Name
Abstract | ||
|---|---|---|
This work provides simple algorithms for multi-class (and multi-label) prediction in settings where both the number of examples n and the data dimension d are relatively large. These robust and parameter free algorithms are essentially iterative least-squares updates and very versatile both in theory and in practice. On the theoretical front, we present several variants with convergence guarantees. Owing to their effective use of second-order structure, these algorithms are substantially better than first-order methods in many practical scenarios. On the empirical side, we present a scalable stagewise variant of our approach, which achieves dramatic computational speedups over popular optimization packages such as Liblinear and Vowpal Wabbit on standard datasets (MNIST and CIFAR-10), while attaining state-of-the-art accuracies. |
Year | Venue | DocType |
|---|---|---|
2013 | ICML | Journal |
Volume | Citations | PageRank |
abs/1310.1949 | 11 | 0.62 |
References | Authors | |
22 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alekh Agarwal | 1 | 1606 | 96.62 |
| Sham Kakade | 2 | 4365 | 282.77 |
| Nikos Karampatziakis | 3 | 517 | 24.63 |
| Le Song | 4 | 2437 | 159.27 |
| Gregory Valiant | 5 | 785 | 53.89 |