Manifold learning (ML), known also as non-linear dimension reduction, is a
set of methods to find the low dimensional structure of data. Dimension
reduction for large, high dimensional data is not merely a way to reduce the
data; the new representations and descriptors obtained by ML reveal the
geometric shape of high dimensional point clouds, and allow one to visualize,
de-noise and interpret them. This survey presents the principles underlying ML,
the representative methods, as well as their statistical foundations from a
practicing statistician's perspective. It describes the trade-offs, and what
theory tells us about the parameter and algorithmic choices we make in order to
obtain reliable conclusions.

Manifold learning is a set of methods to find the low dimensional structure of data, allowing visualization, de-noising, and interpretation, with a focus on statistical foundations and parameter choices.

流形学习：是什么，怎么做，为什么

Manifold learning: what, how, and why

Graph embedding techniques are a staple of modern graph learning research.
When using embeddings for downstream tasks such as classification, information
about their stability and robustness, i.e., their susceptibility to sources of
noise, stochastic effects, or specific parameter choices, becomes increasingly
important. As one of the most prominent graph embedding schemes, we focus on
node2vec and analyse its embedding quality from multiple perspectives. Our
findings indicate that embedding quality is unstable with respect to parameter
choices, and we propose strategies to remedy this in practice.

分析了 node2vec 图嵌入策略在多个方面的嵌入质量，发现其嵌入质量对参数选择不稳定，并提出了实际上的解决方案。

node2vec 的出乎意料的行为

On the Surprising Behaviour of node2vec

Measuring bias is key for better understanding and addressing unfairness in
NLP/ML models. This is often done via fairness metrics which quantify the
differences in a model's behaviour across a range of demographic groups. In
this work, we shed more light on the differences and similarities between the
fairness metrics used in NLP. First, we unify a broad range of existing metrics
under three generalized fairness metrics, revealing the connections between
them. Next, we carry out an extensive empirical comparison of existing metrics
and demonstrate that the observed differences in bias measurement can be
systematically explained via differences in parameter choices for our
generalized metrics.

本文提出了三个广义公平度量标准，系统分析了不同的参数选择如何导致公平度量方法之间的差异，以更好地了解和处理 NLP/ML 模型中的不公平性。

量化自然语言处理中的社会偏见：对外在公平度量的一般化及实证比较

Quantifying Social Biases in NLP: A Generalization and Empirical  Comparison of Extrinsic Fairness Metrics

Deep Metric Learning (DML) is arguably one of the most influential lines of
research for learning visual similarities with many proposed approaches every
year. Although the field benefits from the rapid progress, the divergence in
training protocols, architectures, and parameter choices make an unbiased
comparison difficult. To provide a consistent reference point, we revisit the
most widely used DML objective functions and conduct a study of the crucial
parameter choices as well as the commonly neglected mini-batch sampling
process. Under consistent comparison, DML objectives show much higher
saturation than indicated by literature. Further based on our analysis, we
uncover a correlation between the embedding space density and compression to
the generalization performance of DML models. Exploiting these insights, we
propose a simple, yet effective, training regularization to reliably boost the
performance of ranking-based DML models on various standard benchmark datasets.
Code and a publicly accessible WandB-repo are available at
this https URL

本文通过对深度度量学习算法的客观比较研究，发现了深度度量学习算法饱和度高于文献中表明的水平，并揭示了嵌入空间密度与模型泛化性能之间的相关性，并提出了一种简单有效的训练正则化方法来提高基于排名的深度度量学习模型的性能。