mlsanityData to 14 Oct 2026

A knowledge-driven AutoML architecture

arXiv 2311.17124 ↗First posted 28 Nov 2023cs.LG cs.AI cs.SEcited by 0

Not on the map. Why?

Abstract (arXiv)

This paper proposes a knowledge-driven AutoML architecture for pipeline and deep feature synthesis. The main goal is to render the AutoML process explainable and to leverage domain knowledge in the synthesis of pipelines and features. The architecture explores several novel ideas: first, the construction of pipelines and deep features is approached in an unified way. Next, synthesis is driven by a shared knowledge system, interactively queried as to what pipeline operations to use or features to compute. Lastly, the synthesis processes takes decisions at runtime using partial solutions and results of their application on data. Two experiments are conducted to demonstrate the functionality of a na"ive implementation of the proposed architecture and to discuss its advantages, trade-offs as well as future potential for AutoML.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 45 references that are in the map. First posted on arXiv 28 Nov 2023.

Builds on

24 of 45 references.

References · 24

  1. 2020Meta-Learning in Neural Networks: A Surveycited by 950
  2. 2019AutoML: A Survey of the State-of-the-Artcited by 390
  3. 2021Interpretable Machine Learning: Fundamental Principles and 10 Grand Challengescited by 367
  4. 2018Meta-Learning: A Surveycited by 256
  5. 2019Explainable Machine Learning for Scientific Insights and Discoveriescited by 166
  6. 2018Efficient Multi-objective Neural Architecture Search via Lamarckian Evolutioncited by 153
  7. 2021Demystifying the Draft EU Artificial Intelligence Actcited by 84
  8. 2020AutoML to Date and Beyond: Challenges and Opportunitiescited by 63
  9. 2018Automated Machine Learning: From Principles to Practicescited by 62
  10. 2019An ADMM Based Framework for AutoML Pipeline Configurationcited by 47
  11. 2020Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systemscited by 36
  12. 2017One button machine for automating feature engineering in relational databasescited by 32
12 more
  1. 2019Automated Machine Learning: State-of-The-Art and Open Challengescited by 29
  2. 2019Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Toolscited by 25
  3. 2020Amazon SageMaker Autopilot: a white box AutoML solution at scalecited by 20
  4. 2021PyGlove: Symbolic Programming for Automated Machine Learningcited by 19
  5. 2021Automated Evolutionary Approach for the Design of Composite Machine Learning Pipelinescited by 13
  6. 2020MLJ: A Julia package for composable machine learningcited by 9
  7. 2019Multi-Objective Automatic Machine Learning with AutoxgboostMCcited by 7
  8. 2017Knowledge Representation in Bicategories of Relationscited by 4
  9. 2020Evolution of Scikit-Learn Pipelines with Dynamic Structured Grammatical Evolutioncited by 4
  10. 2018Teaching machines to understand data science code by semantic enrichment of dataflow graphscited by 3
  11. 2020Efficient AutoML Pipeline Search with Matrix and Tensor Factorizationcited by 3
  12. 2020Flexible model composition in machine learning and its implementation in MLJcited by 3

Built on by

Not cited yet.