{"total":17,"rows":[{"id":"2110.01889","title":"Deep Neural Networks and Tabular Data: A Survey","created":"2021-10-05","cited_by_count":491,"area":null},{"id":"2209.08018","title":"IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective","created":"2022-09-16","cited_by_count":16,"area":null},{"id":"2406.17813","title":"Unsupervised Concept Drift Detection from Deep Learning Representations in Real-time","created":"2024-06-24","cited_by_count":16,"area":null},{"id":"2109.06088","title":"Concept Drift Detection in Federated Networked Systems","created":"2021-09-13","cited_by_count":10,"area":null},{"id":"2401.15770","title":"PILOT: Legal Case Outcome Prediction with Case Law","created":"2024-01-28","cited_by_count":6,"area":null},{"id":"2212.14720","title":"Learning from Data Streams: An Overview and Update","created":"2022-12-30","cited_by_count":5,"area":null},{"id":"2502.21286","title":"Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis","created":"2025-02-28","cited_by_count":5,"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":"2411.15616","title":"A Scalable Approach to Covariate and Concept Drift Management via Adaptive Data Segmentation","created":"2024-11-23","cited_by_count":2,"area":null},{"id":"2505.24149","title":"RCCDA: Adaptive Model Updates in the Presence of Concept Drift under a Constrained Resource Budget","created":"2025-05-30","cited_by_count":2,"area":null},{"id":"2105.14557","title":"Robust Dynamic Network Embedding via Ensembles","created":"2021-05-30","cited_by_count":1,"area":null},{"id":"2202.05996","title":"Mixture of Online and Offline Experts for Non-stationary Time Series","created":"2022-02-12","cited_by_count":0,"area":null},{"id":"2509.08176","title":"MARLINE: Multi-Source Mapping Transfer Learning for Non-Stationary Environments","created":"2025-09-09","cited_by_count":0,"area":null},{"id":"2509.08181","title":"Multi-Label Transfer Learning in Non-Stationary Data Streams","created":"2025-09-09","cited_by_count":0,"area":null},{"id":"2511.02452","title":"An Adaptive Sampling Framework for Detecting Localized Concept Drift under Label Scarcity","created":"2025-11-04","cited_by_count":0,"area":null},{"id":"2601.22509","title":"Keep Rehearsing and Refining: Lifelong Learning Vehicle Routing under Continually Drifting Tasks","created":"2026-01-30","cited_by_count":0,"area":null},{"id":"2608.02845","title":"NOMADD: Numerical Optimization of Models Adapting to Data Drift","created":"2026-08-03","cited_by_count":0,"area":null}],"next_cursor":null}