When data are right-censored, i.e. some outcomes are missing due to a limited
period of observation, survival analysis can compute the "time to event".
Multiple classes of outcomes lead to a classification variant: predicting the
most likely event, known as competing risks, which has been less studied. To
build a loss that estimates outcome probabilities for such settings, we
introduce a strictly proper censoring-adjusted separable scoring rule that can
be optimized on a subpart of the data because the evaluation is made
independently of observations. It enables stochastic optimization for competing
risks which we use to train gradient boosting trees. Compared to 11
state-of-the-art models, this model, MultiIncidence, performs best in
estimating the probability of outcomes in survival and competing risks. It can
predict at any time horizon and is much faster than existing alternatives.

当数据存在右侧截断情况时，生存分析可以计算 “事件发生时间”。多类别的结果导致了一种分类变种：预测最可能的事件，即已知的竞争风险，这方面的研究较少。为了构建一个估计这种情况下的结果概率的损失函数，我们引入了一个严格正确的修正分离的评分规则，这个规则可以在部分数据上进行优化，因为评估是独立于观测的。它可以用于训练梯度提升树来进行竞争风险分析。与 11 种现有模型相比，这个模型 “MultiIncidence” 在生存和竞争风险的结果概率估计上表现最佳。它可以在任何时间点进行预测，并且比现有的替代方法快得多。

教授模型生存力：适当评分规则和具有竞争风险的随机优化

Teaching Models To Survive: Proper Scoring Rule and Stochastic  Optimization with Competing Risks

While deep learning succeeds in a wide range of tasks, it highly depends on
the massive collection of annotated data which is expensive and time-consuming.
To lower the cost of data annotation, active learning has been proposed to
interactively query an oracle to annotate a small proportion of informative
samples in an unlabeled dataset. Inspired by the fact that the samples with
higher loss are usually more informative to the model than the samples with
lower loss, in this paper we present a novel deep active learning approach that
queries the oracle for data annotation when the unlabeled sample is believed to
incorporate high loss. The core of our approach is a measurement Temporal
Output Discrepancy (TOD) that estimates the sample loss by evaluating the
discrepancy of outputs given by models at different optimization steps. Our
theoretical investigation shows that TOD lower-bounds the accumulated sample
loss thus it can be used to select informative unlabeled samples. On basis of
TOD, we further develop an effective unlabeled data sampling strategy as well
as an unsupervised learning criterion that enhances model performance by
incorporating the unlabeled data. Due to the simplicity of TOD, our active
learning approach is efficient, flexible, and task-agnostic. Extensive
experimental results demonstrate that our approach achieves superior
performances than the state-of-the-art active learning methods on image
classification and semantic segmentation tasks.

本文提出了一种利用时间输出差异度来估计样本损失的深度主动学习方法，该方法可以从未标注的数据中选择最具信息量的样本，并且在图像分类和语义分割任务上相较于最先进的主动学习方法取得了更好的性能。