{"total":46,"rows":[{"id":"2306.08777","title":"MMD-FUSE: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting","created":"2023-06-14","cited_by_count":33,"area":null},{"id":"2110.01593","title":"Generalized Kernel Thinning","created":"2021-10-04","cited_by_count":29,"area":null},{"id":"2107.09597","title":"Positively Weighted Kernel Quadrature via Subsampling","created":"2021-07-20","cited_by_count":25,"area":null},{"id":"2111.07941","title":"Distribution Compression in Near-linear Time","created":"2021-11-15","cited_by_count":19,"area":null},{"id":"2301.09517","title":"Sampling-based Nystr\\\"om Approximation and Kernel Quadrature","created":"2023-01-23","cited_by_count":17,"area":null},{"id":"2301.05974","title":"Compress Then Test: Powerful Kernel Testing in Near-linear Time","created":"2023-01-14","cited_by_count":14,"area":null},{"id":"2202.06891","title":"Counterfactual inference in sequential experiments","created":"2022-02-14","cited_by_count":13,"area":null},{"id":"2301.11674","title":"Optimally-Weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference","created":"2023-01-27","cited_by_count":13,"area":null},{"id":"2502.10600","title":"Weighted quantization using MMD: From mean field to mean shift via gradient flows","created":"2025-02-14","cited_by_count":12,"area":null},{"id":"2306.03955","title":"Kernel quadrature with randomly pivoted Cholesky","created":"2023-06-06","cited_by_count":11,"area":null},{"id":"2311.13548","title":"Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores Sampling","created":"2023-11-22","cited_by_count":10,"area":null},{"id":"2504.18830","title":"A Dictionary of Closed-Form Kernel Mean Embeddings","created":"2025-04-26","cited_by_count":9,"area":null},{"id":"2211.15070","title":"Online Kernel CUSUM for Change-Point Detection","created":"2022-11-28","cited_by_count":7,"area":null},{"id":"2301.11832","title":"SOBER: Highly Parallel Bayesian Optimization and Bayesian Quadrature over Discrete and Mixed Spaces","created":"2023-01-27","cited_by_count":7,"area":null},{"id":"2302.00845","title":"Coordinating Distributed Example Orders for Provably Accelerated Training","created":"2023-02-02","cited_by_count":7,"area":null},{"id":"2403.00999","title":"Distributional Dataset Distillation with Subtask Decomposition","created":"2024-03-01","cited_by_count":7,"area":null},{"id":"2502.12063","title":"Low-Rank Thinning","created":"2025-02-17","cited_by_count":7,"area":null},{"id":"2306.05843","title":"Adaptive Batch Sizes for Active Learning A Probabilistic Numerics Approach","created":"2023-06-09","cited_by_count":6,"area":null},{"id":"2602.10056","title":"WildCat: Near-Linear Attention in Theory and Practice","created":"2026-02-10","cited_by_count":6,"area":null},{"id":"2503.07084","title":"A Unified View of Optimal Kernel Hypothesis Testing","created":"2025-03-10","cited_by_count":5,"area":null},{"id":"2305.16527","title":"When can Regression-Adjusted Control Variates Help? Rare Events, Sobolev Embedding and Minimax Optimality","created":"2023-05-25","cited_by_count":4,"area":null},{"id":"2406.18334","title":"Efficient and Accurate Explanation Estimation with Distribution Compression","created":"2024-06-26","cited_by_count":4,"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":"2201.07130","title":"Online, Informative MCMC Thinning with Kernelized Stein Discrepancy","created":"2022-01-18","cited_by_count":3,"area":null},{"id":"2404.12219","title":"A Quadrature Approach for General-Purpose Batch Bayesian Optimization via Probabilistic Lifting","created":"2024-04-18","cited_by_count":3,"area":null},{"id":"2410.13749","title":"Supervised Kernel Thinning","created":"2024-10-17","cited_by_count":3,"area":null},{"id":"2405.04554","title":"Differentially Private Synthetic Data with Private Density Estimation","created":"2024-05-06","cited_by_count":2,"area":null},{"id":"2410.13381","title":"Learning Counterfactual Distributions via Kernel Nearest Neighbors","created":"2024-10-17","cited_by_count":2,"area":null},{"id":"2505.20754","title":"Stationary MMD Points","created":"2025-05-27","cited_by_count":2,"area":null},{"id":"2304.09053","title":"Bayes Hilbert Spaces for Posterior Approximation","created":"2023-04-18","cited_by_count":1,"area":null},{"id":"2310.14768","title":"Policy Gradient with Kernel Quadrature","created":"2023-10-23","cited_by_count":1,"area":null},{"id":"2502.12398","title":"Solving the Cold Start Problem on One's Own as an End User via Preference Transfer","created":"2025-02-18","cited_by_count":1,"area":null},{"id":"2510.03745","title":"Neural Low-Discrepancy Sequences","created":"2025-10-04","cited_by_count":1,"area":null},{"id":"2605.01263","title":"New Bounds for Kernel Sums via Fast Spherical Embeddings","created":"2026-05-02","cited_by_count":1,"area":null},{"id":"2605.28589","title":"Thinned Mean Field Langevin Dynamics","created":"2026-05-27","cited_by_count":1,"area":null},{"id":"2210.15060","title":"Optimal Sub-sampling to Boost Power of Kernel Sequential Change-point Detection","created":"2022-10-26","cited_by_count":0,"area":null},{"id":"2309.16809","title":"GraB-sampler: Optimal Permutation-based SGD Data Sampler for PyTorch","created":"2023-09-28","cited_by_count":0,"area":null},{"id":"2508.01392","title":"Quenched large deviations for Monte Carlo integration with Coulomb gases","created":"2025-08-02","cited_by_count":0,"area":null},{"id":"2509.17543","title":"Bilateral Distribution Compression: Reducing Both Data Size and Dimensionality","created":"2025-09-22","cited_by_count":0,"area":null},{"id":"2512.16105","title":"BayesSum: Bayesian Quadrature in Discrete Spaces","created":"2025-12-18","cited_by_count":0,"area":null},{"id":"2605.05705","title":"Convex-Geometric Error Bounds for Positive-Weight Kernel Quadrature","created":"2026-05-07","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":"2606.10944","title":"Express Language Modeling","created":"2026-06-09","cited_by_count":0,"area":null},{"id":"2606.15871","title":"Amortized quadrature for posterior expectations in inverse problems","created":"2026-06-14","cited_by_count":0,"area":null},{"id":"2607.20692","title":"DS@GT ARC at ImageCLEFmed GANs 2026: Geometric Filtering for Privacy-Preserving CT Slice Generation","created":"2026-07-22","cited_by_count":0,"area":null},{"id":"2609.06327","title":"Query-Oblivious Coresets for Softmax Attention: Improved Bounds and Efficient Constructions","created":"2026-09-06","cited_by_count":0,"area":null}],"next_cursor":null}