{"total":12,"rows":[{"id":"2206.00423","title":"Open-environment Machine Learning","created":"2022-06-01","cited_by_count":48,"area":null},{"id":"2207.02121","title":"Adapting to Online Label Shift with Provable Guarantees","created":"2022-07-05","cited_by_count":26,"area":null},{"id":"2311.15502","title":"Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical","created":"2023-11-27","cited_by_count":10,"area":null},{"id":"2306.06894","title":"A Generalized Unbiased Risk Estimator for Learning with Augmented Classes","created":"2023-06-12","cited_by_count":2,"area":null},{"id":"2207.13274","title":"Learning from Positive and Unlabeled Data with Augmented Classes","created":"2022-07-27","cited_by_count":1,"area":null},{"id":"2211.10701","title":"Complementary Labels Learning with Augmented Classes","created":"2022-11-19","cited_by_count":0,"area":null},{"id":"2409.19600","title":"An Unbiased Risk Estimator for Partial Label Learning with Augmented Classes","created":"2024-09-29","cited_by_count":0,"area":null},{"id":"2411.02921","title":"Theoretically Guaranteed Distribution Adaptable Learning","created":"2024-11-05","cited_by_count":0,"area":null},{"id":"2412.02230","title":"Learning from Concealed Labels","created":"2024-12-03","cited_by_count":0,"area":null},{"id":"2412.02240","title":"ESA: Example Sieve Approach for Multi-Positive and Unlabeled Learning","created":"2024-12-03","cited_by_count":0,"area":null},{"id":"2505.09294","title":"On the Learning with Augmented Class via Forests","created":"2025-05-14","cited_by_count":0,"area":null},{"id":"2608.06896","title":"Recent advances in weakly supervised learning: New supervision paradigms, assumption relaxations, and practical solutions","created":"2026-08-07","cited_by_count":0,"area":null}],"next_cursor":null}