Tabular classification has traditionally relied on supervised algorithms,
which estimate the parameters of a prediction model using its training data.
Recently, Prior-Data Fitted Networks (PFNs) such as TabPFN have successfully
learned to classify tabular data in-context: the model parameters are designed
to classify new samples based on labelled training samples given after the
model training. While such models show great promise, their applicability to
real-world data remains limited due to the computational scale needed. Here we
study the following question: given a pre-trained PFN for tabular data, what is
the best way to summarize the labelled training samples before feeding them to
the model? We conduct an initial investigation of sketching and
feature-selection methods for TabPFN, and note certain key differences between
it and conventionally fitted tabular models.

给定一种预先训练的 TabPFN 对于表格数据，如何将标注的训练样本进行摘要以便输入模型是最佳的方法？我们对 TabPFN 进行了定购和特征选择方法的初步研究，并注意到它与传统拟合表格模型之间的某些关键差异。

为表格先验数据拟合网络缩放的草图和特征选择

Scaling TabPFN: Sketching and Feature Selection for Tabular Prior-Data  Fitted Networks

We present TabPFN, a trained Transformer that can do supervised
classification for small tabular datasets in less than a second, needs no
hyperparameter tuning and is competitive with state-of-the-art classification
methods. TabPFN is fully entailed in the weights of our network, which accepts
training and test samples as a set-valued input and yields predictions for the
entire test set in a single forward pass. TabPFN is a Prior-Data Fitted Network
(PFN) and is trained offline once, to approximate Bayesian inference on
synthetic datasets drawn from our prior. This prior incorporates ideas from
causal reasoning: It entails a large space of structural causal models with a
preference for simple structures. On the 18 datasets in the OpenML-CC18 suite
that contain up to 1 000 training data points, up to 100 purely numerical
features without missing values, and up to 10 classes, we show that our method
clearly outperforms boosted trees and performs on par with complex
state-of-the-art AutoML systems with up to 70$\times$ speedup. This increases
to a 3200$\times$ speedup when a GPU is available. We also validate these
results on an additional 67 small numerical datasets from OpenML. We provide
all our code, the trained TabPFN, an interactive browser demo and a Colab
notebook at this https URL

TabPFN 是一个训练有素的 Transformer，可对小型表格数据集进行监督分类，无需调整超参数，并且在与最先进的分类方法竞争中具有竞争力，它彻底包含在我们网络的权重中，并接受训练和测试样本以及一系列值输入，并在单个前向传递中为整个测试集产生预测，其先验扩展了因果推理的一些想法，适用于预测与给定输入和输出相关的结构因果模型。