124 citations · 1.6k across the 112 of their papers we have counts for
6 papers · 1 filter
Learning in Non-convex Games with an Optimization Oracle
Naman Agarwal, Alon Gonen, Elad Hazan
We consider online learning in an adversarial, non-convex setting under the assumption that the learner has an access to an offline optimization oracle. In the general setting of p…
Efficient Full-Matrix Adaptive Regularization
Naman Agarwal, Brian Bullins, Xinyi Chen +4
Adaptive regularization methods pre-multiply a descent direction by a preconditioning matrix. Due to the large number of parameters of machine learning problems, full-matrix precon…
Online Improper Learning with an Approximation Oracle
Elad Hazan, Wei Hu, Yuanzhi Li +1
We revisit the question of reducing online learning to approximate optimization of the offline problem. In this setting, we give two algorithms with near-optimal performance in the…
Spectral Filtering for General Linear Dynamical Systems
Elad Hazan, Holden Lee, Karan Singh +2
We give a polynomial-time algorithm for learning latent-state linear dynamical systems without system identification, and without assumptions on the spectral radius of the system's…
Online Learning of Quantum States
Scott Aaronson, Xinyi Chen, Elad Hazan +2
Suppose we have many copies of an unknown -qubit state . We measure some copies of using a known two-outcome measurement , then other copies using a measurement $E…
On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization
Sanjeev Arora, Nadav Cohen, Elad Hazan
Conventional wisdom in deep learning states that increasing depth improves expressiveness but complicates optimization. This paper suggests that, sometimes, increasing depth can sp…