{"total":15,"rows":[{"id":"2202.06382","title":"Training with More Confidence: Mitigating Injected and Natural Backdoors During Training","created":"2022-02-13","cited_by_count":38,"area":null},{"id":"2201.02265","title":"Learning to be adversarially robust and differentially private","created":"2022-01-06","cited_by_count":6,"area":null},{"id":"2211.08942","title":"Differentially Private Optimizers Can Learn Adversarially Robust Models","created":"2022-11-16","cited_by_count":6,"area":null},{"id":"2306.08656","title":"Augment then Smooth: Reconciling Differential Privacy with Certified Robustness","created":"2023-06-14","cited_by_count":6,"area":null},{"id":"2312.04542","title":"SoK: Unintended Interactions among Machine Learning Defenses and Risks","created":"2023-12-07","cited_by_count":6,"area":null},{"id":"2503.07501","title":"Trustworthy Machine Learning via Memorization and the Granular Long-Tail: A Survey on Interactions, Tradeoffs, and Beyond","created":"2025-03-10","cited_by_count":6,"area":null},{"id":"2504.07170","title":"Trustworthy AI Must Account for Interactions","created":"2025-04-09","cited_by_count":5,"area":null},{"id":"2601.10237","title":"Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD","created":"2026-01-15","cited_by_count":4,"area":null},{"id":"2412.05767","title":"DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization","created":"2024-12-08","cited_by_count":1,"area":null},{"id":"2412.20798","title":"Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry","created":"2024-12-30","cited_by_count":1,"area":null},{"id":"2605.06505","title":"PACZero: PAC-Private Fine-Tuning of Language Models via Sign Quantization","created":"2026-05-07","cited_by_count":1,"area":null},{"id":"2401.10405","title":"Differentially Private and Adversarially Robust Machine Learning: An Empirical Evaluation","created":"2024-01-18","cited_by_count":0,"area":null},{"id":"2402.17018","title":"A Curious Case of Remarkable Resilience to Gradient Attacks via Fully Convolutional and Differentiable Front End with a Skip Connection","created":"2024-02-26","cited_by_count":0,"area":null},{"id":"2511.21799","title":"The Double-Edged Nature of the Rashomon Set for Trustworthy Machine Learning","created":"2025-11-26","cited_by_count":0,"area":null},{"id":"2607.12354","title":"Reducing information dependency does not cause training data privacy. Adversarially non-robust features do","created":"2026-07-14","cited_by_count":0,"area":null}],"next_cursor":null}