Hazan's research primarily focuses on machine learning,mathematical optimization,control theory and reinforcement learning. He is the co-inventor of five US patents.
Machine learning and mathematical optimization
Hazan co-introduced adaptive subgradient methods to dynamically incorporate knowledge of the geometry of the data observed in earlier iterations,and to perform more informative gradient-based learning. The AdaGrad algorithm changed optimization for deep learning and serves as the basis for today's fastest algorithms. In his study,he also made substantial contributions to the theory of online convex optimization,including the Online Newton Step and Online Frank Wolfe algorithm,projection free methods,and adaptive-regret algorithms. [6]
In the area of mathematical optimization,Hazan proposed the first sublinear-time algorithms for linear classification as well as semi-definite programming. He also gave the first linearly converging Frank-Wolfe-type algorithm. [7]
More recently,Hazan and his group proposed a new paradigm for differentiable reinforcement learning called non-stochastic control,which applies online convex optimization to control. [8]
