Tamir Hazan, Ph.D.
Associate professor

Faculty of Industrial Engineering and Management
Technion - Israel Institute of Technology
Bloomfield building, room 503
Technion City, Haifa 32000
tamir.hazan at technion.ac.il

[research]   [papers]  

Research interests

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. Asisstant professor at UIUC.
Alon Cohen. Researcher at Google
Idan Schwartz (Ph.D. student)
Guy Lorberbom (Ph.D. studen)
Itai Gat (Ph.D. studen)
Adi Manos (M.Sc. student)
Ram Yazdi (M.Sc. student)
Chana Ross (M.Sc. student)
Hedda Cohen (M.Sc. student)
Bar Mayo (M.Sc. student)
Tom Ron (M.Sc. student)
Alon Berliner (M.Sc. student)
Vered Halperin (M.Sc. student)
Gilad Goldreich (M.Sc. student)
Meghan Lahmi (M.Sc. student)
Avrech Ben-David (M.Sc. student)

Edited Volumes

Perturbations, Optimization, and Statistics (2016)
Tamir Hazan, George Papandreou, Daniel Tarlow (Editors).
Neural Information Processing series, MIT Press.
[MIT Press], [amazon]

Recent papers

  • B. Mayo, T. Hazan, A. Tal.
    Visual Navigation with Spatial Attention
    Conference on Computer Vision and Pattern Recognition (CVPR), 2021.

  • S. Khadka, E. Aflalo, M. Mardar, A. Ben-David, S. Miret, S. Mannor, T. Hazan, H. Tang, S. Majumdar.
    Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning
    International Conference on Learning Representations (ICLR), 2021.
  • I. Gat, I. Schwartz, A. Schwing and T. Hazan.
    Removing Bias in Multi-modal Classifiers: Regularization by Maximizing Functional Entropies
    Neural Information Processing Systems (NeurIPS), 2020.
  • G. Lorberbom, C.Maddison, N. Heess, T. Hazan and D. Tarlow.
    Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces
    Neural Information Processing Systems (NeurIPS), 2020.
  • T. Hazan, S. Sabach and S. Voldman.
    Stochastic Proximal Linear Method for Structured Non-Convex Problems
    Optimization Methods and Software, 2020.
  • K. Mike, T. Hazan and O.Hazan.
    Equalizing Data Science Curriculum for Computer Science Pupils
    Koli Calling International Conference on Computing Education Research, 2020.