BayesianOptimization(BO) is a sample-efficient black-box optimizer, and
extensive methods have been proposed to build the absolute function response of
the black-box function through a probabilistic surrogate model, including
Tree-structured Parzen Estimator (TPE), random forest (SMAC), and Gaussian
process (GP). However, few methods have been explored to estimate the relative
rankings of candidates, which can be more robust to noise and have better
practicality than absolute function responses, especially when the function
responses are intractable but preferences can be acquired. To this end, we
propose a novel ranking-based surrogate model based on the Poisson process and
introduce an efficient BO framework, namely Poisson Process Bayesian
Optimization (PoPBO). Two tailored acquisition functions are further derived
from classic LCB and EI to accommodate it. Compared to the classic GP-BO
method, our PoPBO has lower computation costs and better robustness to noise,
which is verified by abundant experiments. The results on both simulated and
real-world benchmarks, including hyperparameter optimization (HPO) and neural
architecture search (NAS), show the effectiveness of PoPBO.

基于泊松过程的贝叶斯优化方法，提出了一种基于排名的替代模型。通过两个定制的获取函数，解决了传统方法中的计算成本高和对噪音敏感的问题，并在模拟和真实世界基准测试中证明了其有效性。

贝叶斯优化的泊松过程

Poisson Process for Bayesian Optimization

We propose average Localisation-Recall-Precision (aLRP), a unified, bounded,
balanced and ranking-based loss function for both classification and
localisation tasks in object detection. aLRP extends the
Localisation-Recall-Precision (LRP) performance metric (Oksuz et al., 2018)
inspired from how Average Precision (AP) Loss extends precision to a
ranking-based loss function for classification (Chen et al., 2020). aLRP has
the following distinct advantages: (i) aLRP is the first ranking-based loss
function for both classification and localisation tasks. (ii) Thanks to using
ranking for both tasks, aLRP naturally enforces high-quality localisation for
high-precision classification. (iii) aLRP provides provable balance between
positives and negatives. (iv) Compared to on average $\sim$6 hyperparameters in
the loss functions of state-of-the-art detectors, aLRP Loss has only one
hyperparameter, which we did not tune in practice. On the COCO dataset, aLRP
Loss improves its ranking-based predecessor, AP Loss, up to around $5$ AP
points, achieves $48.9$ AP without test time augmentation and outperforms all
one-stage detectors. Code available at: this https URL .

本文提出了一个统一，有界并平衡的基于排名的损失函数：平均定位 - 召回 - 精度 (aLRP)，用于目标检测中的分类和定位任务，它具有排名和平衡的优点，可自然增强高质量的定位。

一种基于排名的平衡损失函数，统一了对象检测中的分类和定位

A Ranking-based, Balanced Loss Function Unifying Classification and  Localisation in Object Detection

Extreme Multi-label classification (XML) is an important yet challenging
machine learning task, that assigns to each instance its most relevant
candidate labels from an extremely large label collection, where the numbers of
labels, features and instances could be thousands or millions. XML is more and
more on demand in the Internet industries, accompanied with the increasing
business scale / scope and data accumulation. The extremely large label
collections yield challenges such as computational complexity, inter-label
dependency and noisy labeling. Many methods have been proposed to tackle these
challenges, based on different mathematical formulations. In this paper, we
propose a deep learning XML method, with a word-vector-based self-attention,
followed by a ranking-based AutoEncoder architecture. The proposed method has
three major advantages: 1) the autoencoder simultaneously considers the
inter-label dependencies and the feature-label dependencies, by projecting
labels and features onto a common embedding space; 2) the ranking loss not only
improves the training efficiency and accuracy but also can be extended to
handle noisy labeled data; 3) the efficient attention mechanism improves
feature representation by highlighting feature importance. Experimental results
on benchmark datasets show the proposed method is competitive to
state-of-the-art methods.

本文提出了基于深度学习的 XML 方法，采用基于词向量的自注意力和基于排序的自编码器，不仅能处理特征和标签之间的依赖关系，同时也可以考虑标签之间的依赖关系，该方法在标准数据集上表现优异。