Predict-then-Optimize is a framework for using machine learning to perform
decision-making under uncertainty. The central research question it asks is,
"How can the structure of a decision-making task be used to tailor ML models
for that specific task?" To this end, recent work has proposed learning
task-specific loss functions that capture this underlying structure. However,
current approaches make restrictive assumptions about the form of these losses
and their impact on ML model behavior. These assumptions both lead to
approaches with high computational cost, and when they are violated in
practice, poor performance. In this paper, we propose solutions to these
issues, avoiding the aforementioned assumptions and utilizing the ML model's
features to increase the sample efficiency of learning loss functions. We
empirically show that our method achieves state-of-the-art results in four
domains from the literature, often requiring an order of magnitude fewer
samples than comparable methods from past work. Moreover, our approach
outperforms the best existing method by nearly 200% when the localness
assumption is broken.

本文提出了一种使用机器学习进行决策的方法 —— Predict-then-Optimize。 在这个方法中，学习任务特定的损失函数是核心问题，实验结果表明我们的方法在四个领域中均取得了最好的结果，并且需要的样本数比过去的方法少一个数量级。

离开巢穴：超越本地损失函数的预测优化

Leaving the Nest: Going Beyond Local Loss Functions for  Predict-Then-Optimize

Unsupervised domain adaptation (UDA) for nuclei instance segmentation is
important for digital pathology, as it alleviates the burden of labor-intensive
annotation and domain shift across datasets. In this work, we propose a Cycle
Consistency Panoptic Domain Adaptive Mask R-CNN (CyC-PDAM) architecture for
unsupervised nuclei segmentation in histopathology images, by learning from
fluorescence microscopy images. More specifically, we first propose a nuclei
inpainting mechanism to remove the auxiliary generated objects in the
synthesized images. Secondly, a semantic branch with a domain discriminator is
designed to achieve panoptic-level domain adaptation. Thirdly, in order to
avoid the influence of the source-biased features, we propose a task
re-weighting mechanism to dynamically add trade-off weights for the
task-specific loss functions. Experimental results on three datasets indicate
that our proposed method outperforms state-of-the-art UDA methods
significantly, and demonstrates a similar performance as fully supervised
methods.

本文提出了一种基于 Cycle Consistency Panoptic Domain Adaptive Mask R-CNN 架构的无监督核分割方法，该方法利用荧光显微镜图像进行学习，并引入对源域的偏置特征的动态修正机制，以实现跨数据集的域适应，实验结果表明，该方法在三个数据集上优于最先进的无监督域自适应方法，同时表现出类似于完全监督方法的性能。