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. Coordination on a Budget: Federated Active Learning with Few Labels

    Aug 19, 2026Liam Mohr, Daphna WeinshallActive LearningPrivacy-Preserving ML

    School of Computer Science and Engineering The Hebrew University of Jerusalem, Jerusalem 91904, Israel

  2. Probing Association Instability with Track-State Perturbations for Clip-Level Active Learning in Query-Propagation Multi-Object Tracking

    Aug 18, 2026Riku Inoue, Shogo Sato, Kazuhiko Murasaki +3Multi-Object TrackingActive Learning

    Human Informatics Laboratories NTT, Inc. Kanagawa, Japan

  3. GATTA: Graph Active Learning with Test-Time Augmentation

    Aug 15, 2026Zsombor Bánfi, András Gézsi, András FormanekGraph Data AugmentationActive Learning

    Department of Artificial Intelligence and Systems Engineering, Budapest University of Technology and Economics, Magyar tudósok krt. 2, Budapest, H-1117, Hungary. · Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, 3001 Leuven, Belgium.

  4. Coverage Aware Active Evaluation for Failure Discovery with Paired Systems

    Aug 13, 2026Anjali Parashar, Rachel Luo, Apoorva Sharma +6Robot Failure DetectionAdaptive Sampling

    Laboratory of Information & Decision Systems (LIDS), MIT, USA · NVIDIA Research, USA

  5. Multitask Scanning Probe Microscopy

    Aug 10, 2026Aditya Raghavan, Yu Liu, Ian Mercer +2Active LearningMulti-Task Learning

    University of Tennessee, Knoxville, TN, USA · 2Pennsylvania State University, University Park, PA, USA

  6. Trajectory Design and Budgeted Querying for Digital Twin Calibration

    Aug 9, 2026Vladyslava Spitkovska, Dmytro KuzmenkoActive LearningSystem Identification

    Department of Mathematics, National University of Kyiv-Mohyla Academy, Kyiv, Ukraine · Department of Multimedia Systems, National University of Kyiv-Mohyla Academy, Kyiv, Ukraine · Department of Computer Science, University of Turin, Turin, Italy

  7. An active-learning framework for real-time depth perception from monocular vision streams

    Aug 5, 2026Xiaorong Zeng, Weiqiang Chen, Peng Shi +4Edge InferenceMonocular Depth Estimation

    School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen, Fujian 361024, China · King Long United Automotive Industry Co. Ltd., Xiamen 361023, China · School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA 5005, Australia +2

  8. Active Learning Guided Design Space Refinement for Scalable Multi-Objective Bayesian Optimization in Materials Discovery

    Aug 5, 2026Alexandros Ntagiantas, Panagiotis Tsilimidos, George Giannakopoulos +2Bayesian OptimizationActive Learning

    National Centre for Scientific Research “Demokritos”

  9. How Many Labels Are Enough? ALDA: Active Learning Deployment Advisor for Medical Image Classification

    Aug 4, 2026Julia Machnio, Mads Nielsen, Mostafa Mehdipour GhaziActive LearningMedical Image Classification

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

  10. One Knob to Rule Them All: A Unified Optimal Transport View of Cold-Start Active Learning

    Aug 4, 2026Ning Zhu, Xiaochuan Ma, Juntao Xu +4Active LearningTraining Data Selection

    Glasgow College, University of Electronic Science and Technology of China · School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China · School of Mathematical Sciences, University of Electronic Science and Technology of China

  11. Fermat Active Laplace Learning for Semi-Supervised Hyperspectral Image Classification

    Aug 3, 2026Vutichart Buranasiri, James M. MurphyActive LearningGraph-Based Semi-Supervised Learning

    Department of Mathematics Tufts University Medford, MA 02155, USA

  12. BRiG-AFA: Bellman Risk-to-Go Learning for Non-Myopic Active Feature Acquisition

    Aug 3, 2026Jiaorong Feng, Qian Li, Ying LiCost-Aware InferenceActive Learning

    Curtin Business School, Curtin University, Perth, Western Australia, Australia · †Present affiliation: Independent Researcher. · School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Perth, Western Australia, Australia

  13. Active Regression for Single-Index Models with Unknown Link Functions

    Aug 2, 2026Chansophea Wathanak In, Yi Li, Wai Ming Tai +1Single-Index ModelsActive Learning

    School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore · Independent Researcher · John Hopcroft Center for Computer Science, Shanghai Jiao Tong University, China

  14. Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields

    Aug 1, 2026Anand Kumar, Puli Saikiran, Vineet Dawara +1Neural Surrogate ModelingUncertainty Quantification

    Dept. of Design & Manufacturing, Indian Institute of Science, Bangalore · Dept. of Mechanical Engineering, Indian Institute of Science, Bangalore

  15. FunnelAL: Retrieve-then-Rank Active Learning for Single-Class Discovery

    Jul 28, 2026Reihaneh Rostami, Brian GoodwinActive LearningClassification

    RAIC Labs

  16. Unifying Active Learning and Semi-Supervised Learning for Medical Image Segmentation

    Jul 27, 2026Bahram Jafrasteh, Cheng Wan, Heejong Kim +2Active LearningSemi-Supervised Medical Image Segmentation

