Interests: Theoretical foundations of machine learning, design and analysis of efficient algorithms for machine learning and mathematical optimization.
Elad Hazan is a professor of computer science at Princeton University. His research focuses on the design and analysis of algorithms for basic problems in machine learning and optimization. Amongst his contributions are the co-invention of the AdaGrad algorithm for deep learning, and the first sublinear-time algorithms for convex optimization. He is the recipient of the Bell Labs prize, the IBM Goldberg best paper award twice, in 2012 and 2008, a European Research Council grant, a Marie Curie fellowship and twice the Google Research Award. He served on the steering committee of the Association for Computational Learning and has been program chair for COLT 2015. In 2017 he co-founded In8 inc. focusing on efficient optimization and control, acquired by Google in 2018. He is the co-founder and director of Google AI Princeton.
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization. (with J. Duchi and Y. Singer)
Journal of Machine Learning Research (JMLR) Volume 12, 2/1/2011 Pages 2121-2159
Introduction to online convex optimization
Foundations and Trends in Optimization, 2(3-4), pp.157-325, 2016.
Sublinear Optimization for Machine Learning. (with K. Clarkson and D. Woodruff)
Journal of the ACM (JACM), Volume 59 Issue 5, October 2012.
Playing Non-linear Games with Linear Oracles (with D. Garber)
54th Annual IEEE Symposium on Foundations of Computer Science (FOCS 2013)
Interior-Point Methods for Full-Information and Bandit Online Learning. (with Jacob Abernethy and Alexander Rakhlin)
IEEE Transactions on Information Theory 58(7): 4164-4175 (2012)
Logarithmic Regret Algorithms for Online Convex Optimization. (with Amit Agarwal and Satyen Kale)
Machine Learning Journal Volume 69 , Issue 2-3 Pages: 169 - 192, December 2007