{"total":26,"rows":[{"id":"2510.17998","title":"SimBA: Simplifying Benchmark Analysis Using Performance Matrices Alone","created":"2025-10-20","cited_by_count":6,"area":null},{"id":"2602.14696","title":"A Critical Look at Targeted Instruction Selection: Disentangling What Matters (and What Doesn't)","created":"2026-02-16","cited_by_count":4,"area":null},{"id":"2511.18831","title":"VideoCompressa: Data-Efficient Video Understanding via Joint Temporal Compression and Spatial Reconstruction","created":"2025-11-24","cited_by_count":3,"area":null},{"id":"2507.12011","title":"DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning","created":"2025-07-16","cited_by_count":2,"area":null},{"id":"2602.23128","title":"Bound to Disagree: Generalization Bounds via Certifiable Surrogates","created":"2026-02-26","cited_by_count":2,"area":null},{"id":"2605.25773","title":"Efficient Benchmarking Is Just Feature Selection and Multiple Regression","created":"2026-05-25","cited_by_count":2,"area":null},{"id":"2505.21438","title":"Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution","created":"2025-05-27","cited_by_count":1,"area":null},{"id":"2602.16073","title":"ScenicRules: An Autonomous Driving Benchmark with Multi-Objective Specifications and Abstract Scenarios","created":"2026-02-17","cited_by_count":1,"area":null},{"id":"2507.02979","title":"Iterative Misclassification Error Training (IMET): An Optimized Neural Network Training Technique for Image Classification","created":"2025-07-01","cited_by_count":0,"area":null},{"id":"2509.21748","title":"SubZeroCore: A Submodular Approach with Zero Training for Coreset Selection","created":"2025-09-26","cited_by_count":0,"area":null},{"id":"2510.21286","title":"Adaptive Data Selection for Multi-Layer Perceptron Training: A Sub-linear Value-Driven Method","created":"2025-10-24","cited_by_count":0,"area":null},{"id":"2602.19113","title":"Learning from Complexity: Exploring Dynamic Sample Pruning of Spatio-Temporal Training","created":"2026-02-22","cited_by_count":0,"area":null},{"id":"2602.19789","title":"Position: Stop Preaching and Start Practising Data Frugality for Responsible Development of AI","created":"2026-02-23","cited_by_count":0,"area":null},{"id":"2602.23358","title":"A Dataset is Worth 1 MB","created":"2026-02-26","cited_by_count":0,"area":null},{"id":"2605.18012","title":"SAS: Semantic-aware Sampling for Generative Dataset Distillation","created":"2026-05-18","cited_by_count":0,"area":null},{"id":"2606.04100","title":"Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials","created":"2026-06-02","cited_by_count":0,"area":null},{"id":"2607.01089","title":"Group-invariant Coresets for Data-efficient Active Learning","created":"2026-07-01","cited_by_count":0,"area":null},{"id":"2607.09100","title":"A Coreset Selection Framework with Ensemble Aggregation for Image Classification","created":"2026-07-10","cited_by_count":0,"area":null},{"id":"2607.14897","title":"Selectivity Drives Efficiency: Dataset Pruning for Visual Place Recognition","created":"2026-07-16","cited_by_count":0,"area":null},{"id":"2607.19315","title":"ERank in Latent Space as an Image-Complexity and Richness Measure","created":"2026-07-21","cited_by_count":0,"area":null},{"id":"2607.25318","title":"Dataset Distillation Based on Saliency-Driven Prototype Alignment","created":"2026-07-28","cited_by_count":0,"area":null},{"id":"2607.27632","title":"First-order Constrained Trilevel Optimization Over Distributed Networks for Robust Coreset Selection","created":"2026-07-30","cited_by_count":0,"area":null},{"id":"2608.16101","title":"EMS Coreset: An Efficient Expectation-Maximization Algorithm for Sinkhorn Coreset","created":"2026-08-17","cited_by_count":0,"area":null},{"id":"2608.21754","title":"Fidelity-Diversity-Consistency (FDC): Data Pruning for Remote Sensing Change Detection","created":"2026-08-22","cited_by_count":0,"area":null},{"id":"2609.05877","title":"A budget-dependent crossover between coverage- and response-based training-set selection for machine-learned interatomic potentials","created":"2026-09-05","cited_by_count":0,"area":null},{"id":"2610.10347","title":"Dataset Pruning from First Principles: A Label-Free Linear Programming Approach","created":"2026-10-07","cited_by_count":0,"area":null}],"next_cursor":null}