{"total":19,"rows":[{"id":"2202.05928","title":"Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data","created":"2022-02-11","cited_by_count":74,"area":null},{"id":"2402.15926","title":"Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency","created":"2024-02-24","cited_by_count":35,"area":null},{"id":"2201.12293","title":"Understanding Why Generalized Reweighting Does Not Improve Over ERM","created":"2022-01-28","cited_by_count":29,"area":null},{"id":"2303.07608","title":"On the Implicit Geometry of Cross-Entropy Parameterizations for Label-Imbalanced Data","created":"2023-03-14","cited_by_count":23,"area":null},{"id":"2203.13225","title":"Distributionally Robust Optimization via Ball Oracle Acceleration","created":"2022-03-24","cited_by_count":14,"area":null},{"id":"2303.04020","title":"When is Importance Weighting Correction Needed for Covariate Shift Adaptation?","created":"2023-03-07","cited_by_count":13,"area":null},{"id":"2302.02931","title":"Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shifts","created":"2023-02-06","cited_by_count":9,"area":null},{"id":"2403.12729","title":"Posterior Uncertainty Quantification in Neural Networks using Data Augmentation","created":"2024-03-18","cited_by_count":9,"area":null},{"id":"2209.09315","title":"Deep Linear Networks can Benignly Overfit when Shallow Ones Do","created":"2022-09-19","cited_by_count":8,"area":null},{"id":"2209.08745","title":"Importance Tempering: Group Robustness for Overparameterized Models","created":"2022-09-19","cited_by_count":6,"area":null},{"id":"2211.15724","title":"Malign Overfitting: Interpolation Can Provably Preclude Invariance","created":"2022-11-28","cited_by_count":6,"area":null},{"id":"2310.16499","title":"Data Optimization in Deep Learning: A Survey","created":"2023-10-25","cited_by_count":6,"area":null},{"id":"2206.12739","title":"On how to avoid exacerbating spurious correlations when models are overparameterized","created":"2022-06-25","cited_by_count":4,"area":null},{"id":"2303.07475","title":"General Loss Functions Lead to (Approximate) Interpolation in High Dimensions","created":"2023-03-13","cited_by_count":4,"area":null},{"id":"2406.11685","title":"Edge Classification on Graphs: New Directions in Topological Imbalance","created":"2024-06-17","cited_by_count":4,"area":null},{"id":"2205.13094","title":"Undersampling is a Minimax Optimal Robustness Intervention in Nonparametric Classification","created":"2022-05-26","cited_by_count":3,"area":null},{"id":"2405.06546","title":"Sharp analysis of out-of-distribution error for \"importance-weighted\" estimators in the overparameterized regime","created":"2024-05-10","cited_by_count":3,"area":null},{"id":"2601.12011","title":"Why Loss Re-weighting Works If You Stop Early: Training Dynamics of Unconstrained Features","created":"2026-01-17","cited_by_count":2,"area":null},{"id":"2409.04101","title":"Ultra-imbalanced classification guided by statistical information","created":"2024-09-06","cited_by_count":0,"area":null}],"next_cursor":null}