Mohammad Ghavamzadeh
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Description

Mohammad Ghavamzadeh received a Ph.D. degree in computer science from the University of Massachusetts Amherst in 2005. Since September 2005 he has been a postdoctoral fellow at the Department of Computing Science at the University of Alberta, working with Prof. Richard Sutton. The main objective of his research is to investigate the principles of scalable decision-making grounded by real-world applications. In the last two years, Ghavamzadeh’s research has been mostly focused on using recent advances in statistical machine learning, especially Bayesian reasoning and kernel methods, to develop more scalable reinforcement learning algorithms.


Lectures:

lecture
flag Safety in RL
as author at  Deep Learning and Reinforcement Learning Summer School, Toronto 2018,
88 views
  lecture
flag A Top-down Approach to Feature Selection in Reinforcement Learning
as author at  Large-scale Online Learning and Decision Making (LSOLDM) Workshop, Cumberland Lodge 2012,
118 views
lecture
flag Sparse Reinforcement Learning in High Dimensions
as author at  Cognitive Systems Workshop & Thematic Programmes and Pump Priming Workshops, Cumberland Lodge 2012,
218 views
  poster
flag Poster session
as author at  New Frontiers in Model Order Selection,
together with: Alexandre Lacoste, Nicolas Baskiotis, Stefan Kremer, Aurélie Boisbunon, Morteza Haghir Chehreghani, Yuri Grinberg, Amir-massoud Farahmand, Marina Sapir, Yevgeny Seldin,
218 views
demonstration video
flag LSTD with Random Projections
as author at  Video Journal of Machine Learning Abstracts - Volume 1,
147 views
  tutorial
flag Introduction to Reinforcement Learning and Bayesian learning
as author at  Tutorials,
2391 views