{"total":53,"rows":[{"id":"2209.11715","title":"The \"Beatrix'' Resurrections: Robust Backdoor Detection via Gram Matrices","created":"2022-09-23","cited_by_count":63,"area":null},{"id":"2208.12084","title":"Calibrated Selective Classification","created":"2022-08-25","cited_by_count":33,"area":null},{"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":"2206.09194","title":"Efficient Aggregated Kernel Tests using Incomplete $U$-statistics","created":"2022-06-18","cited_by_count":27,"area":null},{"id":"2102.05573","title":"A Witness Two-Sample Test","created":"2021-02-10","cited_by_count":23,"area":null},{"id":"2106.04590","title":"PEARL: Data Synthesis via Private Embeddings and Adversarial Reconstruction Learning","created":"2021-06-08","cited_by_count":23,"area":null},{"id":"2211.14908","title":"A Permutation-free Kernel Two-Sample Test","created":"2022-11-27","cited_by_count":21,"area":null},{"id":"2206.08843","title":"AutoML Two-Sample Test","created":"2022-06-17","cited_by_count":20,"area":null},{"id":"2210.13355","title":"Calibration tests beyond classification","created":"2022-10-21","cited_by_count":20,"area":null},{"id":"2106.07636","title":"Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data","created":"2021-06-14","cited_by_count":18,"area":null},{"id":"2106.03227","title":"Neural Tangent Kernel Maximum Mean Discrepancy","created":"2021-06-06","cited_by_count":15,"area":null},{"id":"2305.12511","title":"PCF-GAN: generating sequential data via the characteristic function of measures on the path space","created":"2023-05-21","cited_by_count":15,"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":"2110.14868","title":"An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddings","created":"2021-10-28","cited_by_count":12,"area":null},{"id":"2210.01742","title":"CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive Learning","created":"2022-10-04","cited_by_count":11,"area":null},{"id":"2210.13027","title":"E-Valuating Classifier Two-Sample Tests","created":"2022-10-24","cited_by_count":10,"area":null},{"id":"2302.10687","title":"Boosting the Power of Kernel Two-Sample Tests","created":"2023-02-21","cited_by_count":10,"area":null},{"id":"2203.08644","title":"Context-Aware Drift Detection","created":"2022-03-16","cited_by_count":9,"area":null},{"id":"2103.00394","title":"Convergence of Gaussian-smoothed optimal transport distance with sub-gamma distributions and dependent samples","created":"2021-02-28","cited_by_count":8,"area":null},{"id":"2105.03425","title":"Kernel Two-Sample Tests for Manifold Data","created":"2021-05-07","cited_by_count":8,"area":null},{"id":"2312.07790","title":"Characteristic Circuits","created":"2023-12-12","cited_by_count":8,"area":null},{"id":"2102.13079","title":"Quantization Algorithms for Random Fourier Features","created":"2021-02-25","cited_by_count":7,"area":null},{"id":"2302.07415","title":"Variable Selection for Kernel Two-Sample Tests","created":"2023-02-15","cited_by_count":7,"area":null},{"id":"2310.19384","title":"Deep anytime-valid hypothesis testing","created":"2023-10-30","cited_by_count":7,"area":null},{"id":"2203.12777","title":"Kernel Robust Hypothesis Testing","created":"2022-03-23","cited_by_count":6,"area":null},{"id":"2502.13570","title":"A Scalable Nystrom-Based Kernel Two-Sample Test with Permutations","created":"2025-02-19","cited_by_count":6,"area":null},{"id":"2510.11140","title":"DUAL: Learning Diverse Kernels for Aggregated Two-sample and Independence Testing","created":"2025-10-13","cited_by_count":6,"area":null},{"id":"2302.09930","title":"Nystr\\\"om $M$-Hilbert-Schmidt Independence Criterion","created":"2023-02-20","cited_by_count":5,"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":"2406.08401","title":"Nystr\\\"om