{"total":37,"rows":[{"id":"2210.12067","title":"Efficient Dataset Distillation Using Random Feature Approximation","created":"2022-10-21","cited_by_count":111,"area":null},{"id":"2205.12679","title":"Self-Guided Noise-Free Data Generation for Efficient Zero-Shot Learning","created":"2022-05-25","cited_by_count":50,"area":null},{"id":"2307.15967","title":"Graph Condensation for Inductive Node Representation Learning","created":"2023-07-29","cited_by_count":31,"area":null},{"id":"2301.09880","title":"Probabilistic Bilevel Coreset Selection","created":"2023-01-24","cited_by_count":27,"area":null},{"id":"2110.14094","title":"Learning-Augmented $k$-means Clustering","created":"2021-10-27","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":"2203.09572","title":"Triangle and Four Cycle Counting with Predictions in Graph Streams","created":"2022-03-17","cited_by_count":20,"area":null},{"id":"2312.01537","title":"Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents","created":"2023-12-03","cited_by_count":16,"area":null},{"id":"2106.12491","title":"Training Data Subset Selection for Regression with Controlled Generalization Error","created":"2021-06-23","cited_by_count":14,"area":null},{"id":"2210.14664","title":"Coresets for Vertical Federated Learning: Regularized Linear Regression and $K$-Means Clustering","created":"2022-10-26","cited_by_count":14,"area":null},{"id":"2110.08991","title":"Dimensionality Reduction for Wasserstein Barycenter","created":"2021-10-18","cited_by_count":13,"area":null},{"id":"2311.08675","title":"Refined Coreset Selection: Towards Minimal Coreset Size under Model Performance Constraints","created":"2023-11-15","cited_by_count":13,"area":null},{"id":"2409.12255","title":"Efficient Data Subset Selection to Generalize Training Across Models: Transductive and Inductive Networks","created":"2024-09-18","cited_by_count":13,"area":null},{"id":"2209.10614","title":"Learning-Augmented Algorithms for Online Linear and Semidefinite Programming","created":"2022-09-21","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":"2109.12534","title":"Data Summarization via Bilevel Optimization","created":"2021-09-26","cited_by_count":8,"area":null},{"id":"2110.15263","title":"Coresets for Time Series Clustering","created":"2021-10-28","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":"2107.00068","title":"Robust and Fully-Dynamic Coreset for Continuous-and-Bounded Learning (With Outliers) Problems","created":"2021-06-30","cited_by_count":6,"area":null},{"id":"2103.03574","title":"Extending Contrastive Learning to Unsupervised Coreset Selection","created":"2021-03-05","cited_by_count":4,"area":null},{"id":"2112.14793","title":"A sampling-based approach for efficient clustering in large datasets","created":"2021-12-29","cited_by_count":4,"area":null},{"id":"2303.04183","title":"Robustness-preserving Lifelong Learning via Dataset Condensation","created":"2023-03-07","cited_by_count":4,"area":null},{"id":"2309.02476","title":"Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning","created":"2023-09-05","cited_by_count":4,"area":null},{"id":"2504.14572","title":"Data Selection for ERMs","created":"2025-04-20","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":"2603.02488","title":"Learning-Augmented Moment Estimation on Time-Decay Models","created":"2026-03-03","cited_by_count":2,"area":null},{"id":"2109.01691","title":"ALLWAS: Active Learning on Language models in WASserstein space","created":"2021-09-03","cited_by_count":1,"area":null},{"id":"2210.04249","title":"Coresets for Relational Data and The Applications","created":"2022-10-09","cited_by_count":1,"area":null},{"id":"2407.13980","title":"Byzantine-tolerant distributed learning of finite mixture models","created":"2024-07-19","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":"2512.06950","title":"PARIS: Pruning Algorithm via the Representer theorem for Imbalanced Scenarios","created":"2025-12-07","cited_by_count":1,"area":null},{"id":"2306.15848","title":"Ordering for Non-Replacement SGD","created":"2023-06-28","cited_by_count":0,"area":null},{"id":"2408.14225","title":"Provable Imbalanced Point Clustering","created":"2024-08-26","cited_by_count":0,"area":null},{"id":"2501.12299","title":"Sublinear Variational Optimization of Gaussian Mixture Models with Millions to Billions of Parameters","created":"2025-01-21","cited_by_count":0,"area":null},{"id":"2505.12353","title":"Importance Sampling for Nonlinear Models","created":"2025-05-18","cited_by_count":0,"area":null},{"id":"2605.00185","title":"Fair Dataset Distillation via Cross-Group Barycenter Alignment","created":"2026-04-30","cited_by_count":0,"area":null},{"id":"2609.06394","title":"Beyond Worst-Case Coreset Bounds for $k$-Clustering via Determinantal Sampling","created":"2026-09-06","cited_by_count":0,"area":null}],"next_cursor":null}