20142017
 
PhD Advisee
Tomáš Kocák
****@ens-lyon.fr
20132016
 
Postdoc Advisor
Rémi Munos
****@google.com
20112012
 
PhD Advisor
Milos Hauskrecht
****@pitt.edu
20052011
 

Expertise

For each line, enter comma-separated keyphrases representing an intersection of your interests. Think of each line as a query for papers in which you would have expertise and interest. For example: deep learning, RNNs, dependency parsing

self-supervised learning
2020Present
 
reinforcement learning
2018Present
 
bandits
2012Present
 
online learning
2007Present
 
semi-supervised learning
2005Present
 
graph-based learning
2003Present
 
neural networks
20002005