Name
Abstract | ||
|---|---|---|
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, there has been a growing interest in understanding how more data can be leveraged to reduce the required training runtime. In this paper, we study the runtime of learning as a function of the number of available training examples, and underscore the main high-level techniques. We provide some initial positive results showing that the runtime can decrease exponentially while only requiring a polynomial growth of the number of examples, and spell-out several interesting open problems. |
Year | Venue | DocType |
|---|---|---|
2012 | AISTATS | Journal |
Volume | Citations | PageRank |
abs/1106.1216 | 12 | 0.81 |
References | Authors | |
13 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shai Shalev-Shwartz | 1 | 3681 | 276.32 |
| Ohad Shamir | 2 | 1627 | 119.03 |
| Eran Tromer | 3 | 2960 | 137.46 |