{"total":21,"rows":[{"id":"2110.05365","title":"Intriguing Properties of Input-dependent Randomized Smoothing","created":"2021-10-11","cited_by_count":24,"area":null},{"id":"2206.07912","title":"Double Sampling Randomized Smoothing","created":"2022-06-16","cited_by_count":22,"area":null},{"id":"2309.13794","title":"Projected Randomized Smoothing for Certified Adversarial Robustness","created":"2023-09-25","cited_by_count":11,"area":null},{"id":"2309.16096","title":"Adversarial Examples Might be Avoidable: The Role of Data Concentration in Adversarial Robustness","created":"2023-09-28","cited_by_count":11,"area":null},{"id":"2204.05687","title":"3DeformRS: Certifying Spatial Deformations on Point Clouds","created":"2022-04-12","cited_by_count":10,"area":null},{"id":"2210.16140","title":"Localized Randomized Smoothing for Collective Robustness Certification","created":"2022-10-28","cited_by_count":10,"area":null},{"id":"2302.01961","title":"Asymmetric Certified Robustness via Feature-Convex Neural Networks","created":"2023-02-03","cited_by_count":8,"area":null},{"id":"2211.14207","title":"Invariance-Aware Randomized Smoothing Certificates","created":"2022-11-25","cited_by_count":7,"area":null},{"id":"2207.05327","title":"Certified Adversarial Robustness via Anisotropic Randomized Smoothing","created":"2022-07-12","cited_by_count":6,"area":null},{"id":"2209.15042","title":"Generalizability of Adversarial Robustness Under Distribution Shifts","created":"2022-09-29","cited_by_count":6,"area":null},{"id":"2206.02535","title":"Certified Robustness in Federated Learning","created":"2022-06-06","cited_by_count":4,"area":null},{"id":"2406.00826","title":"Policy Verification in Stochastic Dynamical Systems Using Logarithmic Neural Certificates","created":"2024-06-02","cited_by_count":4,"area":null},{"id":"2312.12608","title":"Rethinking Randomized Smoothing from the Perspective of Scalability","created":"2023-12-19","cited_by_count":3,"area":null},{"id":"2304.13019","title":"Certifying Ensembles: A General Certification Theory with S-Lipschitzness","created":"2023-04-25","cited_by_count":2,"area":null},{"id":"2206.10235","title":"Riemannian data-dependent randomized smoothing for neural networks certification","created":"2022-06-21","cited_by_count":1,"area":null},{"id":"2208.07464","title":"An Overview and Prospective Outlook on Robust Training and Certification of Machine Learning Models","created":"2022-08-15","cited_by_count":1,"area":null},{"id":"2405.14176","title":"Certified Robustness against Sparse Adversarial Perturbations via Data Localization","created":"2024-05-23","cited_by_count":1,"area":null},{"id":"2510.19977","title":"Towards Strong Certified Defense with Universal Asymmetric Randomization","created":"2025-10-22","cited_by_count":1,"area":null},{"id":"2202.04978","title":"Towards Assessing and Characterizing the Semantic Robustness of Face Recognition","created":"2022-02-10","cited_by_count":0,"area":null},{"id":"2310.07780","title":"Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches","created":"2023-10-11","cited_by_count":0,"area":null},{"id":"2511.09316","title":"AdaptDel: Adaptable Deletion Rate Randomized Smoothing for Certified Robustness","created":"2025-11-12","cited_by_count":0,"area":null}],"next_cursor":null}