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. Adaptive Diversity-Uncertainty Active Learning with Redundancy Control for Bioacoustic Event Classification

    Jul 6, 2026Gabriel Dubus, Hugo Magaldi, Anatole Gros-MartialMulti-Label ClassificationActive Learning

    Eco-Anthropologie, Mus´eum National d’Histoire Naturelle, UMR7206, CNRS, Paris, France · Centre d’Etudes Biologiques de Chiz´e, UMR 7372, CNRS, La Rochelle Universit´e, France

  2. Neuron-Aware Active Few-Shot Learning for LLMs

    Jul 2, 2026Zhuowei Chen, Liwei Chen, Christian Schunn +2Active LearningFew-Shot Learning

    University of Pittsburgh, USA

  3. Group-invariant Coresets for Data-efficient Active Learning

    Jul 1, 2026L. C. Ayres, J. C. M. Bermudez, S. J. M. de Almeida +1Active LearningCoreset Selection

    Universidade Federal de Santa Catarina, Florianópolis, SC, Brazil · Universidade Católica de Pelotas, Pelotas, RS, Brazil · Université de Lorraine, CNRS, CRAN, Vandoeuvre-lès-Nancy, France

  4. Active Learning for Cascaded Object Detection: Balancing Coverage and Uncertainty in Table Extraction Pipelines

    Jul 1, 2026Eliott Thomas, Mickael Coustaty, Aurelie Joseph +3Table Structure RecognitionDocument Information Extraction

    L3i, La Rochelle Université, La Rochelle, France · Yooz, Aimargues, France

  5. Detecting the Undetectable: Enhancing Unsupervised time series Anomaly Detection via Active Learning

    Jul 1, 2026Seung Hun Han, Hyeongwon Kang, Jinwoo Park +1Industrial Anomaly DetectionActive Learning

    LG CNS, 71 Magokjungang 8-ro, Gangseo-gu, Seoul, Republic of Korea · Department of Industrial & Management Engineering, Korea University, 126-16 Anam-dong 5-ga, Seongbuk-gu, Seoul, Republic of Korea · Department of Industrial Engineering, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul, Republic of Korea

  6. A Mechanism-Driven Theory of Phase Transitions in Active Learning

    Jun 30, 2026Julia Machnio, Mads Nielsen, Mostafa Mehdipour GhaziActive LearningStatistical Learning Theory

    Pioneer Centre for AI, University of Copenhagen, Copenhagen, Denmark

  7. Hybrid Active-Online Learning Framework for Label-Efficient Concept Drift Adaptation in Optical Network Failure Detection

    Jun 29, 2026Yousuf Moiz Ali, Jaroslaw E. Prilepsky, João Pedro +4Concept DriftActive Learning

    Aston University, Birmingham, UK · Nokia, Optical Networks, Carnaxide, Portugal · Nokia, Munich, Germany

  8. Transformer-Based Active Learning for Data-Efficient Vaccine Epitope Selection in PRRS

    Jun 27, 2026Aspen Erlandsson Brisebois, Zahed Khatooni, Connor Burbridge +5TransformerActive Learning

    Vaccine and Infectious Disease Organization (VIDO), University of Saskatchewan, Saskatoon, SK, Canada · Engineering Science, Faculty of Applied Science, University of Toronto, Toronto, ON, Canada · Department of Mathematics and Statistics, University of Saskatchewan, Saskatoon, SK, Canada +4

  9. Efficient Adaptive Data Acquisition via Pretrained Belief Representations

    Jun 23, 2026Daolang Huang, Zhuoyue Huang, Conor Hassan +3Bayesian OptimizationActive Learning

    ELLIS Institute Finland · Department of Computer Science, Aalto University, Finland · Department of Statistics, University of Oxford, UK +2

  10. Dataset-Aware Cold-Start Active Learning for Annotation-Efficient 3D Medical Image Segmentation

    Jun 18, 2026Rémi Hattat, Marine Beaumont, Charline Bertholdt +3Active Learning3D Medical Image Segmentation

    IADI (U1254), Inserm and Université de Lorraine, Nancy, France · CIC-IT 1433, Inserm, Université de Lorraine, and CHRU Nancy, Nancy, France

  11. AURA: Adaptive Uncertainty-aware Refinement for LLM-as-a-Judge Auditing

    Jun 18, 2026Zilong Zhang, Yi-Ting Hung, Weiyi He +3LLM-as-a-JudgeLLM Auditing

    Department of Mathematics and Statistics Georgia State University · Department of Probability and Statistics Department of Computer Science and Engineering Michigan State University · Department of Mathematics and Statistics Concordia University +1

  12. Can In-Context Learning Support Intrinsic Curiosity?

    Jun 17, 2026Eric Elmoznino, Sangnie Bhardwaj, Johannes von Oswald +5Intrinsic Reward MethodsIntrinsic Motivation

    1Google – Paradigms of Intelligence Team, 2Google DeepMind

  13. Zero-Shot Active Feature Acquisition via LLM-Elicitation

    Jun 17, 2026Binyamin Perets, Natalie Mendelson, Shiran Vainberg +3Zero-Shot LearningMarkov Random Fields

    Faculty of Medicine, Technion. · NVIDIA

  14. QueryMarket: Cost-Aware Online Active Learning in Data Markets

    Jun 16, 2026Xiwen Huang, Pierre PinsonActive Learning

    Dyson School of Design Engineering, Imperial College London, London, SW7 2DB, U.K. · Dyson School of Design Engineering, Imperial College London, London, U.K. · Halfspace (part of Accenture) +2

