Active Learning

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Overview

Active learning studies how to choose data or queries so models can learn effectively with fewer labels, measurements, or experiments. Common approaches include uncertainty sampling, diversity and coreset selection, Bayesian information gain, batch strategies, and cost-aware or human-in-the-loop querying; scientific applications also use Gaussian processes, multi-fidelity models, and physics-based acquisition to guide simulations and laboratory work. Work spans image and language benchmarks, medical annotation, robotics, and scientific discovery, with newer methods using foundation-model representations and adapting selection to class imbalance, domain shift, or label noise. A central open tension is that no acquisition strategy wins everywhere: gains depend on the task, budget, model and data, and random sampling can match or outperform sophisticated methods, making realistic evaluations and strong baselines essential.

Latest papers

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296 papers39 papers in the last four weeks, up 22% on the four weeks before. 0.2% of all new papers.

  1. ALMAB-DC: Active Learning, Multi-Armed Bandits, and Distributed Computing for Sequential Experimental Design and Black-Box Optimization

    Mar 22, 2026Foo Hui-Mean, Yuan-chin I ChangSequential Experimental DesignActive Experimental Design

    Institute of Statistical Science, Academia Sinica, Taipei, Taiwan

  2. Dodgersort: Uncertainty-Aware VLM-Guided Human-in-the-Loop Pairwise Ranking

    Mar 21, 2026Yujin Park, Haejun Chung, Ikbeom JangPairwise RankingHuman-in-the-Loop Annotation

    Hanyang University, Seoul, Republic of Korea · Hankuk University of Foreign Studies, Yongin, Republic of Korea

  3. Demonstration of Adapt4Me: An Uncertainty-Aware Authoring Environment for Personalizing Automatic Speech Recognition to Non-normative Speech

    Mar 20, 2026Niclas Pokel, Yiming Zhao, Pehuén Moure +2Personalized Speech RecognitionAutomatic Speech Recognition

    Institute of Neuroinformatics, University of Zurich and ETH Zurich · Department of Computer Science, ETH Zurich · Department of Computational Linguistics, University of Zurich

  4. MedQ-Engine: A Closed-Loop Data Engine for Evolving MLLMs in Medical Image Quality Assessment

    Mar 20, 2026Jiyao Liu, Junzhi Ning, Wanying Qu +4Medical Image Quality AssessmentMedical Image Analysis

    Fudan University, Shanghai, China · Shanghai Artificial Intelligence Laboratory, Shanghai, China

  5. KA2L: A Knowledge-Aware Active Learning Framework for LLMs

    Mar 18, 2026Haoxuan Yin, Chen Tang, Yangfan Wang +2Active LearningLLM Fine-Tuning

    Faculty of Computing, Harbin Institute of Technology, Harbin, 150001, China · Institute for Advanced Algorithms Research, Shanghai, 201306, China · National Key Laboratory of Smart Farm Technologies and Systems, Harbin, 150001, China

  6. Optimization-Embedded Active Multi-Fidelity Surrogate Learning for Multi-Condition Airfoil Shape Optimization

    Mar 17, 2026Isaac Robledo, Alberto Vilariño, Arnau Miró +3Multi-Fidelity OptimizationSurrogate Modeling

    Aerial Platforms Department Subdirectorate General for Aeronautical Systems Spanish National Institute for Aerospace Technology (INTA) · Department of Aerospace Engineering Universidad Carlos III de Madrid Madrid, Spain · TUAREG Research Group Universitat Politècnica de Catalunya (UPC) CASE Large-Scale Computational Fluid Dynamics Barcelona Supercomputing Centre (BSC) Barcelona, Spain

  7. Active Seriation: Efficient Ordering Recovery with Statistical Guarantees

    Mar 16, 2026James Cheshire, Yann IssartelActive Learning

    LTCI, Télécom Paris, Institut Polytechnique de Paris

  8. CASHomon Sets: Efficient Rashomon Sets Across Multiple Model Classes and their Hyperparameters

    Mar 16, 2026Fiona Katharina Ewald, Martin Binder, Matthias Feurer +2Hyperparameter OptimizationActive Learning

    Department of Statistics, LMU Munich, Munich, Germany · Munich Center for Machine Learning (MCML), Munich, Germany · TU Dortmund University, Dortmund, Germany +1

  9. Point-Identification of a Robust Predictor Under Latent Shift with Imperfect Proxies

    Mar 16, 2026Zahra Rahiminasab, Reza Soumi, Arto Klami +1Proximal Causal InferenceDistribution Shift Robustness

    Department of Computer Science, Aalto University · Department of Computer Science, University of Helsinki · ELLIS Institute Finland +1

  10. Efficient Semi-Automated Material Microstructure Analysis Using Deep Learning: A Case Study in Additive Manufacturing

    Mar 14, 2026Sanjeev S. Navaratna, Nikhil Thawari, Gunashekhar Mari +3Interactive Image SegmentationImage Segmentation

    Department of Metallurgical and Materials Engineering, Indian Institute of Technology Madras, Chennai 600036, India · Center for Atomistic Modelling and Materials Design, IIT Madras, Chennai 600036, India

  11. Active Sampling Sample-based Quantum Diagonalization from Finite-Shot Measurements

    Mar 13, 2026Rinka MiuraQuantum Many-Body PhysicsActive Learning

    Department of Applied Chemistry, Kobe City College of Technology, Japan

  12. BoSS: A Best-of-Strategies Selector as an Oracle for Deep Active Learning

    Mar 13, 2026Denis Huseljic, Paul Hahn, Marek Herde +2Active Learning

    Intelligent Embedded Systems University of Kassel Kassel, Hesse, Germany · Machine Learning Research Unit TU Wien Vienna, Vienna, Austria

