{"total":37,"rows":[{"id":"2210.11620","title":"LOT: Layer-wise Orthogonal Training on Improving $\\ell_2$ Certified Robustness","created":"2022-10-20","cited_by_count":33,"area":null},{"id":"2110.06850","title":"Boosting the Certified Robustness of L-infinity Distance Nets","created":"2021-10-13","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":"2402.02263","title":"MixedNUTS: Training-Free Accuracy-Robustness Balance via Nonlinearly Mixed Classifiers","created":"2024-02-03","cited_by_count":16,"area":null},{"id":"2402.06244","title":"Quantifying and Enhancing Multi-modal Robustness with Modality Preference","created":"2024-02-09","cited_by_count":16,"area":null},{"id":"2209.05055","title":"CARE: Certifiably Robust Learning with Reasoning via Variational Inference","created":"2022-09-12","cited_by_count":10,"area":null},{"id":"2402.19322","title":"Verification of Neural Networks' Global Robustness","created":"2024-02-29","cited_by_count":10,"area":null},{"id":"2206.03362","title":"Building Robust Ensembles via Margin Boosting","created":"2022-06-07","cited_by_count":8,"area":null},{"id":"2205.13909","title":"(De-)Randomized Smoothing for Decision Stump Ensembles","created":"2022-05-27","cited_by_count":5,"area":null},{"id":"2211.02578","title":"Data Models for Dataset Drift Controls in Machine Learning With Optical Images","created":"2022-11-04","cited_by_count":5,"area":null},{"id":"2309.11005","title":"It's Simplex! Disaggregating Measures to Improve Certified Robustness","created":"2023-09-20","cited_by_count":4,"area":null},{"id":"2310.02513","title":"A Recipe for Improved Certifiable Robustness","created":"2023-10-04","cited_by_count":4,"area":null},{"id":"2407.02811","title":"SPLITZ: Certifiable Robustness via Split Lipschitz Randomized Smoothing","created":"2024-07-03","cited_by_count":4,"area":null},{"id":"2305.03626","title":"Verifiable Learning for Robust Tree Ensembles","created":"2023-05-05","cited_by_count":3,"area":null},{"id":"2211.10882","title":"Multi-head Ensemble of Smoothed Classifiers for Certified Robustness","created":"2022-11-20","cited_by_count":2,"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":"2306.06193","title":"Consistent Explanations in the Face of Model Indeterminacy via Ensembling","created":"2023-06-09","cited_by_count":2,"area":null},{"id":"2409.08143","title":"Effective Segmentation of Post-Treatment Gliomas Using Simple Approaches: Artificial Sequence Generation and Ensemble Models","created":"2024-09-12","cited_by_count":2,"area":null},{"id":"2601.18513","title":"LipNeXt: Scaling up Lipschitz-based Certified Robustness to Billion-parameter Models","created":"2026-01-26","cited_by_count":2,"area":null},{"id":"2204.10839","title":"How Sampling Impacts the Robustness of Stochastic Neural Networks","created":"2022-04-22","cited_by_count":1,"area":null},{"id":"2205.06154","title":"Smooth-Reduce: Leveraging Patches for Improved Certified Robustness","created":"2022-05-12","cited_by_count":1,"area":null},{"id":"2206.00278","title":"On the Perils of Cascading Robust Classifiers","created":"2022-06-01","cited_by_count":1,"area":null},{"id":"2310.05959","title":"Automating global landslide detection with heterogeneous ensemble deep-learning classification","created":"2023-09-12","cited_by_count":1,"area":null},{"id":"2312.09020","title":"Exploring Transferability for Randomized Smoothing","created":"2023-12-14","cited_by_count":1,"area":null},{"id":"2404.05350","title":"Certified PEFTSmoothing: Parameter-Efficient Fine-Tuning with Randomized Smoothing","created":"2024-04-08","cited_by_count":1,"area":null},{"id":"2410.06895","title":"Average Certified Radius is a Poor Metric for Randomized Smoothing","created":"2024-10-09","cited_by_count":1,"area":null},{"id":"2410.08950","title":"On the Adversarial Transferability of Generalized \"Skip Connections\"","created":"2024-10-11","cited_by_count":1,"area":null},{"id":"2410.24214","title":"ARQ: A Mixed-Precision Quantization Framework for Accurate and Certifiably Robust DNNs","created":"2024-10-31","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":"2605.01519","title":"Certified vs. Empirical Adversarial Robust-ness via Hybrid Convolutions with Attention Stochasticity","created":"2026-05-02","cited_by_count":1,"area":null},{"id":"2606.01437","title":"CEAR: Certified Ensemble Adversarial Robustness in DNNs","created":"2026-05-31","cited_by_count":1,"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":"2402.14988","title":"Verifiable Boosted Tree Ensembles","created":"2024-02-22","cited_by_count":0,"area":null},{"id":"2406.10087","title":"Provably Robust Pre-Trained Ensembles for Biomarker-Based Cancer Classification","created":"2024-06-14","cited_by_count":0,"area":null},{"id":"2411.08933","title":"Confidence-aware Denoised Fine-tuning of Off-the-shelf Models for Certified Robustness","created":"2024-11-13","cited_by_count":0,"area":null},{"id":"2507.10048","title":"On the Efficiency of Training Robust Decision Trees","created":"2025-07-14","cited_by_count":0,"area":null},{"id":"2606.27832","title":"USAD: Uncertainty-aware Statistical Adversarial Detection","created":"2026-06-26","cited_by_count":0,"area":null}],"next_cursor":null}