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.
Alex Schwing, now asisstant professor at UIUC.
Alon Cohen, (Ph.D. student)
Idan Schwartz, (Ph.D. student)
Sergey Voldman, (Ph.D. student)
Guy Lorberbom, (M.Sc. studen)
Adi Manos, (M.Sc. student)
Ram Yazdi, (M.Sc. student)
Chana Ross, (M.Sc. student)
Noam Heimann, (M.Sc. student)
Hedda Cohen, (M.Sc. student)
Bar Mayo, (M.Sc. student)
Perturbations, Optimization, and Statistics (2016)
Tamir Hazan, George Papandreou, Daniel Tarlow (Editors).
Neural Information Processing series, MIT Press.
[MIT Press], [amazon]