Abstract
Big Data applications are typically associated with systems involving large numbers of users, massive complex software systems, and large-scale heterogeneous computing and storage architectures. The construction of such systems involves many distributed design choices. The end products (e.g., recommendation systems, medical analysis tools, real-time game engines, speech recognizers) thus involve many tunable configuration parameters. These parameters are often specified and hard-coded into the software by various developers or teams. If optimized jointly, these parameters can result in significant improvements. Bayesian optimization is a powerful tool for the joint optimization of design choices that is gaining great popularity in recent years. It promises greater automation so as to increase both product quality and human productivity. This review paper introduces Bayesian optimization, highlights some of its methodological aspects, and showcases a wide range of applications.
| Original language | English (US) |
|---|---|
| Article number | 7352306 |
| Pages (from-to) | 148-175 |
| Number of pages | 28 |
| Journal | Proceedings of the IEEE |
| Volume | 104 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 2016 |
| Externally published | Yes |
All Science Journal Classification (ASJC) codes
- General Computer Science
- Electrical and Electronic Engineering
Keywords
- decision making
- design of experiments
- genomic medicine
- optimization
- response surface methodology
- statistical learning