Key papers

• The Boundaries of Automation: A Theory of Persistent Human Participation (2026)
  Fares Fourati et al.
  4.39
• Pre-Hoc Predictions in AutoML: Leveraging LLMs to Enhance Model Selection and Benchmarking for Tabular datasets (2025)
  Yannis Belkhiter et al.
  1.50
• EnvX: Agentize Everything with Agentic AI (2025)
  Linyao Chen et al.
  1.44
• API Agents vs. GUI Agents: Divergence and Convergence (2025)
  Chaoyun Zhang et al.
  1.28
• AutoML Benchmark with shorter time constraints and early stopping (2025)
  Israel Campero Jurado et al.
  1.17
• EDCA - An Evolutionary Data-Centric AutoML Framework for Efficient Pipelines (2025)
  Joana Sim\~oes and Jo\~ao Correia
  1.11
• Fast Data Aware Neural Architecture Search via Supernet Accelerated Evaluation (2025)
  Emil Njor et al.
  1.06
• Online Meta-learning for AutoML in Real-time (OnMAR) (2025)
  Mia Gerber et al.
  1.06
• AutoQML: A Framework for Automated Quantum Machine Learning (2025)
  Marco Roth et al.
  1.06
• Identifying and Harnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation Tool (2016)
  Randal S. Olson and Jason H. Moore
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• An ADMM Based Framework for AutoML Pipeline Configuration (2019)
  Sijia Liu et al.
  —
• AutoML using Metadata Language Embeddings (2019)
  Iddo Drori et al.
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• AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data (2020)
  Nick Erickson et al.
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• Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning (2020)
  Matthias Feurer et al.
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• Can AutoML outperform humans? An evaluation on popular OpenML datasets using AutoML Benchmark (2020)
  Marc Hanussek et al.
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• AutoML to Date and Beyond: Challenges and Opportunities (2020)
  Shubhra Kanti Karmaker Santu et al.
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• Interpret-able feedback for AutoML systems (2021)
  Behnaz Arzani et al.
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• Naive Automated Machine Learning -- A Late Baseline for AutoML (2021)
  Felix Mohr et al.
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• Privileged Zero-Shot AutoML (2021)
  Nikhil Singh et al.
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• Automatic Componentwise Boosting: An Interpretable AutoML System (2021)
  Stefan Coors and Daniel Schalk and Bernd Bischl and David R\"ugamer
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• Towards Green Automated Machine Learning: Status Quo and Future Directions (2021)
  Tanja Tornede and Alexander Tornede and Jonas Hanselle and Marcel Wever and Felix Mohr and Eyke H\"ullermeier
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• SubStrat: A Subset-Based Strategy for Faster AutoML (2022)
  Teddy Lazebnik et al.
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• Benchmarking AutoML algorithms on a collection of synthetic classification problems (2022)
  Pedro Henrique Ribeiro et al.
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• AutoML in The Wild: Obstacles, Workarounds, and Expectations (2023)
  Yuan Sun et al.
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• Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML (2023)
  Hilde Weerts et al.
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• Optimizing Neural Networks through Activation Function Discovery and Automatic Weight Initialization (2023)
  Garrett Bingham
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• AutoML-GPT: Automatic Machine Learning with GPT (2023)
  Shujian Zhang et al.
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• PiML Toolbox for Interpretable Machine Learning Model Development and Diagnostics (2023)
  Agus Sudjianto et al.
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• AutoML in Heavily Constrained Applications (2023)
  Felix Neutatz and Marius Lindauer and Ziawasch Abedjan
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• Assembled-OpenML: Creating Efficient Benchmarks for Ensembles in AutoML with OpenML (2023)
  Lennart Purucker et al.
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• AutoML-GPT: Large Language Model for AutoML (2023)
  Yun-Da Tsai et al.
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• ASP: Automatic Selection of Proxy dataset for efficient AutoML (2023)
  Peng Yao et al.
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• Integrating Hyperparameter Search into Model-Free AutoML with Context-Free Grammars (2024)
  Hern\'an Ceferino V\'azquez et al.
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• EcoMLS: A Self-Adaptation Approach for Architecting Green ML-Enabled Systems (2024)
  Meghana Tedla et al.
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