Search Results for author: Kfir Levy

Found 5 papers, 0 papers with code

Online Convex Optimization in the Random Order Model

no code implementations ICML 2020 Dan Garber, Gal Korcia, Kfir Levy

Focusing on two important families of online tasks, one which generalizes online linear and logistic regression, and the other being online PCA, we show that under standard well-conditioned-data assumptions (that are often being made in the corresponding offline settings), standard online gradient descent (OGD) methods become much more efficient in the random-order model.

STORM+: Fully Adaptive SGD with Recursive Momentum for Nonconvex Optimization

no code implementations NeurIPS 2021 Kfir Levy, Ali Kavis, Volkan Cevher

In this work we propose $\rm{STORM}^{+}$, a new method that is completely parameter-free, does not require large batch-sizes, and obtains the optimal $O(1/T^{1/3})$ rate for finding an approximate stationary point.

Online to Offline Conversions, Universality and Adaptive Minibatch Sizes

no code implementations NeurIPS 2017 Kfir Levy

We present an approach towards convex optimization that relies on a novel scheme which converts adaptive online algorithms into offline methods.

Fast Rates for Exp-concave Empirical Risk Minimization

no code implementations NeurIPS 2015 Tomer Koren, Kfir Levy

In this setting, we establish the first evidence that ERM is able to attain fast generalization rates, and show that the expected loss of the ERM solution in $d$ dimensions converges to the optimal expected loss in a rate of $d/n$.

Stochastic Optimization

Bandit Convex Optimization: Towards Tight Bounds

no code implementations NeurIPS 2014 Elad Hazan, Kfir Levy

Bandit Convex Optimization (BCO) is a fundamental framework for decision making under uncertainty, which generalizes many problems from the realm of online and statistical learning.

Decision Making Decision Making Under Uncertainty

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