7k citations
- University of California, Santa BarbaraUS71 papers
- University of Maryland, College ParkUS21 papers
- ETH ZurichCH18 papers
- University of California, BerkeleyUS18 papers
- California Institute of TechnologyUS17 papers
- University of California, Los AngelesUS13 papers
- Board of the Swiss Federal Institutes of TechnologyCH11 papers
- Microsoft Research (United Kingdom)GB10 papers
- Oak Ridge National LaboratoryUS8 papers
- Princeton UniversityUS8 papers
- University of Tennessee at KnoxvilleUS8 papers
- RWTH Aachen UniversityDE7 papers
Showing 2011 · cs.LGShow all
3 papers · 2 filters
cs.LG2011★ 150 cited
Better Mini-Batch Algorithms via Accelerated Gradient Methods
Andrew Cotter, Ohad Shamir, Nathan Srebro +1
Mini-batch algorithms have been proposed as a way to speed-up stochastic convex optimization problems. We study how such algorithms can be improved using accelerated gradient metho…
cs.LG2011★ 5 cited
Using More Data to Speed-up Training Time
Shai Shalev-Shwartz, Ohad Shamir, Eran Tromer
In many recent applications, data is plentiful. By now, we have a rather clear understanding of how more data can be used to improve the accuracy of learning algorithms. Recently,…
cs.LG2011★ 94 cited
Large-Scale Convex Minimization with a Low-Rank Constraint
Shai Shalev-Shwartz, Alon Gonen, Ohad Shamir
We address the problem of minimizing a convex function over the space of large matrices with low rank. While this optimization problem is hard in general, we propose an efficient g…