  13. Foundation-Model Surrogates Enable Data-Efficient Active Learning for Materials Discovery

    Mar 13, 2026Jeffrey Hu, Rongzhi Dong, Ying Feng +2Active LearningData-Efficient Learning

    Department of Materials Science and Engineering, University of Illinois Urbana Champaign, Champaign,IL, USA · Department of Computer Science & Engineering, University of South Carolina, Columbia, SC, USA · Zuoyue Honors College, Hangzhou Dianzhi University, Hangzhou, China +1

  14. ActiveFreq: Integrating Active Learning and Frequency Domain Analysis for Interactive Segmentation

    Mar 12, 2026Lijun Guo, Qian Zhou, Zidi Shi +2Interactive Medical Image SegmentationActive Learning

    School of Computer Science, Wuhan University, Wuhan, 430072, China · Dongguan Polytechnic, Dongguan, 523651, China

  15. BALD-SAM: Disagreement-based Active Prompting in Interactive Segmentation

    Mar 11, 2026Prithwijit Chowdhury, Mohit Prabhushankar, Ghassan AlRegibInteractive Image SegmentationPromptable Image Segmentation

    OLIVES at the Georgia Institute of Technology

  16. Adaptive Active Learning for Regression via Reinforcement Learning

    Mar 11, 2026Simon D. Nguyen, Troy Russo, Kentaro Hoffman +1Active LearningAdaptive Sampling

    Department of Statistics, University of Washington · Department of Sociology, University of Washington

  17. Federated Active Learning Under Extreme Non-IID and Global Class Imbalance

    Mar 11, 2026Chen-Chen Zong, Sheng-Jun HuangActive LearningFederated Learning

    Nanjing University of Aeronautics and Astronautics, China

  18. ActiveUltraFeedback: Efficient Preference Data Generation using Active Learning

    Mar 10, 2026Davit Melikidze, Marian Schneider, Jessica Lam +4Data SelectionActive Learning

    ETH Zurich · Institute of Neuroinformatics, University of Zurich and ETH Zurich · ETH AI Center

  19. Adaptive Active Learning for Online Reliability Prediction of Satellite Electronics

    Mar 10, 2026Shixiang Li, Yubin Tian, Dianpeng Wang +2Active Learning

    ZJUI Institute, Zhejiang University · Faculty of Computational Mathematics and Cybernetics, Shenzhen MSU-BIT University · The School of Mathematics and Statistics, Beijing Institute of Technology

  20. Revisiting Unknowns: Towards Effective and Efficient Open-Set Active Learning

    Mar 9, 2026Chen-Chen Zong, Yu-Qi Chi, Xie-Yang Wang +2Active LearningOpen-Set Recognition

    Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

  21. PromptGate Client Adaptive Vision Language Gating for Open Set Federated Active Learning

    Mar 7, 2026Adea Nesturi, David Dueñas Gaviria, Jiajun Zeng +1Active LearningFederated Learning

    University of Bonn, University Hospital Bonn, Clinic for Diagnostic and Interventional Radiology, 53127 Bonn, Germany

  22. Active Bipartite Ranking with Smooth Posterior Distributions

    Feb 27, 2026James Cheshire, Stephan ClémençonLearning to RankStatistical Learning Theory

    Telecom Paris

  23. Active Learning for Planet Habitability Classification under Extreme Class Imbalance

    Feb 27, 2026R. I. El-Kholy, Z. M. HaymanActive LearningClass-Imbalanced Learning

    Department of Astronomy, Space Science, and Meteorology Faculty of Science, Cairo University 1 Gamaa Street, Giza 12613, Egypt

  24. Active Value Querying to Minimize Additive Error in Subadditive Set Function Learning

    Feb 26, 2026Martin Černý, David Sychrovský, Filip Úradník +1Learning with Missing DataActive Learning

    Charles University, Prague, Czechia · Columbia University, New York, USA

  25. Error-awareness Accelerates Active Automata Learning

    Feb 25, 2026Loes Kruger, Sebastian Junges, Jurriaan RotAutomata LearningActive Learning

    Institute for Computing and Information Sciences, Radboud University, Nijmegen, the Netherlands

  26. Goal-Oriented Influence-Maximizing Data Acquisition for Learning and Optimization

    Feb 23, 2026Weichi Yao, Bianca Dumitrascu, Bryan R. Goldsmith +1Active Information GatheringActive Learning

    University of Michigan · Columbia University

  27. Similarity-as-Evidence: Calibrating Overconfident VLMs for Interpretable and Label-Efficient Medical Active Learning

    Feb 21, 2026Zhuofan Xie, Zishan Lin, Jinliang Lin +3Active LearningVLM Calibration

    School of Electronic Technology and Engineering, Xiamen University, Xiamen, China · School of Informatics, Xiamen University, Xiamen, China · School of Engineering, Case Western Reserve University, Cleveland, USA

  28. Learning Without Training

    Feb 20, 2026Ryan O'DowdFunction ApproximationSupervised Learning

    Claremont Graduate University

  29. In-Context Pure Exploration in Continuous Decision Spaces

    Feb 20, 2026Alessio Russo, Yin-Ching Lee, Ryan Welch +1Sequential Hypothesis TestingActive Learning

    Faculty of Computing and Data Sciences, Boston University, Boston, MA, USA. · Department of Computer Science, Stanford University, Stanford, CA, USA.

  30. Adapting Actively on the Fly: Relevance-Guided Online Meta-Learning with Latent Concepts for Geospatial Discovery

    Feb 19, 2026Jowaria Khan, Anindya Sarkar, Yevgeniy Vorobeychik +1Adaptive SamplingActive Learning

    University of Michigan, Ann Arbor, MI, USA · Washington University in St. Louis, St. Louis, MO, USA