| Autor: |
Hihn, Heinke, Braun, Daniel A. |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
Neural Processing Letters, 1-34, 2020 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1007/s11063-020-10351-3 |
| Popis: |
Joining multiple decision-makers together is a powerful way to obtain more sophisticated decision-making systems, but requires to address the questions of division of labor and specialization. We investigate in how far information constraints in hierarchies of experts not only provide a principled method for regularization but also to enforce specialization. In particular, we devise an information-theoretically motivated on-line learning rule that allows partitioning of the problem space into multiple sub-problems that can be solved by the individual experts. We demonstrate two different ways to apply our method: (i) partitioning problems based on individual data samples and (ii) based on sets of data samples representing tasks. Approach (i) equips the system with the ability to solve complex decision-making problems by finding an optimal combination of local expert decision-makers. Approach (ii) leads to decision-makers specialized in solving families of tasks, which equips the system with the ability to solve meta-learning problems. We show the broad applicability of our approach on a range of problems including classification, regression, density estimation, and reinforcement learning problems, both in the standard machine learning setup and in a meta-learning setting. |
| Databáze: |
arXiv |
| Externí odkaz: |
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