{"total":24,"rows":[{"id":"2206.13033","title":"Normalized/Clipped SGD with Perturbation for Differentially Private Non-Convex Optimization","created":"2022-06-27","cited_by_count":36,"area":null},{"id":"2312.14334","title":"DP-AdamBC: Your DP-Adam Is Actually DP-SGD (Unless You Apply Bias Correction)","created":"2023-12-21","cited_by_count":28,"area":null},{"id":"2305.13865","title":"Selective Pre-training for Private Fine-tuning","created":"2023-05-23","cited_by_count":23,"area":null},{"id":"2301.11989","title":"Practical Differentially Private Hyperparameter Tuning with Subsampling","created":"2023-01-27","cited_by_count":19,"area":null},{"id":"2302.02936","title":"Private GANs, Revisited","created":"2023-02-06","cited_by_count":17,"area":null},{"id":"2303.01256","title":"Choosing Public Datasets for Private Machine Learning via Gradient Subspace Distance","created":"2023-03-02","cited_by_count":16,"area":null},{"id":"2401.00583","title":"Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners","created":"2023-12-31","cited_by_count":12,"area":null},{"id":"2212.04486","title":"A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization","created":"2022-12-08","cited_by_count":11,"area":null},{"id":"2503.00703","title":"Towards hyperparameter-free optimization with differential privacy","created":"2025-03-02","cited_by_count":7,"area":null},{"id":"2211.01852","title":"Revisiting Hyperparameter Tuning with Differential Privacy","created":"2022-11-03","cited_by_count":6,"area":null},{"id":"2303.03451","title":"Improved Differentially Private Regression via Gradient Boosting","created":"2023-03-06","cited_by_count":6,"area":null},{"id":"2503.22988","title":"DC-SGD: Differentially Private SGD with Dynamic Clipping through Gradient Norm Distribution Estimation","created":"2025-03-29","cited_by_count":6,"area":null},{"id":"2402.13087","title":"Revisiting Differentially Private Hyper-parameter Tuning","created":"2024-02-20","cited_by_count":5,"area":null},{"id":"2405.04034","title":"Differentially Private Post-Processing for Fair Regression","created":"2024-05-07","cited_by_count":5,"area":null},{"id":"2405.18194","title":"Delving into Differentially Private Transformer","created":"2024-05-28","cited_by_count":5,"area":null},{"id":"2206.07125","title":"Self-Supervised Pretraining for Differentially Private Learning","created":"2022-06-14","cited_by_count":4,"area":null},{"id":"2306.05734","title":"DP-HyPO: An Adaptive Private Hyperparameter Optimization Framework","created":"2023-06-09","cited_by_count":4,"area":null},{"id":"2209.04445","title":"Privacy-Preserving Deep Learning Model for Covid-19 Disease Detection","created":"2022-09-07","cited_by_count":3,"area":null},{"id":"2210.15961","title":"DPVIm: Differentially Private Variational Inference Improved","created":"2022-10-28","cited_by_count":2,"area":null},{"id":"2310.00829","title":"Online Sensitivity Optimization in Differentially Private Learning","created":"2023-10-02","cited_by_count":2,"area":null},{"id":"2402.06701","title":"Privacy Profiles for Private Selection","created":"2024-02-09","cited_by_count":2,"area":null},{"id":"2312.16554","title":"A Theoretical Analysis of Efficiency Constrained Utility-Privacy Bi-Objective Optimization in Federated Learning","created":"2023-12-27","cited_by_count":1,"area":null},{"id":"2502.06044","title":"Differentially Private Hyperparameter Tuning using Local Bayesian Optimization","created":"2025-02-09","cited_by_count":1,"area":null},{"id":"2504.09850","title":"Accelerating Differentially Private Federated Learning via Adaptive Extrapolation","created":"2025-04-14","cited_by_count":1,"area":null}],"next_cursor":null}