Tamir Hazan, Ph.D.
Faculty of Industrial Engineering and Management
Our research interests involve the theoretical and practical aspects of machine learning. Our research focuses on mathematically founded solutions to modern real life problems that demonstrate non-traditional statistical behavior. Recent examples include efficient learning of high dimensional statistics using Gumbel-max perturbation mdoels in discriminative learning, generative learning and reinforcement learning. We also learn graph based attention models across modalities and consider different aspects of Bayesian deep learning using Gaussian perturbations of their parameters. The practice of our work is motivated by many visual and language problems.
Guy Lorberbom (Ph.D. student)
Itai Gat (Ph.D. student)
Hedda Cohen (Ph.D. student)
Tom Ron (M.Sc. student)
Alon Berliner (M.Sc. student)
Vered Halperin (M.Sc. student)
Igor Drozdov (M.Sc. student)
Hadar Sinai (M.Sc. student)
Assaf Mushkin (M.Sc. student)
Alex Schwing. Asisstant professor at UIUC
Alon Cohen. Assistant professor at TAU
Idan Schwartz postdoc at TAU
Perturbations, Optimization, and Statistics (2016)
Tamir Hazan, George Papandreou, Daniel Tarlow (Editors).
Neural Information Processing series, MIT Press.
[MIT Press], [amazon]