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20112020
most citedLarge-Scale Convex Minimization with a Low-Rank Constraint

94 citations · 107 across the 5 of their papers we have counts for

collaborators

7 papers

cs.LG20203 cited

Towards a combinatorial characterization of bounded memory learning

Alon Gonen, Shachar Lovett, Michal Moshkovitz

Combinatorial dimensions play an important role in the theory of machine learning. For example, VC dimension characterizes PAC learning, SQ dimension characterizes weak learning wi…

cs.LG20191 cited

Private Learning Implies Online Learning: An Efficient Reduction

Alon Gonen, Elad Hazan, Shay Moran

We study the relationship between the notions of differentially private learning and online learning in games. Several recent works have shown that differentially private learning…

cs.LG2018

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…

cs.LG2018

Optimal Sketching Bounds for Exp-concave Stochastic Minimization

Naman Agarwal, Alon Gonen

We derive optimal statistical and computational complexity bounds for exp-concave stochastic minimization in terms of the effective dimension. For common eigendecay patterns of the…

cs.IT2016

Faster Low-rank Approximation using Adaptive Gap-based Preconditioning

Alon Gonen, Shai Shalev-Shwartz

We propose a method for rank approximation to a given input matrix which runs in time \[ \tilde{O} \left(d ~\cdot~ \min\left\{n + \tilde{sr}(X)…

cs.LG20159 cited

Strongly Adaptive Online Learning

Amit Daniely, Alon Gonen, Shai Shalev-Shwartz

Strongly adaptive algorithms are algorithms whose performance on every time interval is close to optimal. We present a reduction that can transform standard low-regret algorithms t…