Abstract
We present a new affine-invariant optimization algorithm called Online Lazy Newton. The regret of Online Lazy Newton is independent of conditioning: the algorithm's performance depends on the best possible preconditioning of the problem in retrospect and on its intrinsic dimensionality. As an application, we show how Online Lazy Newton can be used to achieve an optimal regret of order √rT for the low-rank experts problem, improving by a √r factor over the previously best known bound and resolving an open problem posed by Hazan et al. [15].
| Original language | English (US) |
|---|---|
| Pages (from-to) | 4748-4756 |
| Number of pages | 9 |
| Journal | Advances in Neural Information Processing Systems |
| Volume | 2017-December |
| State | Published - 2017 |
| Event | 31st Annual Conference on Neural Information Processing Systems, NIPS 2017 - Long Beach, United States Duration: Dec 4 2017 → Dec 9 2017 |
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Information Systems
- Signal Processing