# Reinforcement-Enhanced Autoregressive Feature Transformation: Gradient-steered Search in Continuous Space for Postfix Expressions

arXiv: https://arxiv.org/abs/2309.13618 · HTML: https://mlsanity.com/paper/2309.13618/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 34 references that are in the map.
- First posted on arXiv 24 Sep 2023.

## Builds on (most cited first, 15 of 15)

- [AutoML: A Survey of the State-of-the-Art](https://mlsanity.com/paper/1908.00709/) (2019)
- [Beam Search Strategies for Neural Machine Translation](https://mlsanity.com/paper/1702.01806/) (2017)
- [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)
- [Beyond Discrete Selection: Continuous Embedding Space Optimization for Generative Feature Selection](https://mlsanity.com/paper/2302.13221/) (2023)
- [Traceable Automatic Feature Transformation via Cascading Actor-Critic Agents](https://mlsanity.com/paper/2212.13402/) (2022)
- [Traceable Group-Wise Self-Optimizing Feature Transformation Learning: A Dual Optimization Perspective](https://mlsanity.com/paper/2306.16893/) (2023)
- [Techniques for Automated Machine Learning](https://mlsanity.com/paper/1907.08908/) (2019)
- [DIFER: Differentiable Automated Feature Engineering](https://mlsanity.com/paper/2010.08784/) (2020)

## Built on by (most cited first, 23 of 23)

- [A Comprehensive Survey on Data Augmentation](https://mlsanity.com/paper/2405.09591/) (2024)
- [Evolutionary Large Language Model for Automated Feature Transformation](https://mlsanity.com/paper/2405.16203/) (2024)
- [Unsupervised Generative Feature Transformation via Graph Contrastive Pre-training and Multi-objective Fine-tuning](https://mlsanity.com/paper/2405.16879/) (2024)
- [Tabular Data Augmentation for Machine Learning: Progress and Prospects of Embracing Generative AI](https://mlsanity.com/paper/2407.21523/) (2024)
- [Neuro-Symbolic Embedding for Short and Effective Feature Selection via Autoregressive Generation](https://mlsanity.com/paper/2404.17157/) (2024)
- [Reinforcement Feature Transformation for Polymer Property Performance Prediction](https://mlsanity.com/paper/2409.15616/) (2024)
- [Resolving the Imbalance Issue in Hierarchical Disciplinary Topic Inference via LLM-based Data Augmentation](https://mlsanity.com/paper/2310.05318/) (2023)
- [Unsupervised Feature Transformation via In-context Generation, Generator-critic LLM Agents, and Duet-play Teaming](https://mlsanity.com/paper/2504.21304/) (2025)
- [FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based Optimization](https://mlsanity.com/paper/2405.06312/) (2024)
- [Feature Interaction Aware Automated Data Representation Transformation](https://mlsanity.com/paper/2309.17011/) (2023)
- [Autonomous Data Agents: A New Opportunity for Smart Data](https://mlsanity.com/paper/2509.18710/) (2025)
- [Enhancing Tabular Data Optimization with a Flexible Graph-based Reinforced Exploration Strategy](https://mlsanity.com/paper/2406.07404/) (2024)
- [FastFT: Accelerating Reinforced Feature Transformation via Advanced Exploration Strategies](https://mlsanity.com/paper/2503.20394/) (2025)
- [Distribution Shift Aware Neural Tabular Learning](https://mlsanity.com/paper/2508.19486/) (2025)
- [DELTA: Variational Disentangled Learning for Privacy-Preserving Data Reprogramming](https://mlsanity.com/paper/2509.00693/) (2025)
- [Enhanced Gene Selection in Single-Cell Genomics: Pre-Filtering Synergy and Reinforced Optimization](https://mlsanity.com/paper/2406.07418/) (2024)
- [DALL-M: Context-Aware Clinical Data Augmentation with LLMs](https://mlsanity.com/paper/2407.08227/) (2024)
- [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)
- [GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement](https://mlsanity.com/paper/2508.20824/) (2025)
- [Dataforge: Agentic Platform for Autonomous Data Engineering](https://mlsanity.com/paper/2511.06185/) (2025)
- [Heterogeneous Multi-Agent Reinforcement Learning with Attention for Cooperative and Scalable Feature Transformation](https://mlsanity.com/paper/2511.21934/) (2025)

## Abstract (arXiv)

Feature transformation aims to generate new pattern-discriminative feature space from original features to improve downstream machine learning (ML) task performances. However, the discrete search space for the optimal feature explosively grows on the basis of combinations of features and operations from low-order forms to high-order forms. Existing methods, such as exhaustive search, expansion reduction, evolutionary algorithms, reinforcement learning, and iterative greedy, suffer from large search space. Overly emphasizing efficiency in algorithm design usually sacrifices stability or robustness. To fundamentally fill this gap, we reformulate discrete feature transformation as a continuous space optimization task and develop an embedding-optimization-reconstruction framework. This framework includes four steps: 1) reinforcement-enhanced data preparation, aiming to prepare high-quality transformation-accuracy training data; 2) feature transformation operation sequence embedding, intending to encapsulate the knowledge of prepared training data within a continuous space; 3) gradient-steered optimal embedding search, dedicating to uncover potentially superior embeddings within the learned space; 4) transformation operation sequence reconstruction, striving to reproduce the feature transformation solution to pinpoint the optimal feature space.