Name
Abstract | ||
|---|---|---|
We propose a framework of boosting for learning and control in environments that maintain a state. Leveraging methods for online learning with memory and for online boosting, we design an efficient online algorithm that can provably improve the accuracy of weak-learners in stateful environments. As a consequence, we give efficient boosting algorithms for both prediction and the control of dynamical systems. Empirical evaluation on simulated and real data for both control and prediction supports our theoretical findings. |
Year | Venue | DocType |
|---|---|---|
2019 | CoRR | Journal |
Volume | Citations | PageRank |
abs/1906.08720 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Naman Agarwal | 1 | 23 | 3.82 |
| Nataly Brukhim | 2 | 1 | 2.04 |
| Elad Hazan | 3 | 8 | 1.19 |
| Zhou Lu | 4 | 0 | 0.68 |