Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization | Paper Citation Record

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This paper cites “everyone wants to do the model work, not the data work.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization “everyone wants to do the model work, not the data work

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This paper cites Andrew ng, ai minimalist: The machine-learning pioneer says small is the new big,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Andrew ng, ai minimalist: The machine-learning pioneer says small is the new big,

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This paper cites Deep neural networks and tabular data: A survey,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Deep neural networks and tabular data: A survey,

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Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Data-centric Artificial Intelligence: A Survey

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This paper cites Benchmarking automl for regression tasks on small tabular data in materials design,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Benchmarking automl for regression tasks on small tabular data in materials design,

Reference 5

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This paper cites Towards Data-Centric AI: A Comprehensive Survey of Traditional, Reinforcement, and Generative Approaches for Tabular Data Transformation.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Towards Data-Centric AI: A Comprehensive Survey of Traditional, Reinforcement, and Generative Approaches for Tabular Data Transformation

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This paper cites Dong and H.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Dong and H

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This paper cites Learning feature engineering for classification.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Learning feature engineering for classification

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This paper cites Fastft: Accelerating reinforced feature transformation via advanced exploration strategies,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Fastft: Accelerating reinforced feature transformation via advanced exploration strategies,

Reference 9

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This paper cites Gradient boosted decision trees for high dimensional sparse output,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Gradient boosted decision trees for high dimensional sparse output,

Reference 10

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This paper cites Representation learning: A review and new perspectives,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Representation learning: A review and new perspectives,

Reference 11

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This paper cites Why do tree-based models still outperform deep learning on typical tabular data?.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Why do tree-based models still outperform deep learning on typical tabular data?

Reference 12

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This paper cites Tabular data: Deep learning is not all you need,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Tabular data: Deep learning is not all you need,

Reference 13

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Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Tabular Data Augmentation for Machine Learning: Progress and Prospects of Embracing Generative AI

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This paper cites Towards data-centric ai: A comprehensive survey of traditional, reinforcement, and generative approaches for tabular data transformation,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Towards data-centric ai: A comprehensive survey of traditional, reinforcement, and generative approaches for tabular data transformation,

Reference 15

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This paper cites Deep feature synthesis: Towards automating data science endeavors,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Deep feature synthesis: Towards automating data science endeavors,

Reference 16

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This paper cites Cognito: Automated feature engineering for supervised learning,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Cognito: Automated feature engineering for supervised learning,

Reference 17

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This paper cites The autofeat Python Library for Automated Feature Engineering and Selection.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization The autofeat Python Library for Automated Feature Engineering and Selection

Reference 18

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This paper cites Genetic programming for feature construction and selection in classification on high-dimensional data,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Genetic programming for feature construction and selection in classification on high-dimensional data,

Reference 19

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This paper cites Learning a data-driven policy network for pre-training automated fea- ture engineering,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Learning a data-driven policy network for pre-training automated fea- ture engineering,

Reference 20

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This paper cites An interpretable automated feature engineering framework for improving logistic regression,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization An interpretable automated feature engineering framework for improving logistic regression,

Reference 21

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This paper cites Group-wise reinforce- ment feature generation for optimal and explainable representation space reconstruction,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Group-wise reinforce- ment feature generation for optimal and explainable representation space reconstruction,

Reference 22

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This paper cites Traceable automatic feature transformation via cascading actor- critic agents,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Traceable automatic feature transformation via cascading actor- critic agents,

Reference 23

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This paper cites Traceable group-wise self-optimizing feature transformation learning: A dual optimization perspective,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Traceable group-wise self-optimizing feature transformation learning: A dual optimization perspective,

Reference 24

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This paper cites Self-optimizing feature generation via categorical hashing representation and hierarchical reinforcement crossing,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Self-optimizing feature generation via categorical hashing representation and hierarchical reinforcement crossing,

Reference 25

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This paper cites Feature engineering for predictive modeling using reinforcement learning,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Feature engineering for predictive modeling using reinforcement learning,

Reference 26

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This paper cites Difer: differentiable automated feature engineering,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Difer: differentiable automated feature engineering,

Reference 27

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This paper cites OpenFE: Automated feature generation with expert-level performance,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization OpenFE: Automated feature generation with expert-level performance,

Reference 28

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This paper cites Reinforcement- enhanced autoregressive feature transformation: Gradient-steered search in continuous space for postfix expressions,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Reinforcement- enhanced autoregressive feature transformation: Gradient-steered search in continuous space for postfix expressions,

Reference 29

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This paper cites Unsupervised Generative Feature Transformation via Graph Contrastive Pre-training and Multi-objective Fine-tuning.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Unsupervised Generative Feature Transformation via Graph Contrastive Pre-training and Multi-objective Fine-tuning

Reference 30

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This paper cites A comprehensive survey of multiagent reinforcement learning,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization A comprehensive survey of multiagent reinforcement learning,

Reference 31

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Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Cooperative multi-agent learning: The state of the art,

Reference 32

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This paper cites A tutorial on spectral clustering,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization A tutorial on spectral clustering,

Reference 33

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Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Modeling relational data with graph convolutional networks,

Reference 34

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This paper cites Kaggle dataset download,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Kaggle dataset download,

Reference 35

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Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Libsvm dataset download,

Reference 36

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Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Openml dataset download,

Reference 37

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Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Analysis of the automl challenge series 2015-2018,

Reference 38

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Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Uci dataset download,