Kernel Stein Discrepancy","created":"2024-06-12","cited_by_count":4,"area":null},{"id":"2306.09780","title":"Understanding Deep Generative Models with Generalized Empirical Likelihoods","created":"2023-06-16","cited_by_count":3,"area":null},{"id":"2607.21542","title":"Zero-Flow Two-Sample Tests","created":"2026-07-23","cited_by_count":3,"area":null},{"id":"2110.03118","title":"A fast and effective kernel two-sample test for large-scale data","created":"2021-10-07","cited_by_count":2,"area":null},{"id":"2202.03077","title":"Adversarial Attack and Defense for Non-Parametric Two-Sample Tests","created":"2022-02-07","cited_by_count":2,"area":null},{"id":"2410.13986","title":"Recurrent Neural Goodness-of-Fit Test for Time Series","created":"2024-10-17","cited_by_count":2,"area":null},{"id":"2412.05135","title":"The Polynomial Stein Discrepancy for Assessing Moment Convergence","created":"2024-12-06","cited_by_count":2,"area":null},{"id":"2511.19476","title":"FAST: Topology-Aware Frequency-Domain Distribution Matching for Coreset Selection","created":"2025-11-22","cited_by_count":2,"area":null},{"id":"2212.03317","title":"Drift Identification for L\\'{e}vy alpha-Stable Stochastic Systems","created":"2022-12-06","cited_by_count":1,"area":null},{"id":"2501.18006","title":"Topological Signatures of Adversaries in Multimodal Alignments","created":"2025-01-29","cited_by_count":1,"area":null},{"id":"2510.10477","title":"Anchor-based Maximum Discrepancy for Relative Similarity Testing","created":"2025-10-12","cited_by_count":1,"area":null},{"id":"2512.13997","title":"Maximum Mean Discrepancy with Unequal Sample Sizes via Generalized U-Statistics","created":"2025-12-16","cited_by_count":1,"area":null},{"id":"2110.15440","title":"HD-cos Networks: Efficient Neural Architectures for Secure Multi-Party Computation","created":"2021-10-28","cited_by_count":0,"area":null},{"id":"2210.05835","title":"Synthetic Power Analyses: Empirical Evaluation and Application to Cognitive Neuroimaging","created":"2022-10-11","cited_by_count":0,"area":null},{"id":"2303.08984","title":"Forecasting Particle Accelerator Interruptions Using Logistic LASSO Regression","created":"2023-03-15","cited_by_count":0,"area":null},{"id":"2304.13848","title":"Bootstrapped Edge Count Tests for Nonparametric Two-Sample Inference Under Heterogeneity","created":"2023-04-26","cited_by_count":0,"area":null},{"id":"2310.01109","title":"R-divergence for Estimating Model-oriented Distribution Discrepancy","created":"2023-10-02","cited_by_count":0,"area":null},{"id":"2411.03007","title":"Data Quality Awareness: A Journey from Traditional Data Management to Data Science Systems","created":"2024-11-05","cited_by_count":0,"area":null},{"id":"2505.11740","title":"Simple and Effective Specialized Representations for Fair Classifiers","created":"2025-05-16","cited_by_count":0,"area":null},{"id":"2505.20433","title":"Kernel Quantile Embeddings and Associated Probability Metrics","created":"2025-05-26","cited_by_count":0,"area":null},{"id":"2507.12843","title":"Are Two Datasets Close Enough With Statistical Significance? A Kernel Distributional Closeness Testing Approach","created":"2025-07-17","cited_by_count":0,"area":null},{"id":"2605.14284","title":"Smooth Multi-Policy Causal Effect Estimation in Longitudinal Settings","created":"2026-05-14","cited_by_count":0,"area":null},{"id":"2607.20119","title":"Directional Kernel Mean Difference: A Fast Signed Statistic for Univariate Distribution Comparison","created":"2026-07-22","cited_by_count":0,"area":null},{"id":"2608.01547","title":"Finite-Probe Total-Variation Certificates for Finite-Basis Drifting Models","created":"2026-08-03","cited_by_count":0,"area":null}],"next_cursor":null}