Automated Machine Learning

Also known as AutoML

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Overview

Automated machine learning studies how to choose and improve the steps that turn data into a useful model, from preprocessing and feature selection to algorithms, hyperparameters, and deployment. Traditional approaches include Bayesian optimization, evolutionary search, neural architecture search, and meta-learning that uses dataset characteristics or prior experiments to guide choices. Increasingly, language-model agents generate and revise executable pipelines, using tool feedback, memory, and validation results to automate broader machine-learning engineering tasks. These systems can reduce manual effort, but reliable progress depends on fair compute budgets, leakage-free evaluation, reproducibility, and evidence that improvements generalize beyond the search data.

Latest papers

CardsList

97 papers13 papers in the last four weeks, up 44% on the four weeks before. 0.1% of all new papers.

  1. Predict the Reward, Not the World: World Reward Models for ML Agents

    Oct 28, 2026Yuxiao Lee, Zherui Yang, Fan Liu +1Preference LearningAutomated Machine Learning

    The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China

  2. Auto-Research Arena: Benchmarking and Advancing Auto-Research in Language Modeling

    Oct 26, 2026Xi Chen, Chongjian Ge, Yiran Xu +5Automated Machine LearningEfficient Language Model Training

    University of California San Diego · Adobe Research

  3. AutoMIL: Automated Multiple Instance Learning for Whole-Slide Image Classification

    Oct 23, 2026Jonas Waibel, Frank Kramer, Dominik MüllerHistopathology Image ClassificationWhole-Slide Image Analysis

    IT-Infrastructure for Translational Medical Research, University of Augsburg, Germany · Department of Computer Science, Ulm University of Applied Sciences, Ulm, Germany

  4. A Multi-Agent Framework for Automated Machine Learning with Evidence-based Planning for Medical Image Analysis

    Oct 20, 2026Yiqing Shen, Mathias UnberathAutomated Machine LearningMedical Image Analysis

    Department of Computer Science, Johns Hopkins University

  5. Can LLMs predict the outcomes of machine learning experiments?

    Oct 15, 2026Ayush Bansal, Aldo Pareja, Sean Klein +2LLM EvaluationAutonomous Experimentation

    Autolab AI · Institute of Foundation Models · MIT

  6. MLExpert: Towards a Generalist Agentic Model for Autonomous Machine Learning Engineering

    Oct 15, 2026Shangheng Du, Jinxin Shi, Shuaiyu Zhang +7LLM Agent TrainingAutomated Machine Learning

    East China Normal University · Shanghai Artificial Intelligence Laboratory

  7. ML Engineering Agents - A Data Management Perspective

    Oct 12, 2026Arnab Phani, Elias Strauss, Tim Schuster +1Dataset DiscoveryAutomated Machine Learning

    BIFOLD & TU Berlin, Germany

  8. FlexiFlow: Bandit-based Model Switching in ML Workflows

    Oct 5, 2026Abhilash Jindal, Todd Nief, Bhanu Prakash Vangala +6Multi-Armed BanditsInference-Time Optimization

    IIT Delhi India · University of Chicago USA · University of Missouri USA +1

  9. EvoCast: Reliable Autonomous Research Agents for Iterative Forecasting Architecture Evolution

    Oct 3, 2026Kaipeng Xu, Xianli Yan, Yan Wang +2Autonomous Research AgentsAutomated Machine Learning

    School of Automation and Intelligent Science, Jiangnan University, Wuxi 214122, China · School of Automation, Southeast University, Nanjing 210096, China

  10. How Much of a Harness Does a Strong Agent Need for Autonomous ML Engineering?

    Sep 30, 2026Kirill Brilliantov, Alejandro Hernández-Cano, Emmanuel AbbéAI Coding AgentsLLM Agent Evaluation

    EPFL · Apple

  11. TabFM-Auto: Self-Evolving Pipelines for Tabular Foundation Models

    Sep 29, 2026Deqing Fu, Huangyuan Su, Rajat Sen +4Tabular PredictionAutomated Machine Learning

    Google Research · University of Southern California · Google DeepMind +2

  12. Equally Good, Yet Different: Benchmarking Rashomon sets in AutoML packages

    Sep 29, 2026Katarzyna Woźnica, Katarzyna Rogalska, Zuzanna Sieńko +1Automated Machine LearningExplainability Evaluation

    Warsaw University of Technology · Systems Research Institute, Polish Academy of Sciences · Eskisehir Technical University

  13. Large Language Models for Automated Cross-Domain Machine Learning Task Type Identification: A Benchmark Dataset and Evaluation

    Sep 28, 2026Petros Tsialis, Steffen Limmer, Tobias Rodemann +1LLM EvaluationTabular ML

    University of Applied Sciences Aalen · Honda Research Institute Europe

  14. EdgeCraft: Automated Model Crafting for Edge IoT

    Sep 28, 2026Genglin Wang, Kaiwei Liu, Liekang Zeng +4Edge ComputingInternet of Things

    Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China · School of Computer Science, Peking University, Beijing, China

  15. Automated Feature Engineering, AutoML, and Decision-Focused Learning for Improved Energy Consumption Forecasting

    Sep 28, 2026Nasser AlkhulaifiAutomated Machine LearningElectricity Load Forecasting

    School of Computer Science, Faculty of Science University of Nottingham

  16. Right Choice of Classification Algorithms Based on Reinforcement Learning for Prediction of Non-Alcoholic Fatty Liver

    Sep 24, 2026Hasan Samadbin, Arman DaliriAlgorithm SelectionReinforcement Learning

    Department of Computer Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran

  17. A Rapid Pipeline for Training and Deploying ML Models on WeBe Band

    Sep 24, 2026Ehsan Kourkchi, Asmita Asmita, Houman Homayoun +1Tiny MLEdge ML

    Dep. of Electrical and Computer Engineering UC Davis, CA, USA. · dept. name of organization (of Aff.) Quandary Peak Research Los Angeles, CA, USA.