    Department of Radiology, Weill Cornell Medicine, New York, NY, USA · Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA

  17. Active few-shot segmentation by reinforcing data selection

    Jul 24, 2026Chenlan Zhao, Benny Wong, Timothy F. Lundberg +8Active LearningMedical Image Segmentation

    Centre for Bioengineering, School of Engineering and Materials Science, Queen Mary University of London, London, United Kingdom · Digital Environment Research Institute, Queen Mary University of London, London, United Kingdom · UCL Hawkes Institute; Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom +2

  18. Frequency-Hierarchical Active k-Space Sampling for Diagnostic MRI

    Jul 22, 2026Ruru Xu, Kian Anvari Hamedani, Zhikai Yang +1Active LearningAccelerated MRI Reconstruction

    Computer Engineering Department, Istanbul Technical University, Istanbul, Turkey · Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada · Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada +1

  19. TypiCore: A Hybrid Active Query Strategy for Class-Incremental Learning on Time Series

    Jul 20, 2026Gabor Szucs, Samuel Jacsev, Marcell Nemeth +2Class-Incremental LearningTime Series Classification

    Department of Telecommunications and Artificial Intelligence, Budapest University of Technology and Economics, Budapest, Hungary. · Department of Information Engineering, University of Padua, Padua, Italy.

  20. Efficient Sequential Evaluation of Large Language Models

    Jul 19, 2026Chia-Yu Hsu, Shubhanshu ShekharLLM EvaluationConfidence Estimation in Language Models

    EECS Department, University of Michigan

  21. Foundation-Assisted Active Learning for Object Detection Annotation

    Jul 18, 2026Jinchang Zhang, Arnold Zumbrun, Jing Lin +1Remote Sensing Image UnderstandingRemote Sensing Small Object Detection

    Intelligent Vision and Sensing (IVS) Lab at Indiana University Bloomington

  22. Embodied Active Learning under Limited Annotation and Navigation Budget for Object Detection

    Jul 17, 2026Hadrien Crassous, Mohamed Yassine Kabouri, Minahil Raza +2Active LearningObject Detection

    Inria Scool, the Universit´e de Lille and also with the Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) · LAAS-CNRS, Universite de Toulouse, CNRS, Toulouse, France · Department of Electrical Engineering and Automation at Aalto University

  23. Full-data accuracy with fewer labels for training and fine-tuning machine-learning force fields

    Jul 16, 2026Sheng Bi, Yi-Ze Wang, Jun ChengUncertainty QuantificationMachine Learning Interatomic Potentials

    College of Materials, Xiamen University, Xiamen, China. · Laboratory of AI for Electrochemistry (AI4EC), IKKEM, Xiamen, China. · State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, China.

  24. Cover First, Disagree Softly: Rethinking Mismatch-First Active Learning for Frame-Level Audio Classification

    Jul 15, 2026Shiqi Zhang, Tuomas VirtanenMulti-Label ClassificationActive Learning

    Audio Research Group, Tampere University, Tampere, Finland

  25. Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning

    Jul 15, 2026Shiqi Zhang, Marius Faiß, Ariana Strandburg-Peshkin +1Long-Tail LearningActive Learning

    1Audio Research Group, Tampere University, Tampere, Finland · Department for the Ecology of Animal Societies, Max Planck Institute of Animal Behavior · 3International Max Planck Research School for Quantitative Behavior, Ecology and Evolution +1

  26. Active Learning for Efficient Annotation of Surgical Videos with Weak Supervision

    Jul 14, 2026Manasa Dendukuri, Matjaz Jogan, Daniel A. Hashimoto +1Video Object SegmentationHuman-in-the-Loop Annotation

    1*GRASP Laboratory, University of Pennsylvania, 3330 Walnut Street, Philadelphia, PA, 19104, USA. · 2PCASO Laboratory, Dept. of Surgery, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA. · 3Dept. of Computer and Information Science, University of Pennsylvania, 3330 Walnut Street, Philadelphia, PA, 19104, USA.

  27. Playing ZendoWorld: Challenging AI Agents on Active Visual Concept Induction

    Jul 9, 2026Sophia Koehler, Antonia Wüst, Inga Ibs +5AI Agent EvaluationActive Learning

    1AIML Lab, TU Darmstadt · 2Hessian Center for AI (hessian.AI) · Psychology of Information Processing, TU Darmstadt +4

  28. The Approximation Ratio for the Risk of Myopic Bayesian Active Learning for Linear Regression

    Jul 7, 2026Stephen MussmannActive LearningLinear Regression

    School of Computer Science Georgia Institute of Technology

  29. Determinantal point process sampling for bioacoustic active learning

    Jul 7, 2026Hugo Magaldi, Gabriel DubusActive LearningAdaptive Sampling

    Eco-Anthropologie, Mus´eum National d’Histoire Naturelle, UMR7206, CNRS, Paris, France

  30. Active Learning on Adversarially Corrupted Graphs

    Jul 6, 2026Marco Bressan, Nicolò Cesa-Bianchi, Tommaso d`Orsi +2Adversarial AttacksActive Learning

    Università degli Studi di Milano, Italy · Bocconi University, Italy · Google Research