Classical machine learning methods may lead to poor prediction performance
when the target distribution differs from the source populations. This paper
utilizes data from multiple sources and introduces a group distributionally
robust prediction model defined to optimize an adversarial reward about
explained variance with respect to a class of target distributions. Compared to
classical empirical risk minimization, the proposed robust prediction model
improves the prediction accuracy for target populations with distribution
shifts. We show that our group distributionally robust prediction model is a
weighted average of the source populations' conditional outcome models. We
leverage this key identification result to robustify arbitrary machine learning
algorithms, including, for example, random forests and neural networks. We
devise a novel bias-corrected estimator to estimate the optimal aggregation
weight for general machine-learning algorithms and demonstrate its improvement
in the convergence rate. Our proposal can be seen as a distributionally robust
federated learning approach that is computationally efficient and easy to
implement using arbitrary machine learning base algorithms, satisfies some
privacy constraints, and has a nice interpretation of different sources'
importance for predicting a given target covariate distribution. We demonstrate
the performance of our proposed group distributionally robust method on
simulated and real data with random forests and neural networks as
base-learning algorithms.

利用多源数据建立组分布鲁棒预测模型以优化关于目标分布解释方差的对抗性奖励，提高了面对分布偏移的目标群体的预测准确性；该模型是源群体条件结果模型的加权平均，并通过引入偏差校正估计器提高了聚合权重的收敛速度，同时满足一些隐私约束并具有解释不同源数据对于预测给定目标协变量分布的重要性的优点。

基于多源数据的分布鲁棒机器学习

Distributionally Robust Machine Learning with Multi-source Data

Video data is with complex temporal dynamics due to various factors such as
camera motion, speed variation, and different activities. To effectively
capture this diverse motion pattern, this paper presents a new temporal
adaptive module ({\bf TAM}) to generate video-specific temporal kernels based
on its own feature map. TAM proposes a unique two-level adaptive modeling
scheme by decoupling the dynamic kernel into a location sensitive importance
map and a location invariant aggregation weight. The importance map is learned
in a local temporal window to capture short-term information, while the
aggregation weight is generated from a global view with a focus on long-term
structure. TAM is a modular block and could be integrated into 2D CNNs to yield
a powerful video architecture (TANet) with a very small extra computational
cost. The extensive experiments on Kinetics-400 and Something-Something
datasets demonstrate that our TAM outperforms other temporal modeling methods
consistently, and achieves the state-of-the-art performance under the similar
complexity. The code is available at \url{
this https URL}.

该研究提出了一种新的时间自适应模块（TAM），可生成基于其自身特征图的视频特定时间核，采用独特的两级自适应建模方案，通过将动态核解耦为位置敏感重要性地图和位置不变聚合权重，从本地时间窗口中学习重要性地图以捕捉短期信息，从全局视图中生成聚合权重，并成功应用于 TANet 架构中，从而实现卓越的性能表现。