In this paper, we propose a new method for the augmentation of numeric and
mixed datasets. The method generates additional data points by utilizing
cross-validation resampling and latent variable modeling. It is particularly
efficient for datasets with moderate to high degrees of collinearity, as it
directly utilizes this property for generation. The method is simple, fast, and
has very few parameters, which, as shown in the paper, do not require specific
tuning. It has been tested on several real datasets; here, we report detailed
results for two cases, prediction of protein in minced meat based on near
infrared spectra (fully numeric data with high degree of collinearity) and
discrimination of patients referred for coronary angiography (mixed data, with
both numeric and categorical variables, and moderate collinearity). In both
cases, artificial neural networks were employed for developing the regression
and the discrimination models. The results show a clear improvement in the
performance of the models; thus for the prediction of meat protein, fitting the
model to the augmented data resulted in a reduction in the root mean squared
error computed for the independent test set by 1.5 to 3 times.

我们提出了一种新的方法来增加数字和混合数据集，通过利用交叉验证重采样和潜变量建模来生成额外的数据点。该方法对于具有中度到高度共线性的数据集特别有效，因为它直接利用了这个属性进行生成。该方法简单、快速，并且有很少的参数，在文中已经证明无需特定调整。我们对多个真实数据集进行了测试；在这里，我们报告了两种情况的详细结果，即基于近红外光谱的切碎肉中蛋白质的预测（完全数字数据，具有高度共线性）和因冠状动脉造影而被提及的患者的区分（混合数据，包括数字和分类变量，并具有中度共线性）。在这两种情况下，我们使用人工神经网络来开发回归和区分模型。结果显示模型性能有明显提高；因此，在预测肉类蛋白质时，拟合增强数据的模型可以将独立测试集的均方根误差减少 1.5 至 3 倍。

使用 Procrustes 验证集合进行共线数据集增强

Collinear datasets augmentation using Procrustes validation sets

Deep generative models have made tremendous progress in modeling complex
data, often exhibiting generation quality that surpasses a typical human's
ability to discern the authenticity of samples. Undeniably, a key driver of
this success is enabled by the massive amounts of web-scale data consumed by
these models. Due to these models' striking performance and ease of
availability, the web will inevitably be increasingly populated with synthetic
content. Such a fact directly implies that future iterations of generative
models must contend with the reality that their training is curated from both
clean data and artificially generated data from past models. In this paper, we
develop a framework to rigorously study the impact of training generative
models on mixed datasets (of real and synthetic data) on their stability. We
first prove the stability of iterative training under the condition that the
initial generative models approximate the data distribution well enough and the
proportion of clean training data (w.r.t. synthetic data) is large enough. We
empirically validate our theory on both synthetic and natural images by
iteratively training normalizing flows and state-of-the-art diffusion models on
CIFAR10 and FFHQ.

本文开发了一个框架，以严格研究在混合数据集（包括真实和合成数据）上训练生成模型对它们的稳定性的影响。通过在 CIFAR10 和 FFHQ 上迭代训练常规流和最先进的扩散模型，我们在合成和自然图像上经验验证了我们的理论。

关于迭代重新训练生成模型的稳定性

On the Stability of Iterative Retraining of Generative Models on their  own Data

Most offline reinforcement learning (RL) algorithms return a target policy
maximizing a trade-off between (1) the expected performance gain over the
behavior policy that collected the dataset, and (2) the risk stemming from the
out-of-distribution-ness of the induced state-action occupancy. It follows that
the performance of the target policy is strongly related to the performance of
the behavior policy and, thus, the trajectory return distribution of the
dataset. We show that in mixed datasets consisting of mostly low-return
trajectories and minor high-return trajectories, state-of-the-art offline RL
algorithms are overly restrained by low-return trajectories and fail to exploit
high-performing trajectories to the fullest. To overcome this issue, we show
that, in deterministic MDPs with stochastic initial states, the dataset
sampling can be re-weighted to induce an artificial dataset whose behavior
policy has a higher return. This re-weighted sampling strategy may be combined
with any offline RL algorithm. We further analyze that the opportunity for
performance improvement over the behavior policy correlates with the
positive-sided variance of the returns of the trajectories in the dataset. We
empirically show that while CQL, IQL, and TD3+BC achieve only a part of this
potential policy improvement, these same algorithms combined with our
reweighted sampling strategy fully exploit the dataset. Furthermore, we
empirically demonstrate that, despite its theoretical limitation, the approach
may still be efficient in stochastic environments. The code is available at
this https URL

通过对混合数据集进行重加权抽样，以产生一个具有更高回报的行为策略的人为数据集，offline reinforcement learning 有望从低回报的情况中脱颖而出，实现行为策略的性能改进。