{"total":33,"rows":[{"id":"2203.09410","title":"A Framework and Benchmark for Deep Batch Active Learning for Regression","created":"2022-03-17","cited_by_count":49,"area":null},{"id":"2205.10186","title":"Bayesian Active Learning with Fully Bayesian Gaussian Processes","created":"2022-05-20","cited_by_count":26,"area":null},{"id":"2408.01536","title":"Active Learning for Neural PDE Solvers","created":"2024-08-02","cited_by_count":22,"area":null},{"id":"2303.01560","title":"Active Learning and Bayesian Optimization: a Unified Perspective to Learn with a Goal","created":"2023-03-02","cited_by_count":17,"area":null},{"id":"2403.03589","title":"Active Adaptive Experimental Design for Treatment Effect Estimation with Covariate Choices","created":"2024-03-06","cited_by_count":12,"area":null},{"id":"2203.10794","title":"Human-Centric Artificial Intelligence Architecture for Industry 5.0 Applications","created":"2022-03-21","cited_by_count":11,"area":null},{"id":"2204.07177","title":"Active Learning for Regression by Inverse Distance Weighting","created":"2022-04-14","cited_by_count":9,"area":null},{"id":"2207.14769","title":"Image Quality Assessment: Integrating Model-Centric and Data-Centric Approaches","created":"2022-07-29","cited_by_count":5,"area":null},{"id":"2511.18107","title":"Active Learning with Selective Time-Step Acquisition for PDEs","created":"2025-11-22","cited_by_count":4,"area":null},{"id":"2208.07497","title":"Bucketized Active Sampling for Learning ACOPF","created":"2022-08-16","cited_by_count":3,"area":null},{"id":"2304.06808","title":"Active Cost-aware Labeling of Streaming Data","created":"2023-04-13","cited_by_count":3,"area":null},{"id":"2411.11256","title":"Progressive Generalization Risk Reduction for Data-Efficient Causal Effect Estimation","created":"2024-11-18","cited_by_count":3,"area":null},{"id":"2505.05242","title":"Enhancing Treatment Effect Estimation via Active Learning: A Counterfactual Covering Perspective","created":"2025-05-08","cited_by_count":3,"area":null},{"id":"2205.11117","title":"PyRelationAL: a python library for active learning research and development","created":"2022-05-23","cited_by_count":2,"area":null},{"id":"2306.09321","title":"Crowd-Powered Photo Enhancement Featuring an Active Learning Based Local Filter","created":"2023-06-15","cited_by_count":2,"area":null},{"id":"2309.01013","title":"Streaming Active Learning for Regression Problems Using Regression via Classification","created":"2023-09-02","cited_by_count":2,"area":null},{"id":"2405.20620","title":"\"Forgetting\" in Machine Learning and Beyond: A Survey","created":"2024-05-31","cited_by_count":2,"area":null},{"id":"2410.19631","title":"Efficient Biological Data Acquisition through Inference Set Design","created":"2024-10-25","cited_by_count":2,"area":null},{"id":"2501.15343","title":"Development and Application of Self-Supervised Machine Learning for Smoke Plume and Active Fire Identification from the FIREX-AQ Datasets","created":"2025-01-25","cited_by_count":2,"area":null},{"id":"2105.13809","title":"Distribution Matching for Machine Teaching","created":"2021-05-06","cited_by_count":1,"area":null},{"id":"2302.00098","title":"Does Deep Active Learning Work in the Wild?","created":"2023-01-31","cited_by_count":1,"area":null},{"id":"2307.05508","title":"Human in the AI loop via xAI and Active Learning for Visual Inspection","created":"2023-07-03","cited_by_count":1,"area":null},{"id":"2412.02331","title":"Sample Efficient Robot Learning in Supervised Effect Prediction Tasks","created":"2024-12-03","cited_by_count":1,"area":null},{"id":"2511.06248","title":"Constraint-Informed Active Learning for End-to-End ACOPF Optimization Proxies","created":"2025-11-09","cited_by_count":1,"area":null},{"id":"2603.11525","title":"MDS-VQA: Model-Informed Data Selection for Video Quality Assessment","created":"2026-03-12","cited_by_count":1,"area":null},{"id":"2105.11492","title":"Adaptive Local Kernels Formulation of Mutual Information with Application to Active Post-Seismic Building Damage Inference","created":"2021-05-24","cited_by_count":0,"area":null},{"id":"2105.11990","title":"Optimal Sampling Density for Nonparametric Regression","created":"2021-05-25","cited_by_count":0,"area":null},{"id":"2204.12302","title":"From Limited Annotated Raw Material Data to Quality Production Data: A Case Study in the Milk Industry (Technical Report)","created":"2022-04-26","cited_by_count":0,"area":null},{"id":"2404.09953","title":"Classification Tree-based Active Learning: A Wrapper Approach","created":"2024-04-15","cited_by_count":0,"area":null},{"id":"2505.10799","title":"Cell Library Characterization for Composite Current Source Models Based on Gaussian Process Regression and Active Learning","created":"2025-05-16","cited_by_count":0,"area":null},{"id":"2604.02019","title":"Feature Weighting Improves Pool-Based Sequential Active Learning for Regression","created":"2026-04-02","cited_by_count":0,"area":null},{"id":"2604.12891","title":"TCL: Enabling Fast and Efficient Cross-Hardware Tensor Program Optimization via Continual Learning","created":"2026-04-14","cited_by_count":0,"area":null},{"id":"2606.12182","title":"How Low Can You Go? Active Learning for Sparse Model Discovery in the Ultra-Low-Data Limit","created":"2026-06-10","cited_by_count":0,"area":null}],"next_cursor":null}