图 书 馆
基于随机嵌入的十亿维贝叶斯优化Bayesian Optimization in a Billion Dimensions via Random Embeddings |
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| 课程网址: | http://videolectures.net/lsoldm2013_de_freitas_random_embeddings/ |
| 主讲教师: | Nando de Freitas |
| 开课单位: | 牛津大学 |
| 开课时间: | 2013-11-07 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Bayesian optimization techniques have been successfully applied to robotics, planning, sensor placement, recommendation, advertising, intelligent user interfaces and automatic algorithm configuration. Despite these successes, the approach is restricted to problems of moderate dimension, and several workshops on Bayesian optimization have identified its scaling to high-dimensions as one of the holy grails of the field. In this paper, we introduce a novel random embedding idea to attack this problem. The resulting Random EMbedding Bayesian Optimization (REMBO) algorithm is very simple, has important invariance properties, and applies to domains with both categorical and continuous variables. We present a thorough theoretical analysis of REMBO, including regret bounds that only depend on the problem's intrinsic dimensionality. Empirical results confirm that REMBO can effectively solve problems with billions of dimensions, provided the intrinsic dimensionality is low. They also show that REMBO achieves state-of-the-art performance in optimizing the 47 discrete parameters of a popular mixed integer linear programming solver. |
| 关 键 词: | 优化技术; 随机嵌入; 嵌入思想 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-05-29：chenxin01 |
| 最后编审: | 2023-05-29：chenxin01 |
| 阅读次数: | 30 |