用模仿学习自动分析数据，无需人工定义奖励函数。
ILAEDA: An Imitation Learning Based Approach for Automatic Exploratory Data Analysis
- 通过专家数据分析记录学习操作序列，替代传统奖励设计。
- 在基准测试中性能最高提升3倍，跨数据集泛化能力强。
- 适合需要快速探索数据的工程师和研究人员使用。
自动化端到端探索性数据分析（AutoEDA）是开放性难题，现有方法多基于强化学习，通过预测分析操作序列（如FILTER、GROUP）来实现。但为每个操作定义奖励函数极具挑战，通常依赖各类‘有趣性度量’构造奖励函数。本文认为，仅靠数学建模难以准确捕捉操作重要性的全部特征。为此，我们提出一种基于生成对抗模仿学习（GAIL）的AutoEDA模型，直接从专家数据分析会话中学习，避免了手动设计有趣性度量。该方法在少量专家数据下仍具备良好泛化能力，并引入一种生成合成数据分析演示的新方法。实验表明，本方法在基准测试上性能较现有最先进方法最高提升3倍，展现出优异性能与泛化性，且自然捕获了多样化的有趣性特征。
原文摘要 · Abstract (English)
Automating end-to-end Exploratory Data Analysis (AutoEDA) is a challenging open problem, often tackled through Reinforcement Learning (RL) by learning to predict a sequence of analysis operations (FILTER, GROUP, etc). Defining rewards for each operation is a challenging task and existing methods rely on various \emph{interestingness measures} to craft reward functions to capture the importance of each operation. In this work, we argue that not all of the essential features of what makes an operation important can be accurately captured mathematically using rewards. We propose an AutoEDA model trained through imitation learning from expert EDA sessions, bypassing the need for manually defined interestingness measures. Our method, based on generative adversarial imitation learning (GAIL), generalizes well across datasets, even with limited expert data. We also introduce a novel approach for generating synthetic EDA demonstrations for training. Our method outperforms the existing state-of-the-art end-to-end EDA approach on benchmarks by upto 3x, showing strong performance and generalization, while naturally capturing diverse interestingness measures in generated EDA sessions.
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