图 书 馆
并行随机时长实验的预算优化Budgeted Optimization with Concurrent Stochastic-Duration Experiments |
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| 课程网址: | http://videolectures.net/nips2011_azimi_experiments/ |
| 主讲教师: | Javad Azimi |
| 开课单位: | 俄勒冈州立大学 |
| 开课时间: | 2012-01-19 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Budgeted optimization involves optimizing an unknown function that is costly to evaluate by requesting a limited number of function evaluations at intelligently selected inputs. Typical problem formulations assume that experiments are selected one at a time with a limited total number of experiments, which fail to capture important aspects of many real-world problems. This paper defines a novel problem formulation with the following important extensions: 1) allowing for concurrent experiments; 2) allowing for stochastic experiment durations; and 3) placing constraints on both the total number of experiments and the total experimental time. We develop both offline and online algorithms for selecting concurrent experiments in this new setting and provide experimental results on a number of optimization benchmarks. The results show that our algorithms produce highly effective schedules compared to natural baselines. |
| 关 键 词: | 预算优化; 智能选择; 在线算法 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-07-26：cwx |
| 阅读次数: | 35 |