{"total":38,"rows":[{"id":"2303.04947","title":"InfoBatch: Lossless Training Speed Up by Unbiased Dynamic Data Pruning","created":"2023-03-08","cited_by_count":90,"area":null},{"id":"2302.06755","title":"Dataset Distillation with Convexified Implicit Gradients","created":"2023-02-13","cited_by_count":46,"area":null},{"id":"2106.04254","title":"Coresets for Classification -- Simplified and Strengthened","created":"2021-06-08","cited_by_count":28,"area":null},{"id":"2407.14245","title":"Dataset Distillation by Automatic Training Trajectories","created":"2024-07-19","cited_by_count":26,"area":null},{"id":"2202.12793","title":"Towards Optimal Lower Bounds for k-median and k-means Coresets","created":"2022-02-25","cited_by_count":23,"area":null},{"id":"2209.08554","title":"Pruning Neural Networks via Coresets and Convex Geometry: Towards No Assumptions","created":"2022-09-18","cited_by_count":20,"area":null},{"id":"2107.06615","title":"Oblivious sketching for logistic regression","created":"2021-07-14","cited_by_count":18,"area":null},{"id":"2111.03044","title":"A Unified Approach to Coreset Learning","created":"2021-11-04","cited_by_count":18,"area":null},{"id":"2203.04370","title":"New Coresets for Projective Clustering and Applications","created":"2022-03-08","cited_by_count":16,"area":null},{"id":"2402.17327","title":"Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond","created":"2024-02-27","cited_by_count":15,"area":null},{"id":"2303.05151","title":"Provable Data Subset Selection For Efficient Neural Network Training","created":"2023-03-09","cited_by_count":14,"area":null},{"id":"2203.13568","title":"$p$-Generalized Probit Regression and Scalable Maximum Likelihood Estimation via Sketching and Coresets","created":"2022-03-25","cited_by_count":11,"area":null},{"id":"2110.03195","title":"Coresets for Decision Trees of Signals","created":"2021-10-07","cited_by_count":10,"area":null},{"id":"2410.13761","title":"GDeR: Safeguarding Efficiency, Balancing, and Robustness via Prototypical Graph Pruning","created":"2024-10-17","cited_by_count":8,"area":null},{"id":"2112.02504","title":"A Novel Sequential Coreset Method for Gradient Descent Algorithms","created":"2021-12-05","cited_by_count":7,"area":null},{"id":"2307.08086","title":"Dataset Distillation Meets Provable Subset Selection","created":"2023-07-16","cited_by_count":7,"area":null},{"id":"2203.03009","title":"Coresets for Data Discretization and Sine Wave Fitting","created":"2022-03-06","cited_by_count":6,"area":null},{"id":"2203.04075","title":"Obstacle Aware Sampling for Path Planning","created":"2022-03-08","cited_by_count":6,"area":null},{"id":"2210.04260","title":"Coresets for Wasserstein Distributionally Robust Optimization Problems","created":"2022-10-09","cited_by_count":6,"area":null},{"id":"2403.00680","title":"Scalable Learning of Item Response Theory Models","created":"2024-03-01","cited_by_count":6,"area":null},{"id":"2111.03046","title":"Introduction to Coresets: Approximated Mean","created":"2021-11-04","cited_by_count":5,"area":null},{"id":"2312.13385","title":"ORBSLAM3-Enhanced Autonomous Toy Drones: Pioneering Indoor Exploration","created":"2023-12-20","cited_by_count":5,"area":null},{"id":"2305.11980","title":"AutoCoreset: An Automatic Practical Coreset Construction Framework","created":"2023-05-19","cited_by_count":4,"area":null},{"id":"2502.16703","title":"Subsampling Graphs with GNN Performance Guarantees","created":"2025-02-23","cited_by_count":4,"area":null},{"id":"2305.14113","title":"On the Size and Approximation Error of Distilled Sets","created":"2023-05-23","cited_by_count":3,"area":null},{"id":"2406.00328","title":"Optimal bounds for $\\ell_p$ sensitivity sampling via $\\ell_2$ augmentation","created":"2024-06-01","cited_by_count":3,"area":null},{"id":"2607.02721","title":"Provable Pruning for Efficient 3D Gaussian Splatting via Coresets","created":"2026-07-02","cited_by_count":2,"area":null},{"id":"2312.15762","title":"On Robust Wasserstein Barycenter: The Model and Algorithm","created":"2023-12-25","cited_by_count":1,"area":null},{"id":"2412.20189","title":"Accurate Coresets for Latent Variable Models and Regularized Regression","created":"2024-12-28","cited_by_count":1,"area":null},{"id":"2501.14253","title":"Distributionally Robust Coreset Selection under Covariate Shift","created":"2025-01-24","cited_by_count":1,"area":null},{"id":"2507.13061","title":"Advancing Complex Wide-Area Scene Understanding with Hierarchical Coresets Selection","created":"2025-07-17","cited_by_count":1,"area":null},{"id":"2507.16729","title":"Improving Model Classification by Optimizing the Training Dataset","created":"2025-07-22","cited_by_count":1,"area":null},{"id":"2210.04807","title":"Efficient NTK using Dimensionality Reduction","created":"2022-10-10","cited_by_count":0,"area":null},{"id":"2306.00732","title":"Sharper Bounds for $\\ell_p$ Sensitivity Sampling","created":"2023-06-01","cited_by_count":0,"area":null},{"id":"2410.17945","title":"Theoretically Grounded Pruning of Large Ground Sets for Constrained, Discrete Optimization","created":"2024-10-23","cited_by_count":0,"area":null},{"id":"2507.14631","title":"$k$-PCA for (non-squared) Euclidean Distances: Polynomial Time Approximation","created":"2025-07-19","cited_by_count":0,"area":null},{"id":"2601.00361","title":"Deterministic Coreset for Lp Subspace","created":"2026-01-01","cited_by_count":0,"area":null},{"id":"2603.19792","title":"Scalable Learning of Multivariate Distributions via Coresets","created":"2026-03-20","cited_by_count":0,"area":null}],"next_cursor":null}