In this work, we investigate the potential of large language models (LLMs)
based agents to automate data science tasks, with the goal of comprehending
task requirements, then building and training the best-fit machine learning
models. Despite their widespread success, existing LLM agents are hindered by
generating unreasonable experiment plans within this scenario. To this end, we
present DS-Agent, a novel automatic framework that harnesses LLM agent and
case-based reasoning (CBR). In the development stage, DS-Agent follows the CBR
framework to structure an automatic iteration pipeline, which can flexibly
capitalize on the expert knowledge from Kaggle, and facilitate consistent
performance improvement through the feedback mechanism. Moreover, DS-Agent
implements a low-resource deployment stage with a simplified CBR paradigm to
adapt past successful solutions from the development stage for direct code
generation, significantly reducing the demand on foundational capabilities of
LLMs. Empirically, DS-Agent with GPT-4 achieves an unprecedented 100% success
rate in the development stage, while attaining 36% improvement on average one
pass rate across alternative LLMs in the deployment stage. In both stages,
DS-Agent achieves the best rank in performance, costing \$1.60 and \$0.13 per
run with GPT-4, respectively.

DS-Agent 基于大型语言模型研究数据科学任务自动化，通过利用案例推理框架实现了任务需求理解、机器学习模型构建和训练，并通过低资源部署阶段显著降低了 LLMs 基本能力的要求，从而在性能上取得了最佳排名和显著改进。

DS-Agent：基于案例推理的大型语言模型的自动化数据科学

DS-Agent: Automated Data Science by Empowering Large Language Models  with Case-Based Reasoning

Synthetic population generation is the process of combining multiple
socioeconomic and demographic datasets from different sources and/or
granularity levels, and downscaling them to an individual level. Although it is
a fundamental step for many data science tasks, an efficient and standard
framework is absent. In this study, we propose a multi-stage framework called
SynC (Synthetic Population via Gaussian Copula) to fill the gap. SynC first
removes potential outliers in the data and then fits the filtered data with a
Gaussian copula model to correctly capture dependencies and marginal
distributions of sampled survey data. Finally, SynC leverages predictive models
to merge datasets into one and then scales them accordingly to match the
marginal constraints. We make three key contributions in this work: 1) propose
a novel framework for generating individual level data from aggregated data
sources by combining state-of-the-art machine learning and statistical
techniques, 2) demonstrate its value as a feature engineering tool, as well as
an alternative to data collection in situations where gathering is difficult
through two real-world datasets, 3) release an easy-to-use framework
implementation for reproducibility, and 4) ensure the methodology is scalable
at the production level and can easily incorporate new data.

本研究提出了一个名为 SynC (Synthetic Population via Gaussian Copula) 的多阶段框架，以从聚合数据源中生成个人级数据，并证明其价值不仅是特征工程工具，而且在收集困难的情况下，还可以作为一种收集数据的替代方法。通过两个真实数据集，本研究展示了其功能，并发布了易于使用的框架实现，并确保该方法可以扩展到生产级别并可以轻松集成新数据。

SynC: 一种使用高斯 Copula 生成合成人口的统一框架

SynC: A Unified Framework for Generating Synthetic Population with  Gaussian Copula

Today's conversational agents are restricted to simple standalone commands.
In this paper, we present Iris, an agent that draws on human conversational
strategies to combine commands, allowing it to perform more complex tasks that
it has not been explicitly designed to support: for example, composing one
command to "plot a histogram" with another to first "log-transform the data".
To enable this complexity, we introduce a domain specific language that
transforms commands into automata that Iris can compose, sequence, and execute
dynamically by interacting with a user through natural language, as well as a
conversational type system that manages what kinds of commands can be combined.
We have designed Iris to help users with data science tasks, a domain that
requires support for command combination. In evaluation, we find that data
scientists complete a predictive modeling task significantly faster (2.6 times
speedup) with Iris than a modern non-conversational programming environment.
Iris supports the same kinds of commands as today's agents, but empowers users
to weave together these commands to accomplish complex goals.

本文介绍了 Iris，一个利用人类交谈策略将命令组合起来以执行更复杂任务的代理系统，并且引入了一种领域特定的语言，用于将命令转化为 Iris 可以动态地组合、排序和执行的自动机，在评估中，我们发现数据科学家使用 Iris 完成预测建模任务的速度显著提高（2.6 倍加速）， Iris 支持与今天的代理系统相同的命令类型，但赋予用户将这些命令编织在一起以实现复杂目标的能力。