Name
Abstract | ||
|---|---|---|
We propose an online convex optimization algorithm (RESCALEDEXP) that achieves optimal regret in the unconstrained setting without prior knowledge of any bounds on the loss functions. We prove a lower bound showing an exponential separation between the regret of existing algorithms that require a known bound on the loss functions and any algorithm that does not require such knowledge. RESCALEDEXP matches this lower bound asymptotically in the number of iterations. RESCALEDEXP is naturally hyperparameter-free and we demonstrate empirically that it matches prior optimization algorithms that require hyperparameter optimization. |
Year | Venue | DocType |
|---|---|---|
2017 | ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 29 (NIPS 2016) | Journal |
Volume | ISSN | Citations |
29 | 1049-5258 | 3 |
PageRank | References | Authors |
0.38 | 8 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cutkosky, Ashok | 1 | 14 | 10.02 |
| K Boahen | 2 | 1188 | 160.52 |