论文标题
有效学习非线性预测模型，并具有时间序列特权信息
Efficient learning of nonlinear prediction models with time-series privileged information
论文作者
论文摘要
在样本量有限的域中，有效的学习算法至关重要。使用特权信息（LUPI）学习，通过允许预测模型在培训时间访问辅助信息，从而提高了样本效率，而在使用模型时，这是不可用的。在最近的工作中，结果表明，对于线性高斯动力学系统的预测，具有中间时间序列数据访问的卢比学习者永远不会比任何公正的经典学习者更糟糕，而且通常更好。我们为此分析提供了新的见解，并将其推广到潜在动力学系统中的非线性预测任务，从而将理论保证扩展到连接潜在变量和观察值的地图已知到线性变换的情况下。此外，我们提出了基于随机特征和表示该地图未知的情况的表示算法。一套经验结果证实了理论发现，并显示了在非线性预测中使用特权时间序列信息的潜力。
In domains where sample sizes are limited, efficient learning algorithms are critical. Learning using privileged information (LuPI) offers increased sample efficiency by allowing prediction models access to auxiliary information at training time which is unavailable when the models are used. In recent work, it was shown that for prediction in linear-Gaussian dynamical systems, a LuPI learner with access to intermediate time series data is never worse and often better in expectation than any unbiased classical learner. We provide new insights into this analysis and generalize it to nonlinear prediction tasks in latent dynamical systems, extending theoretical guarantees to the case where the map connecting latent variables and observations is known up to a linear transform. In addition, we propose algorithms based on random features and representation learning for the case when this map is unknown. A suite of empirical results confirm theoretical findings and show the potential of using privileged time-series information in nonlinear prediction.
