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Fingerprint Dive into the research topics where Elad Hazan is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

  • 3 Similar Profiles
Convex optimization Engineering & Materials Science
Regret Mathematics
Online Optimization Mathematics
Learning systems Engineering & Materials Science
Online Learning Mathematics
Convex Optimization Mathematics
Stochastic Optimization Mathematics
Learning algorithms Engineering & Materials Science

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Projects 2015 2018

Research Output 2003 2019

Efficient full-matrix adaptive regularization

Hazan, E., Jan 1 2019, 36th International Conference on Machine Learning, ICML 2019. International Machine Learning Society (IMLS), p. 139-147 9 p. (36th International Conference on Machine Learning, ICML 2019; vol. 2019-June).

Princeton University

Research output: Chapter in Book/Report/Conference proceedingConference contribution

learning
experiment
Learning systems
Experiments
Deep learning

Hyperparameter optimization: A spectral approach

Hazan, E. E., Jan 1 2018.

Princeton University

Research output: Contribution to conferencePaper

Decision trees
neural network
Polynomials
Compressed sensing
Boolean functions

Online improper learning with an approximation oracle

Hazan, E., Jan 1 2018, In : Advances in Neural Information Processing Systems. 2018-December, p. 5652-5660 9 p.

Princeton University

Research output: Contribution to journalConference article

Approximation algorithms

Online learning of quantum states

Hazan, E., Jan 1 2018, In : Advances in Neural Information Processing Systems. 2018-December, p. 8962-8972 11 p.

Princeton University

Research output: Contribution to journalConference article

Convex optimization
Oils and fats

On the optimization of deep networks: Implicit acceleration by overparameterization

Arora, S. & Hazan, E., Jan 1 2018, 35th International Conference on Machine Learning, ICML 2018. Krause, A. & Dy, J. (eds.). International Machine Learning Society (IMLS), p. 372-389 18 p. (35th International Conference on Machine Learning, ICML 2018; vol. 1).

Princeton University

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Linear regression
Neural networks
Experiments
Deep learning