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
6 papers · 1 filter
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…
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,…
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…
Adapting to the Shifting Intent of Search Queries
Umar Syed, Aleksandrs Slivkins, Nina Mishra
Search engines today present results that are often oblivious to abrupt shifts in intent. For example, the query `independence day' usually refers to a US holiday, but the intent o…
Query Strategies for Evading Convex-Inducing Classifiers
Blaine Nelson, Benjamin I. P. Rubinstein, Ling Huang +4
Classifiers are often used to detect miscreant activities. We study how an adversary can systematically query a classifier to elicit information that allows the adversary to evade…
On the Stability of Empirical Risk Minimization in the Presence of Multiple Risk Minimizers
Benjamin I. P. Rubinstein, Aleksandr Simma
Recently Kutin and Niyogi investigated several notions of algorithmic stability--a property of a learning map conceptually similar to continuity--showing that training-stability is…