{"total":39,"rows":[{"id":"2110.13771","title":"AugMax: Adversarial Composition of Random Augmentations for Robust Training","created":"2021-10-26","cited_by_count":90,"area":null},{"id":"2102.04716","title":"Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training","created":"2021-02-09","cited_by_count":59,"area":null},{"id":"2106.07904","title":"Probabilistic Margins for Instance Reweighting in Adversarial Training","created":"2021-06-15","cited_by_count":57,"area":null},{"id":"2106.06196","title":"CausalAdv: Adversarial Robustness through the Lens of Causality","created":"2021-06-11","cited_by_count":37,"area":null},{"id":"2206.05483","title":"Bilateral Dependency Optimization: Defending Against Model-inversion Attacks","created":"2022-06-11","cited_by_count":30,"area":null},{"id":"2305.16035","title":"Detecting Adversarial Data by Probing Multiple Perturbations Using Expected Perturbation Score","created":"2023-05-25","cited_by_count":20,"area":null},{"id":"2402.16041","title":"Detecting Machine-Generated Texts by Multi-Population Aware Optimization for Maximum Mean Discrepancy","created":"2024-02-25","cited_by_count":20,"area":null},{"id":"2312.08890","title":"Defenses in Adversarial Machine Learning: A Survey","created":"2023-12-13","cited_by_count":14,"area":null},{"id":"2403.12636","title":"A Practical Guide to Sample-based Statistical Distances for Evaluating Generative Models in Science","created":"2024-03-19","cited_by_count":14,"area":null},{"id":"2206.07314","title":"Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack","created":"2022-06-15","cited_by_count":13,"area":null},{"id":"2209.14826","title":"Towards Lightweight Black-Box Attacks against Deep Neural Networks","created":"2022-09-29","cited_by_count":13,"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":"2206.04287","title":"Evaluating Aleatoric Uncertainty via Conditional Generative Models","created":"2022-06-09","cited_by_count":10,"area":null},{"id":"2204.05427","title":"Generalizing Adversarial Explanations with Grad-CAM","created":"2022-04-11","cited_by_count":9,"area":null},{"id":"2305.16446","title":"The Representation Jensen-Shannon Divergence","created":"2023-05-25","cited_by_count":9,"area":null},{"id":"2408.05854","title":"On the Robustness of Kernel Goodness-of-Fit Tests","created":"2024-08-11","cited_by_count":7,"area":null},{"id":"2208.13838","title":"Towards Adversarial Purification using Denoising AutoEncoders","created":"2022-08-29","cited_by_count":6,"area":null},{"id":"2406.00685","title":"Improving Accuracy-robustness Trade-off via Pixel Reweighted Adversarial Training","created":"2024-06-02","cited_by_count":6,"area":null},{"id":"2412.00613","title":"A Unified Data Representation Learning for Non-parametric Two-sample Testing","created":"2024-11-30","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":"2503.02169","title":"One Stone, Two Birds: Enhancing Adversarial Defense Through the Lens of Distributional Discrepancy","created":"2025-03-04","cited_by_count":3,"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":"2409.06890","title":"Learning Representations for Independence Testing","created":"2024-09-10","cited_by_count":2,"area":null},{"id":"2502.12859","title":"PAFT: Prompt-Agnostic Fine-Tuning","created":"2025-02-18","cited_by_count":2,"area":null},{"id":"2506.06027","title":"Sample-Specific Noise Injection For Diffusion-Based Adversarial Purification","created":"2025-06-06","cited_by_count":2,"area":null},{"id":"2405.06003","title":"Binary Hypothesis Testing for Softmax Models and Leverage Score Models","created":"2024-05-09","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":"2502.03982","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","created":"2025-02-06","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":"2606.08460","title":"LOTTERY: Learning from Reference-Only Samples in Two-Sample Testing under Size Asymmetry","created":"2026-06-07","cited_by_count":1,"area":null},{"id":"2111.15518","title":"Detecting Adversaries, yet Faltering to Noise? Leveraging Conditional Variational AutoEncoders for Adversary Detection in the Presence of Noisy Images","created":"2021-11-28","cited_by_count":0,"area":null},{"id":"2205.07859","title":"Btech thesis report on adversarial attack detection and purification of adverserially attacked images","created":"2022-05-09","cited_by_count":0,"area":null},{"id":"2409.19619","title":"Discerning the Chaos: Detecting Adversarial Perturbations while Disentangling Intentional from Unintentional Noises","created":"2024-09-29","cited_by_count":0,"area":null},{"id":"2410.20250","title":"Certifiably Robust Model Evaluation in Federated Learning under Meta-Distributional Shifts","created":"2024-10-26","cited_by_count":0,"area":null},{"id":"2505.08698","title":"Continuous Temporal Learning of Probability Distributions via Neural ODEs with Applications in Continuous Glucose Monitoring Data","created":"2025-05-13","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":"2508.04036","title":"CORE-ReID V2: Advancing the Domain Adaptation for Object Re-Identification with Optimized Training and Ensemble Fusion","created":"2025-08-06","cited_by_count":0,"area":null},{"id":"2606.16941","title":"A nonparametric two-sample test using a parametric integral probability metric","created":"2026-06-15","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}