# Unsupervised Generative Feature Transformation via Graph Contrastive Pre-training and Multi-objective Fine-tuning arXiv: https://arxiv.org/abs/2405.16879 ยท HTML: https://mlsanity.com/paper/2405.16879/ ## Key facts - Cited by 21 papers in the map (data through 14 Oct 2026). - Builds on 15 of its 41 references that are in the map. - First posted on arXiv 27 May 2024. ## Builds on (most cited first, 15 of 15) - [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020) - [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018) - [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017) - [AutoML: A Survey of the State-of-the-Art](https://mlsanity.com/paper/1908.00709/) (2019) - [Feature Engineering for Predictive Modeling using Reinforcement Learning](https://mlsanity.com/paper/1709.07150/) (2017) - [The autofeat Python Library for Automated Feature Engineering and Selection](https://mlsanity.com/paper/1901.07329/) (2019) - [AutoML to Date and Beyond: Challenges and Opportunities](https://mlsanity.com/paper/2010.10777/) (2020) - [Automated Machine Learning on Graphs: A Survey](https://mlsanity.com/paper/2103.00742/) (2021) - [Group-wise Reinforcement Feature Generation for Optimal and Explainable Representation Space Reconstruction](https://mlsanity.com/paper/2205.14526/) (2022) - [One button machine for automating feature engineering in relational databases](https://mlsanity.com/paper/1706.00327/) (2017) - [AutoDS: Towards Human-Centered Automation of Data Science](https://mlsanity.com/paper/2101.05273/) (2021) - [Self-optimizing Feature Generation via Categorical Hashing Representation and Hierarchical Reinforcement Crossing](https://mlsanity.com/paper/2309.04612/) (2023) - [Reinforcement-Enhanced Autoregressive Feature Transformation: Gradient-steered Search in Continuous Space for Postfix Expressions](https://mlsanity.com/paper/2309.13618/) (2023) - [Techniques for Automated Machine Learning](https://mlsanity.com/paper/1907.08908/) (2019) - [Automated data processing and feature engineering for deep learning and big data applications: a survey](https://mlsanity.com/paper/2403.11395/) (2024) ## Built on by (most cited first, 21 of 21) - [GRAVER: Generative Graph Vocabularies for Robust Graph Foundation Models Fine-tuning](https://mlsanity.com/paper/2511.05592/) (2025) - [Topology-aware Reinforcement Feature Space Reconstruction for Graph Data](https://mlsanity.com/paper/2411.05742/) (2024) - [Unsupervised Feature Transformation via In-context Generation, Generator-critic LLM Agents, and Duet-play Teaming](https://mlsanity.com/paper/2504.21304/) (2025) - [GUME: Graphs and User Modalities Enhancement for Long-Tail Multimodal Recommendation](https://mlsanity.com/paper/2407.12338/) (2024) - [Continuous Optimization for Feature Selection with Permutation-Invariant Embedding and Policy-Guided Search](https://mlsanity.com/paper/2505.11601/) (2025) - [Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives](https://mlsanity.com/paper/2506.24124/) (2025) - [Autonomous Data Agents: A New Opportunity for Smart Data](https://mlsanity.com/paper/2509.18710/) (2025) - [FastFT: Accelerating Reinforced Feature Transformation via Advanced Exploration Strategies](https://mlsanity.com/paper/2503.20394/) (2025) - [LLM-ML Teaming: Integrated Symbolic Decoding and Gradient Search for Valid and Stable Generative Feature Transformation](https://mlsanity.com/paper/2506.09085/) (2025) - [Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback](https://mlsanity.com/paper/2505.15572/) (2025) - [DELTA: Variational Disentangled Learning for Privacy-Preserving Data Reprogramming](https://mlsanity.com/paper/2509.00693/) (2025) - [Permutation-Invariant Representation Learning for Robust and Privacy-Preserving Feature Selection](https://mlsanity.com/paper/2510.05535/) (2025) - [Towards Data-Centric AI: A Comprehensive Survey of Traditional, Reinforcement, and Generative Approaches for Tabular Data Transformation](https://mlsanity.com/paper/2501.10555/) (2025) - [Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization](https://mlsanity.com/paper/2504.17355/) (2025) - [Hierarchical and Permutation-Invariant Feature Transformation Learning via Policy-Guided Embedding Search](https://mlsanity.com/paper/2609.10225/) (2026) - [Building a Chinese Medical Dialogue System: Integrating Large-scale Corpora and Novel Models](https://mlsanity.com/paper/2410.03521/) (2024) - [Data-Efficient Symbolic Regression via Foundation Model Distillation](https://mlsanity.com/paper/2508.19487/) (2025) - [Dataforge: Agentic Platform for Autonomous Data Engineering](https://mlsanity.com/paper/2511.06185/) (2025) - [TreeNet: Layered Decision Ensembles](https://mlsanity.com/paper/2510.09654/) (2025) - [Heterogeneous Multi-Agent Reinforcement Learning with Attention for Cooperative and Scalable Feature Transformation](https://mlsanity.com/paper/2511.21934/) (2025) - [Multi-Agent Procedural Graph Extraction with Structural and Logical Refinement](https://mlsanity.com/paper/2601.19170/) (2026) ## Abstract (arXiv) Feature transformation is to derive a new feature set from original features to augment the AI power of data. In many science domains such as material performance screening, while feature transformation can model material formula interactions and compositions and discover performance drivers, supervised labels are collected from expensive and lengthy experiments. This issue motivates an Unsupervised Feature Transformation Learning (UFTL) problem. Prior literature, such as manual transformation, supervised feedback guided search, and PCA, either relies on domain knowledge or expensive supervised feedback, or suffers from large search space, or overlooks non-linear feature-feature interactions. UFTL imposes a major challenge on existing methods: how to design a new unsupervised paradigm that captures complex feature interactions and avoids large search space? To fill this gap, we connect graph, contrastive, and generative learning to develop a measurement-pretrain-finetune paradigm for UFTL. For unsupervised feature set utility measurement, we propose a feature value consistency preservation perspective and develop a mean discounted cumulative gain like unsupervised metric to evaluate feature set utility. For unsupervised feature set representation pretraining, we regard a feature set as a feature-feature interaction graph, and develop an unsupervised graph contrastive learning encoder to embed feature sets into vectors. For generative transformation finetuning, we regard a feature set as a feature cross sequence and feature transformation as sequential generation. We develop a deep generative feature transformation model that coordinates the pretrained feature set encoder and the gradient information extracted from a feature set utility evaluator to optimize a transformed feature generator.