图 书 馆
跨未知环境的稳定预测Stable Prediction across Unknown Environments |
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| 课程网址: | http://videolectures.net/kdd2018_kuang_prediction_environments/ |
| 主讲教师: | Kun Kuang |
| 开课单位: | 清华大学计算机科学与技术系 |
| 开课时间: | 2018-11-23 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In many important machine learning applications, the training distribution used to learn a probabilistic classifier differs from the distribution on which the classifier will be used to make predictions. Traditional methods correct the distribution shift by reweighting training data with the ratio of the density between test and training data. However, in many applications training takes place without prior knowledge of the testing distribution. Recently, methods have been proposed to address the shift by learning the underlying causal structure, but those methods rely on diversity arising from multiple training data sets, and they further have complexity limitations in high dimensions. In this paper, we propose a novel Deep Global Balancing Regression (DGBR) algorithm to jointly optimize a deep auto-encoder model for feature selection and a global balancing model for stable prediction across unknown environments. The global balancing model constructs balancing weights that facilitate estimation of partial effects of features (holding fixed all other features), a problem that is challenging in high dimensions, and thus helps to identify stable, causal relationships between features and outcomes. The deep auto-encoder model is designed to reduce the dimensionality of the feature space, thus making global balancing easier. We show, both theoretically and with empirical experiments, that our algorithm can make stable predictions across unknown environments. Our experiments on both synthetic and real datasets demonstrate that our algorithm outperforms the state-of-the-art methods for stable prediction across unknown environments. |
| 关 键 词: | 重要的机器学习应用; 学习概率分类器; 进行预测的分布; 全局平衡模型 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-01-30：cyh |
| 最后编审: | 2023-01-31：cyh |
| 阅读次数: | 30 |