Reference 39

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This paper cites Neural feature search: A neural architecture for automated feature engineering,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Neural feature search: A neural architecture for automated feature engineering,

Reference 40

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Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Pytorch: An imperative style, high- performance deep learning library,

Reference 41

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This paper cites Survey on categorical data for neural networks,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Survey on categorical data for neural networks,

Reference 42

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This paper cites Techniques for automated machine learning,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Techniques for automated machine learning,

Reference 43

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This paper cites One button machine for automating feature engineering in relational databases.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization One button machine for automating feature engineering in relational databases

Reference 44

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This paper cites Automating feature engineering,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Automating feature engineering,

Reference 45

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Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Explorekit: Automatic feature generation and selection,

Reference 46

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Observation e35c49ff-14d5-492b-921a-75d0806e43ab · outbound

This paper cites Strengthening learning algorithms by feature discovery,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Strengthening learning algorithms by feature discovery,

Reference 47

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This paper cites Evolutionary automated feature engineering,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Evolutionary automated feature engineering,

Reference 48

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This paper cites Mafsids: a reinforcement learning-based intrusion detection model for multi-agent feature selection networks,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Mafsids: a reinforcement learning-based intrusion detection model for multi-agent feature selection networks,

Reference 49

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This paper cites Autods: Towards human-centered automation of data science,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Autods: Towards human-centered automation of data science,

Reference 50

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This paper cites Beyond discrete selection: Continuous embedding space optimization for gener- ative feature selection,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Beyond discrete selection: Continuous embedding space optimization for gener- ative feature selection,

Reference 51

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This paper cites Chatgpt as your personal data scientist,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Chatgpt as your personal data scientist,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:46:51.086229Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f82047fe-4912-46a4-9afa-7643b9756541 · outbound

This paper cites On llms-driven synthetic data generation, curation, and evaluation: A survey,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization On llms-driven synthetic data generation, curation, and evaluation: A survey,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:46:51.074694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 124ac916-1100-4440-b324-acd82ae74f26 · outbound

This paper cites Dynamic and adaptive feature generation with llm,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Dynamic and adaptive feature generation with llm,

Reference 54

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Source-reported events for the cited work

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Observation 693030e2-3c47-4d1d-bc25-6d6f538f142d · outbound

This paper cites Large language models for automated data science: Introducing caafe for context-aware automated feature engineering,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Large language models for automated data science: Introducing caafe for context-aware automated feature engineering,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:46:51.063396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bc993f7e-3e25-473b-af93-604833578a8e · outbound

This paper cites LLM-Select: Feature Selection with Large Language Models.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization LLM-Select: Feature Selection with Large Language Models

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-16T10:46:50.725210Z

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Observation 137b8763-d784-4a54-a3f2-19f9f6b38cba · outbound

This paper cites GPT-4 Technical Report.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization GPT-4 Technical Report

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-16T10:46:50.729263Z

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Observation d0fdc7d3-7daf-4df5-a0cb-04fa58977315 · outbound

This paper cites Profet: Feature engineering captures high-level protein functions,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Profet: Feature engineering captures high-level protein functions,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:46:51.052541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c475c199-86c4-4ad3-a41d-261bd811c163 · outbound

This paper cites ilearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of dna, rna and protein sequence data,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization ilearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of dna, rna and protein sequence data,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:46:51.041776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c8ca7944-b783-4bb2-a872-a210bc8242b1 · outbound

This paper cites Eleven quick tips for data cleaning and feature engineering,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Eleven quick tips for data cleaning and feature engineering,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:46:51.029443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 191785c0-bea8-4fc0-bc20-c57b6f445831 · outbound

This paper cites Bioautoml: automated feature engineering and metalearning to predict noncoding rnas in bacteria,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Bioautoml: automated feature engineering and metalearning to predict noncoding rnas in bacteria,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:46:51.015320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 15f5afe8-67e2-440b-b0af-07e64ed566a1 · outbound

This paper cites Using machine learning and feature engineering to characterize limited material datasets of high-entropy alloys,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Using machine learning and feature engineering to characterize limited material datasets of high-entropy alloys,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:46:51.000824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d48c6c09-a12d-4c16-b41e-ac9f446e76a9 · outbound

This paper cites Machine learning approaches for feature engineering of the crystal structure: Application to the prediction of the formation energy of cubic compounds,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Machine learning approaches for feature engineering of the crystal structure: Application to the prediction of the formation energy of cubic compounds,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:46:50.987549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 309ad3fc-3e4a-46c0-948e-72a598357318 · outbound

This paper cites Physics-constrained automatic feature engineering for predictive modeling in materials science,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Physics-constrained automatic feature engineering for predictive modeling in materials science,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:46:50.975870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 46eb91bf-e475-45b5-9e9c-f0177404a677 · outbound

This paper cites Topological feature engineering for machine learning based halide perovskite materials design,.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Topological feature engineering for machine learning based halide perovskite materials design,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:46:50.963840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f727f91a-f9da-4b45-b486-c1dc1308a1ba · outbound

This paper cites 41 880–41 901.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization 41 880–41 901

Reference 202

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:46:51.315286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c50a1ff8-426d-4b46-9680-2fd7c1b5e762 · outbound

This paper cites Available: https://www.automl.org/wp-content/uploads/ 2018/09/chapter10-challenge.pdf.

Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Available: https://www.automl.org/wp-content/uploads/ 2018/09/chapter10-challenge.pdf

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:46:51.225421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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