{"total":20,"rows":[{"id":"2207.08768","title":"Rank-based Decomposable Losses in Machine Learning: A Survey","created":"2022-07-18","cited_by_count":26,"area":null},{"id":"2311.01106","title":"In Defense of Softmax Parametrization for Calibrated and Consistent Learning to Defer","created":"2023-11-02","cited_by_count":26,"area":null},{"id":"2106.03300","title":"Sum of Ranked Range Loss for Supervised Learning","created":"2021-06-07","cited_by_count":23,"area":null},{"id":"2407.07140","title":"Cardinality-Aware Set Prediction and Top-$k$ Classification","created":"2024-07-09","cited_by_count":23,"area":null},{"id":"2206.14707","title":"An Embedding Framework for the Design and Analysis of Consistent Polyhedral Surrogates","created":"2022-06-29","cited_by_count":14,"area":null},{"id":"2211.01407","title":"On the Informativeness of Supervision Signals","created":"2022-11-02","cited_by_count":12,"area":null},{"id":"2112.14869","title":"Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and Adaptivity","created":"2021-12-30","cited_by_count":11,"area":null},{"id":"2202.02193","title":"Stochastic smoothing of the top-K calibrated hinge loss for deep imbalanced classification","created":"2022-02-04","cited_by_count":10,"area":null},{"id":"2107.10960","title":"Implicit Rate-Constrained Optimization of Non-decomposable Objectives","created":"2021-07-23","cited_by_count":9,"area":null},{"id":"2209.01398","title":"Optimizing Partial Area Under the Top-k Curve: Theory and Practice","created":"2022-09-03","cited_by_count":9,"area":null},{"id":"2207.08873","title":"Consistent Polyhedral Surrogates for Top-$k$ Classification and Variants","created":"2022-07-18","cited_by_count":8,"area":null},{"id":"2504.12988","title":"Why Ask One When You Can Ask $k$? Learning-to-Defer to the Top-$k$ Experts","created":"2025-04-17","cited_by_count":7,"area":null},{"id":"2211.04161","title":"Theoretical analysis and experimental validation of volume bias of soft Dice optimized segmentation maps in the context of inherent uncertainty","created":"2022-11-08","cited_by_count":6,"area":null},{"id":"2109.11282","title":"Unbiased Loss Functions for Multilabel Classification with Missing Labels","created":"2021-09-23","cited_by_count":5,"area":null},{"id":"2403.19625","title":"Top-$k$ Classification and Cardinality-Aware Prediction","created":"2024-03-28","cited_by_count":5,"area":null},{"id":"2109.02345","title":"Tensor Normalization and Full Distribution Training","created":"2021-09-06","cited_by_count":3,"area":null},{"id":"2112.08851","title":"Classification Under Ambiguity: When Is Average-K Better Than Top-K?","created":"2021-12-16","cited_by_count":1,"area":null},{"id":"2212.14106","title":"Provable Robust Saliency-based Explanations","created":"2022-12-28","cited_by_count":1,"area":null},{"id":"2411.03779","title":"Multilingual hierarchical classification of job advertisements for job vacancy statistics","created":"2024-11-06","cited_by_count":1,"area":null},{"id":"2411.11436","title":"Implicit Regularization for Multi-label Feature Selection","created":"2024-11-18","cited_by_count":0,"area":null}],"next_cursor":null}