{"total":21,"rows":[{"id":"2102.06879","title":"Learning from Similarity-Confidence Data","created":"2021-02-13","cited_by_count":19,"area":null},{"id":"2402.01922","title":"A General Framework for Learning from Weak Supervision","created":"2024-02-02","cited_by_count":7,"area":null},{"id":"2101.03419","title":"Training Deep Architectures Without End-to-End Backpropagation: A Survey on the Provably Optimal Methods","created":"2021-01-09","cited_by_count":5,"area":null},{"id":"2309.08216","title":"Unified Risk Analysis for Weakly Supervised Learning","created":"2023-09-15","cited_by_count":5,"area":null},{"id":"2206.05394","title":"Applications of Deep Learning in Fish Habitat Monitoring: A Tutorial and Survey","created":"2022-06-11","cited_by_count":4,"area":null},{"id":"2106.08864","title":"Multi-Class Classification from Single-Class Data with Confidences","created":"2021-06-16","cited_by_count":3,"area":null},{"id":"2104.09015","title":"Labels, Information, and Computation: Efficient Learning Using Sufficient Labels","created":"2021-04-19","cited_by_count":2,"area":null},{"id":"2203.12836","title":"Learning from Label Proportions with Instance-wise Consistency","created":"2022-03-24","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":"2101.01366","title":"A Symmetric Loss Perspective of Reliable Machine Learning","created":"2021-01-05","cited_by_count":0,"area":null},{"id":"2303.02341","title":"RoLNiP: Robust Learning Using Noisy Pairwise Comparisons","created":"2023-03-04","cited_by_count":0,"area":null},{"id":"2305.04539","title":"Q&A Label Learning","created":"2023-05-08","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":"2506.15686","title":"Learning from M-Tuple Dominant Positive and Unlabeled Data","created":"2025-05-25","cited_by_count":0,"area":null},{"id":"2507.07771","title":"A Unified Empirical Risk Minimization Framework for Flexible N-Tuples Weak Supervision","created":"2025-07-10","cited_by_count":0,"area":null},{"id":"2508.05108","title":"Learning from Similarity-Confidence and Confidence-Difference","created":"2025-08-07","cited_by_count":0,"area":null},{"id":"2511.22823","title":"A Unified and Stable Risk Minimization Framework for Weakly Supervised Learning with Theoretical Guarantees","created":"2025-11-28","cited_by_count":0,"area":null},{"id":"2603.00517","title":"FastBUS: A Fast Bayesian Framework for Unified Weakly-Supervised Learning","created":"2026-02-28","cited_by_count":0,"area":null},{"id":"2603.19713","title":"Binary Classification from Coupled Pairwise Labels","created":"2026-03-20","cited_by_count":0,"area":null},{"id":"2607.11947","title":"Generalized Distribution-Free Semi-Supervised Learning with Risk Rewrite","created":"2026-07-11","cited_by_count":0,"area":null},{"id":"2609.10994","title":"Importance Weighting for Unlabeled-unlabeled Learning under Distribution Shift","created":"2026-09-10","cited_by_count":0,"area":null}],"next_cursor":null}