  15. LOPAL: Local Performance-Aware Active Learning from Imperfect Demonstrations

    Jun 15, 2026Johannes Heidersberger, Shail Jadav, Dongheui LeeRobot Skill LearningActive Learning

    Autonomous Systems Lab, Institute of Computer Technology, TU Wien, Vienna, Austria · Institute of Robotics and Mechatronics, German Aerospace Center (DLR), Weßling, Germany

  16. Can Agents Infer Environment from Interaction? Evidence from Agentic Automata Learning

    Jun 15, 2026Reef Menaged, Gili Lior, Shauli Ravfogel +2LLM Agent EvaluationActive Learning

    The Hebrew University of Jerusalem · New York University · Google Research

  17. Active Reference Acquisition in Few-Shot Font Generation

    Jun 15, 2026Shinnosuke MatsuoActive LearningFew-Shot Learning

    NTT, Inc., Japan · Kyushu University

  18. Active Learning with Low-Rank Structure for Data Selection

    Jun 14, 2026Vincent Cohen-Addad, Sasidhar Kunapuli, Vahab Mirrokni +3Active LearningData Selection

    Google Research. · University of California, Berkeley. · Institute of Science and Technology Austria (ISTA). +2

  19. Surprise-Guided MergeSort: Budget-Efficient Human-in-the-Loop Ranking via Adaptive Comparison Scheduling

    Jun 14, 2026Yujin Park, Haejun Chung, Ikbeom JangPairwise Preference LearningPairwise Comparison

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

  20. A Complexity Measure for Active Learning in Multi-group Mean Estimation

    Jun 12, 2026Abdellah Aznag, Rachel Cummings, Adam N. ElmachtoubMulti-Armed BanditsActive Learning

    Department of Industrial Engineering and Operations Research & Data Science Institute, Columbia University

  21. cAPM: Continual AI-Assisted Pace-Mapping with Active Learning

    Jun 11, 2026Dylan O'Hara, Pradeep Bajracharya, Casey Meisenzahl +7Neural Surrogate ModelingElectrocardiography

    Rochester Institute of Technology, Rochester, New York, USA · Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah, USA · Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah, USA +5

  22. SymQNet: Amortized Acquisition for Low-Latency Adaptive Hamiltonian Learning

    Jun 11, 2026Yash Vardhan Tomar, Dheeraj PeddireddyReinforcement LearningQuantum System Identification

    Purdue University West Lafayette, United States

  23. ATLAS: Active Theory Learning for Automated Science

    Jun 10, 2026Noémi Éltető, Nathaniel D. Daw, Kimberly L. Stachenfeld +1Cognitive ModelingActive Learning

    Google DeepMind London, UK · Princeton University; Google DeepMind Princeton, NJ, USA · Google DeepMind; Colombia University New York, NY, USA +1

  24. How Low Can You Go? Active Learning for Sparse Model Discovery in the Ultra-Low-Data Limit

    Jun 10, 2026Ana Larrañaga, Urban Fasel, Steven L. BruntonSparse Identification of Nonlinear DynamicsPDE Discovery

    Department of Mechanical Engineering, University of Washington, Seattle, WA 98195, United States · NSF AI Institute in Dynamic Systems, University of Washington, Seattle, WA 98195, United States · Department of Aeronautics, Imperial College London, SW7 2AZ, United Kingdom

  25. Robust Regression of General ReLUs with Queries

    Jun 9, 2026Ilias Diakonikolas, Daniel M. Kane, Mingchen MaReLU Neural NetworksActive Learning

    University of Wisconsin-Madison · University of California, San Diego

  26. iSAGE: A Human-in-the-Loop Framework for Remote Sensing Semantic Segmentation via Sparse Point Supervision

    Jun 8, 2026Osmar Luiz Ferreira de Carvalho, Osmar Abilio de Carvalho Junior, Anesmar Olino de Albuquerque +1Remote Sensing Image SegmentationHuman-in-the-Loop Annotation

  27. Robust Active Learning for Few-Shot Example Selection in Text-to-SQL

    Jun 8, 2026Arash PourhabibSubmodular OptimizationLLM Grounding

    NVIDIA

  28. Learning Where to Simulate: Generative Active Sampling for Online PDE Surrogate Training

    Jun 8, 2026Pierre Cesar, Sofya Dymchenko, Abhishek Purandare +1PDE Surrogate ModelingActive Learning

    DATAMOVE · Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LIG, Grenoble, France

  29. Deep Active Re-Labeling: Toward Noise-Resilient Annotation Efficiency

    Jun 7, 2026Md Abdullah Al Forhad, Weishi ShiHuman-in-the-Loop AnnotationActive Learning

    Active Machine Learning Lab · Learning · Language · Vision (LLaVi) Lab · Department of Computer Science and Engineering +1

  30. Inverse design of bespoke interatomic potentials via active learning by information-matching

    Jun 6, 2026Yonatan Kurniawan, Logan D. Williams, Amit Samanta +6Uncertainty QuantificationMachine Learning Interatomic Potentials

    Department of Physics and Astronomy, Brigham Young University, Provo, UT, USA · Department of Physics and Astronomy, Brigham Young University, Provo, UT, USA. · Lawrence Livermore National Laboratory, Livermore, CA, USA +7