{"total":79,"rows":[{"id":"2107.07075","title":"Deep Learning on a Data Diet: Finding Important Examples Early in Training","created":"2021-07-15","cited_by_count":533,"area":null},{"id":"2111.11210","title":"GCR: Gradient Coreset Based Replay Buffer Selection For Continual Learning","created":"2021-11-18","cited_by_count":95,"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":"2302.06755","title":"Dataset Distillation with Convexified Implicit Gradients","created":"2023-02-13","cited_by_count":46,"area":null},{"id":"2403.07362","title":"Challenging Forgets: Unveiling the Worst-Case Forget Sets in Machine Unlearning","created":"2024-03-12","cited_by_count":44,"area":"safety"},{"id":"2106.04254","title":"Coresets for Classification -- Simplified and Strengthened","created":"2021-06-08","cited_by_count":28,"area":null},{"id":"2108.00219","title":"Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximization","created":"2021-07-31","cited_by_count":28,"area":null},{"id":"2301.09880","title":"Probabilistic Bilevel Coreset Selection","created":"2023-01-24","cited_by_count":27,"area":null},{"id":"2209.10015","title":"Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics","created":"2022-09-20","cited_by_count":26,"area":null},{"id":"2107.06869","title":"Core-set Sampling for Efficient Neural Architecture Search","created":"2021-07-08","cited_by_count":23,"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":"2107.13098","title":"A Tale Of Two Long Tails","created":"2021-07-27","cited_by_count":19,"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":"2112.12194","title":"Surrogate Likelihoods for Variational Annealed Importance Sampling","created":"2021-12-22","cited_by_count":14,"area":null},{"id":"2106.14952","title":"Adversarial Robustness of Streaming Algorithms through Importance Sampling","created":"2021-06-28","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":"2203.05723","title":"Bayesian inference via sparse Hamiltonian flows","created":"2022-03-11","cited_by_count":12,"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":"2302.06960","title":"Data pruning and neural scaling laws: fundamental limitations of score-based algorithms","created":"2023-02-14","cited_by_count":11,"area":null},{"id":"2310.08782","title":"Selectivity Drives Productivity: Efficient Dataset Pruning for Enhanced Transfer Learning","created":"2023-10-13","cited_by_count":11,"area":null},{"id":"2403.07854","title":"Distilling the Knowledge in Data Pruning","created":"2024-03-12","cited_by_count":11,"area":"distill"},{"id":"2110.14432","title":"Iterative Teaching by Label Synthesis","created":"2021-10-27","cited_by_count":10,"area":null},{"id":"2203.09675","title":"Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement","created":"2022-03-18","cited_by_count":9,"area":null},{"id":"2409.01081","title":"Beyond Efficiency: Molecular Data Pruning for Enhanced Generalization","created":"2024-09-02","cited_by_count":9,"area":null},{"id":"2109.12534","title":"Data Summarization via Bilevel Optimization","created":"2021-09-26","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":"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":"2303.02095","title":"Data-Efficient Training of CNNs and Transformers with Coresets: A Stability Perspective","created":"2023-03-03","cited_by_count":6,"area":null},{"id":"2306.00361","title":"Sharded Bayesian Additive Regression Trees","created":"2023-06-01","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":"2112.03152","title":"Bounding Wasserstein distance with couplings","created":"2021-12-06","cited_by_count":5,"area":null},{"id":"2210.11717","title":"A Survey of Dataset Refinement for Problems in Computer Vision Datasets","created":"2022-10-21","cited_by_count":5,"area":null},{"id":"2301.06163","title":"A Coreset Learning Reality Check","created":"2023-01-15","cited_by_count":5,"area":null},{"id":"2310.17063","title":"Coreset Markov Chain Monte Carlo","created":"2023-10-25","cited_by_count":5,"area":null},{"id":"2103.03574","title":"Extending Contrastive Learning to Unsupervised Coreset Selection","created":"2021-03-05","cited_by_count":4,"area":null},{"id":"2104.13114","title":"One Backward from Ten Forward, Subsampling for Large-Scale Deep Learning","created":"2021-04-27","cited_by_count":4,"area":null},{"id":"2211.02377","title":"Black-box Coreset Variational Inference","created":"2022-11-04","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":"2303.11278","title":"Bayesian