To facilitate effective decision-making, gridded satellite precipitation
products should include uncertainty estimates. Machine learning has been
proposed for issuing such estimates. However, most existing algorithms for this
purpose rely on quantile regression. Distributional regression offers distinct
advantages over quantile regression, including the ability to model
intermittency as well as a stronger ability to extrapolate beyond the training
data, which is critical for predicting extreme precipitation. In this work, we
introduce the concept of distributional regression for the engineering task of
creating precipitation datasets through data merging. Building upon this
concept, we propose new ensemble learning methods that can be valuable not only
for spatial prediction but also for prediction problems in general. These
methods exploit conditional zero-adjusted probability distributions estimated
with generalized additive models for location, scale, and shape (GAMLSS),
spline-based GAMLSS and distributional regression forests as well as their
ensembles (stacking based on quantile regression, and equal-weight averaging).
To identify the most effective methods for our specific problem, we compared
them to benchmarks using a large, multi-source precipitation dataset. Stacking
emerged as the most successful strategy. Three specific stacking methods
achieved the best performance based on the quantile scoring rule, although the
ranking of these methods varied across quantile levels. This suggests that a
task-specific combination of multiple algorithms could yield significant
benefits.

通过分布回归和集成学习方法，可以在空间预测和其他预测问题中利用零调整概率分布来创建由数据合并生成的降水数据集，并提供不确定性估计。通过与基准模型的比较，确定了层叠方法是最有效的策略，而在特定的分位数水平下，具体的层叠方法会有所不同，因此使用多个算法进行任务特定的组合可能会带来显著的好处。

融合分布回归算法的卫星降水空间预测中的不确定性估计

Uncertainty estimation in satellite precipitation spatial prediction by  combining distributional regression algorithms

Compound Expression Recognition (CER) plays a crucial role in interpersonal
interactions. Due to the existence of Compound Expressions , human emotional
expressions are complex, requiring consideration of both local and global
facial expressions to make judgments. In this paper, to address this issue, we
propose a solution based on ensemble learning methods for Compound Expression
Recognition. Specifically, our task is classification, where we train three
expression classification models based on convolutional networks, Vision
Transformers, and multi-scale local attention networks. Then, through model
ensemble using late fusion, we merge the outputs of multiple models to predict
the final result. Our method achieves high accuracy on RAF-DB and is able to
recognize expressions through zero-shot on certain portions of C-EXPR-DB.

我们提出了一种基于集成学习方法的复合表情识别的解决方案，通过训练基于卷积网络、Vision Transformers 和多尺度局部注意力网络的三个表情分类模型，通过模型集成使用后期融合的方法，我们能够在 RAF-DB 上实现高准确率，并能够通过 C-EXPR-DB 的特定部分实现零样本表情识别。

通过多模型合奏进行复合表达式识别

Compound Expression Recognition via Multi Model Ensemble

Sepsis requires urgent diagnosis, but research is predominantly focused on
Western datasets. In this study, we perform a comparative analysis of two
ensemble learning methods, LightGBM and XGBoost, using the public eICU-CRD
dataset and a private South Korean St. Mary's Hospital's dataset. Our analysis
reveals the effectiveness of these methods in addressing healthcare data
imbalance and enhancing sepsis detection. Specifically, LightGBM shows a slight
edge in computational efficiency and scalability. The study paves the way for
the broader application of machine learning in critical care, thereby expanding
the reach of predictive analytics in healthcare globally.

本研究使用公共 eICU-CRD 数据集和韩国圣玛丽医院的私人数据集，对 LightGBM 和 XGBoost 两种集成学习方法进行比较分析，发现这些方法在处理医疗数据不平衡和增强脓毒症检测方面的有效性。具体而言，LightGBM 在计算效率和可扩展性方面略胜一筹。该研究为机器学习在危重护理中的广泛应用铺平了道路，从而全球扩展了预测分析在医疗保健中的应用。