937 citations · 4k across the 76 of their papers we have counts for
8 papers · 1 filter
A Reliable Effective Terascale Linear Learning System
Alekh Agarwal, Olivier Chapelle, Miroslav Dudik +1
We present a system and a set of techniques for learning linear predictors with convex losses on terascale datasets, with trillions of features, {The number of features here refers…
The Generalization Ability of Online Algorithms for Dependent Data
Alekh Agarwal, John C. Duchi
We study the generalization performance of online learning algorithms trained on samples coming from a dependent source of data. We show that the generalization error of any stable…
Stochastic convex optimization with bandit feedback
Alekh Agarwal, Dean P. Foster, Daniel Hsu +2
This paper addresses the problem of minimizing a convex, Lipschitz function over a convex, compact set $\xset$ under a stochastic bandit feedback model. In this model, the algo…
Ergodic Mirror Descent
John C. Duchi, Alekh Agarwal, Mikael Johansson +1
We generalize stochastic subgradient descent methods to situations in which we do not receive independent samples from the distribution over which we optimize, but instead receive…
Distributed Delayed Stochastic Optimization
Alekh Agarwal, John C. Duchi
We analyze the convergence of gradient-based optimization algorithms that base their updates on delayed stochastic gradient information. The main application of our results is to t…
Fast global convergence of gradient methods for high-dimensional statistical recovery
Alekh Agarwal, Sahand N. Negahban, Martin J. Wainwright
Many statistical -estimators are based on convex optimization problems formed by the combination of a data-dependent loss function with a norm-based regularizer. We analyze the…