Pseudo-Coresets via Contrastive Divergence","created":"2023-03-20","cited_by_count":4,"area":null},{"id":"2304.11251","title":"Machine Learning and the Future of Bayesian Computation","created":"2023-04-21","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":"2105.09474","title":"Quantifying sources of uncertainty in drug discovery predictions with probabilistic models","created":"2021-05-18","cited_by_count":3,"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":"2405.11780","title":"General bounds on the quality of Bayesian coresets","created":"2024-05-20","cited_by_count":2,"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":"2606.08574","title":"OrderDP: A Theoretically Guaranteed Lossless Dynamic Data Pruning Framework","created":"2026-06-07","cited_by_count":2,"area":null},{"id":"2201.05206","title":"Reproducible, incremental representation learning with Rosetta VAE","created":"2022-01-13","cited_by_count":1,"area":null},{"id":"2204.08847","title":"Compressed Empirical Measures (in finite dimensions)","created":"2022-04-19","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":"2211.00246","title":"Batch Active Learning from the Perspective of Sparse Approximation","created":"2022-11-01","cited_by_count":1,"area":null},{"id":"2304.09053","title":"Bayes Hilbert Spaces for Posterior Approximation","created":"2023-04-18","cited_by_count":1,"area":null},{"id":"2311.01409","title":"Accurate and Scalable Stochastic Gaussian Process Regression via Learnable Coreset-based Variational Inference","created":"2023-11-02","cited_by_count":1,"area":null},{"id":"2410.05800","title":"Core Tokensets for Data-efficient Sequential Training of Transformers","created":"2024-10-08","cited_by_count":1,"area":null},{"id":"2410.22872","title":"Data subsampling for Poisson regression with pth-root-link","created":"2024-10-30","cited_by_count":1,"area":null},{"id":"2502.04907","title":"Scalable Learning from Probability Measures with Mean Measure Quantization","created":"2025-02-07","cited_by_count":1,"area":null},{"id":"2506.08060","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques","created":"2025-06-09","cited_by_count":1,"area":null},{"id":"2605.21765","title":"Position: The Time for Sampling Is Now! Charting a New Course for Bayesian Deep Learning","created":"2026-05-20","cited_by_count":1,"area":null},{"id":"2105.03650","title":"How To Train Your Program: a Probabilistic Programming Pattern for Bayesian Learning From Data","created":"2021-05-08","cited_by_count":0,"area":null},{"id":"2109.04698","title":"Face-NMS: A Core-set Selection Approach for Efficient Face Recognition","created":"2021-09-10","cited_by_count":0,"area":null},{"id":"2112.03773","title":"On the Effectiveness of Mode Exploration in Bayesian Model Averaging for Neural Networks","created":"2021-12-07","cited_by_count":0,"area":null},{"id":"2307.03927","title":"Fast Empirical Scenarios","created":"2023-07-08","cited_by_count":0,"area":null},{"id":"2311.04158","title":"Computing Approximate $\\ell_p$ Sensitivities","created":"2023-11-07","cited_by_count":0,"area":null},{"id":"2402.06940","title":"Efficient Incremental Belief Updates Using Weighted Virtual Observations","created":"2024-02-10","cited_by_count":0,"area":null},{"id":"2402.09598","title":"MCMC-driven learning","created":"2024-02-14","cited_by_count":0,"area":null},{"id":"2405.17627","title":"Salutary Labeling with Zero Human Annotation","created":"2024-05-27","cited_by_count":0,"area":null},{"id":"2406.05242","title":"Markov chain Monte Carlo without evaluating the target: an auxiliary variable approach","created":"2024-06-07","cited_by_count":0,"area":null},{"id":"2408.06257","title":"Reciprocal Learning","created":"2024-08-12","cited_by_count":0,"area":null},{"id":"2408.16268","title":"UDD: Dataset Distillation via Mining Underutilized Regions","created":"2024-08-29","cited_by_count":0,"area":null},{"id":"2410.18973","title":"Tuning-Free Coreset Markov Chain Monte Carlo via Hot DoG","created":"2024-10-24","cited_by_count":0,"area":null},{"id":"2502.05725","title":"Predictive Coresets","created":"2025-02-08","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":"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},{"id":"2608.16101","title":"EMS Coreset: An Efficient Expectation-Maximization Algorithm for Sinkhorn Coreset","created":"2026-08-17","cited_by_count":0,"area":null}],"next_cursor":null}