E
Elad Hazan
Researcher at Princeton University
Publications - 242
Citations - 28209
Elad Hazan is an academic researcher from Princeton University. The author has contributed to research in topics: Regret & Convex optimization. The author has an hindex of 57, co-authored 225 publications receiving 24683 citations. Previous affiliations of Elad Hazan include Technion – Israel Institute of Technology & Tel Aviv University.
Papers
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Proceedings Article
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization.
TL;DR: Adaptive subgradient methods as discussed by the authors dynamically incorporate knowledge of the geometry of the data observed in earlier iterations to perform more informative gradient-based learning, which allows us to find needles in haystacks in the form of very predictive but rarely seen features.
Journal Article
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
TL;DR: This work describes and analyze an apparatus for adaptively modifying the proximal function, which significantly simplifies setting a learning rate and results in regret guarantees that are provably as good as the best proximal functions that can be chosen in hindsight.
Book
Introduction to Online Convex Optimization
TL;DR: This monograph portrays optimization as a process, by applying an optimization method that learns as one goes along, learning from experience as more aspects of the problem are observed.
Journal ArticleDOI
Logarithmic regret algorithms for online convex optimization
TL;DR: Several algorithms achieving logarithmic regret are proposed, which besides being more general are also much more efficient to implement, and give rise to an efficient algorithm based on the Newton method for optimization, a new tool in the field.
Journal ArticleDOI
The Multiplicative Weights Update Method: A Meta-Algorithm and Applications
TL;DR: A simple meta-algorithm is presented that unifies many of these disparate algorithms and derives them as simple instantiations of the meta-Algorithm.