  18. An LLM-Assisted AutoML Framework for Intrusion Detection in IoT Networks

    Sep 19, 2026Li YangAutomated Machine LearningIoT Intrusion Detection

    Faculty of Business and Information Technology, Ontario Tech University, Oshawa, ON L1G 0C5, Canada · Department of Electrical and Computer Engineering, Western University, London, ON N6A 3K7, Canada

  19. TuiML: Machine Learning for AI Agents

    Sep 16, 2026Nilesh Verma, Nick Lim, Albert Bifet +1Tool-Using AgentsML Reproducibility

    AI Institute, University of Waikato Hamilton 3216, New Zealand · LTCI, T´el´ecom Paris, Institut Polytechnique de Paris 19 place Marguerite Perey, 91120 Palaiseau, France

  20. LongAgent: History-Guided Agentic Search for Longitudinal Outcome Prediction

    Sep 14, 2026Siyao Wang, Florian Guitton, Shuojie Fu +3Clinical Outcome PredictionAutomated Machine Learning

    Data Science Institute, Imperial College London, London, UK · Department of Computing, Imperial College London, London, UK · Department of Brain Sciences, Imperial College London, London, UK

  21. SAGE-Loop: Reliable Closed-Loop LLM-Driven AutoML with Trial-and-Correction and Adaptive Ensembling

    Sep 11, 2026Junquan Gu, Shibo Cui, Xiangfeng Luo +1LLM Self-CorrectionEnsemble Learning

  22. Meta-Learning for Classifier Selection in Image Datasets: A Feature-Driven Framework for Accuracy Prediction

    Sep 10, 2026Zahra Nabizadeh_Shahre_Babak, Farzaneh Koohestani, Nader Karimi +2Algorithm SelectionMeta-Learning

    Department of Electrical and Computer Engineering, McMaster University, Hamilton L8S 4L8, Canada · Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 8415683111, Iran · Department of Computer Science, Seattle University, Seattle, WA 98122-1090, USA

  23. Agentic ML Exploration (A-MLE) for Ads Ranking

    Sep 8, 2026Erwin Gao, Vinodh Kumar Sunkara, Jingyi Guan +36Learning to RankLLM Agent Evaluation

    Meta Platforms, Inc. Menlo Park and New York, USA

  24. AIBuildAI-2.5: Efficient Autonomous AI Model Development Through LLM-Guided Tree Search

    Sep 6, 2026Peijia Qin, Ruiyi Zhang, Qi Cao +3Language Model-Guided Search HeuristicsAutomated Machine Learning

    Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA · Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA

  25. Planetary Prediction Engine: Autonomous Geospatial Prediction via Intelligent Data Selection and Foundation Model Embeddings

    Aug 26, 2026Evelyn Ma, Rama Kumar Pasumarthi, Kishwar Shafin +26Automated Machine Learning

    Google Research · Institut National de Recherche Biomédicale, Democratic Republic of Congo · University of Oxford

  26. TraceML: What Auto-Research Agents Miss in Long-Horizon ML Development

    Aug 26, 2026Jiarui Yan, Weiwei Sun, Sijie Li +2Long-Horizon Agent EvaluationAI Agent Benchmarks

    Carnegie Mellon University

  27. Can Coding Agents Build Robust Baselines? A Skill-Based Approach for Automating the Medical Imaging Model-Development Pipeline

    Aug 24, 2026Eugenia Moris, José Ignacio OrlandoAutomated Machine LearningAI Agents for Scientific Discovery

    Arionkoder LLC, Boston, MA 02114, United States · Yatiris, PLADEMA, UNICEN-CONICET, Tandil, Buenos Aires, Argentina

  28. Candidate-Fate Accounting for Transparent Sensor Diagnostic Pipeline Search

    Aug 19, 2026Haotao Xie, Yutian Chen, Yangqi Liu +1Algorithmic AuditingAutomated Machine Learning

    Hangzhou International Innovation Institute, Beihang University Hangzhou, China

  29. Evolving Executable Pipeline Programs for AutoML with Language Models

    Aug 17, 2026Sofoklis Kitharidis, Cor J. Veenman, Jan N. van Rijn +2Automated Machine LearningTabular ML

    Leiden Institute of Advanced Computer Science (LIACS), Leiden University, The Netherlands · Netherlands Organization for Applied Scientific Research (TNO), The Netherlands

  30. Recovering Wasted Compute in Autoresearch Agents

    Aug 11, 2026Au Kwok Chun, Abhigyan Acherjee, Amrutha Rao +4Inference-Time SearchAgent Harness Optimization

    Department of Computer Science, Columbia University · AI, Analytics and Future of Work Initiative, Georgetown University · Department of Applied Mathematics, Columbia University +3