Gabor Mihucz (Preferred)
Gabor Mihucz
University of Alberta
Names
How do you usually write your name as author of a paper? Also add any other names you have authored papers under.
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.
****@ualberta.ca
Personal Links
Add links to your profiles on other sites. (Optional)
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.
PhD student
University of Alberta (ualberta.ca)
2022 – Present
MS student
University of Alberta (ualberta.ca)
2020 – 2022
Undergrad student
University of Glasgow (glasgow.ac.uk)
2016 – 2020
Advisors, Relations & Conflicts
Enter all advisors, co-workers, and other people that should be included when detecting conflicts of interest.
PhD Advisor
Csaba Szepesvári
****@ualberta.ca
2022 – Present
Coauthor
Adam White
****@ualberta.ca
2021 – 2022
Coauthor
Farzane Aminmansour
****@ualberta.ca
2021 – 2022
Coauthor
Chunlok Lo
****@ualberta.ca
2021 – 2022
Coauthor
Dhawal Gupta
****@ualberta.ca
2020 – 2022
Coauthor
Matthew Schlegel
****@ualberta.ca
2020 – 2022
Coauthor
Kirby Banman
****@ualberta.ca
2020 – 2022
Coauthor
James Kostas
****@cs.umass.edu
2020 – 2022
Coauthor
Philip S. Thomas
****@cs.umass.edu
2020 – 2022
Coauthor
Martha White
****@ualberta.ca
2020 – 2022
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
Reinforcement Learning
2018 – Present
Representation Learning
2018 – Present
Continual Learning
2018 – Present
Model-Based Reinforcement Learning
2018 – Present
Hierarchical Reinforcement Learning
2018 – Present