{"total":40,"rows":[{"id":"2110.05679","title":"Large Language Models Can Be Strong Differentially Private Learners","created":"2021-10-12","cited_by_count":412,"area":null},{"id":"2109.12298","title":"Opacus: User-Friendly Differential Privacy Library in PyTorch","created":"2021-09-25","cited_by_count":383,"area":null},{"id":"2303.00654","title":"How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy","created":"2023-03-01","cited_by_count":153,"area":null},{"id":"2102.06062","title":"Deep Learning with Label Differential Privacy","created":"2021-02-11","cited_by_count":117,"area":null},{"id":"2201.12328","title":"Toward Training at ImageNet Scale with Differential Privacy","created":"2022-01-28","cited_by_count":87,"area":null},{"id":"2210.00038","title":"Differentially Private Optimization on Large Model at Small Cost","created":"2022-09-30","cited_by_count":65,"area":null},{"id":"2212.01539","title":"Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping","created":"2022-12-03","cited_by_count":52,"area":null},{"id":"2205.10683","title":"Scalable and Efficient Training of Large Convolutional Neural Networks with Differential Privacy","created":"2022-05-21","cited_by_count":48,"area":null},{"id":"2210.00036","title":"Differentially Private Bias-Term Fine-tuning of Foundation Models","created":"2022-09-30","cited_by_count":48,"area":null},{"id":"2102.03013","title":"Fast and Memory Efficient Differentially Private-SGD via JL Projections","created":"2021-02-05","cited_by_count":29,"area":null},{"id":"2209.08425","title":"Introspective Learning : A Two-Stage Approach for Inference in Neural Networks","created":"2022-09-17","cited_by_count":20,"area":null},{"id":"2203.01097","title":"Model-agnostic out-of-distribution detection using combined statistical tests","created":"2022-03-02","cited_by_count":14,"area":null},{"id":"2211.13403","title":"Differentially Private Image Classification from Features","created":"2022-11-24","cited_by_count":7,"area":null},{"id":"2311.11822","title":"Zero redundancy distributed learning with differential privacy","created":"2023-11-20","cited_by_count":7,"area":null},{"id":"2411.00999","title":"Normalization Layer Per-Example Gradients are Sufficient to Predict Gradient Noise Scale in Transformers","created":"2024-11-01","cited_by_count":7,"area":null},{"id":"2107.08461","title":"Differentially Private Bayesian Neural Networks on Accuracy, Privacy and Reliability","created":"2021-07-18","cited_by_count":6,"area":null},{"id":"2211.11896","title":"Private Ad Modeling with DP-SGD","created":"2022-11-21","cited_by_count":6,"area":null},{"id":"2310.15454","title":"Private Learning with Public Features","created":"2023-10-24","cited_by_count":6,"area":null},{"id":"2405.18194","title":"Delving into Differentially Private Transformer","created":"2024-05-28","cited_by_count":5,"area":null},{"id":"2102.06735","title":"Learning Deep Neural Networks under Agnostic Corrupted Supervision","created":"2021-02-12","cited_by_count":3,"area":null},{"id":"2104.09493","title":"Bayesian Uncertainty and Expected Gradient Length -- Regression: Two Sides Of The Same Coin?","created":"2021-04-19","cited_by_count":3,"area":null},{"id":"2505.00358","title":"R&B: Domain Regrouping and Data Mixture Balancing for Efficient Foundation Model Training","created":"2025-05-01","cited_by_count":3,"area":null},{"id":"2205.09869","title":"Transformer with Memory Replay","created":"2022-05-19","cited_by_count":2,"area":null},{"id":"2305.17633","title":"DPFormer: Learning Differentially Private Transformer on Long-Tailed Data","created":"2023-05-28","cited_by_count":2,"area":null},{"id":"2406.11087","title":"DP-MemArc: Differential Privacy Transfer Learning for Memory Efficient Language Models","created":"2024-06-16","cited_by_count":2,"area":null},{"id":"2502.01701","title":"Learning with Differentially Private (Sliced) Wasserstein Gradients","created":"2025-02-03","cited_by_count":2,"area":null},{"id":"2510.00236","title":"Per-example gradients: a new frontier for understanding and improving optimizers","created":"2025-09-30","cited_by_count":2,"area":null},{"id":"2103.07491","title":"Private Cross-Silo Federated Learning for Extracting Vaccine Adverse Event Mentions","created":"2021-03-12","cited_by_count":1,"area":null},{"id":"2210.04840","title":"Rieoptax: Riemannian Optimization in JAX","created":"2022-10-10","cited_by_count":1,"area":null},{"id":"2410.06878","title":"Noise is All You Need: Private Second-Order Convergence of Noisy SGD","created":"2024-10-09","cited_by_count":1,"area":null},{"id":"2410.08299","title":"Privately Learning from Graphs with Applications in Fine-tuning Large Language Models","created":"2024-10-10","cited_by_count":1,"area":null},{"id":"2507.01154","title":"FlashDP: Private Training Large Language Models with Efficient DP-SGD","created":"2025-07-01","cited_by_count":1,"area":null},{"id":"2605.24879","title":"Efficient DP-SGD for LLMs with Randomized Clipping","created":"2026-05-24","cited_by_count":1,"area":null},{"id":"2605.31244","title":"Spectral Reach: Understanding Neural Scaling as Progress into the Spectral Tail","created":"2026-05-29","cited_by_count":1,"area":null},{"id":"2607.11898","title":"TAKE: Trajectory-Aware Knowledge Estimation for Text Dataset Distillation","created":"2026-06-13","cited_by_count":1,"area":null},{"id":"2112.00174","title":"Adaptive Optimization with Examplewise Gradients","created":"2021-11-30","cited_by_count":0,"area":null},{"id":"2310.03104","title":"Differentially Private Optimization for Non-Decomposable Objective Functions","created":"2023-10-04","cited_by_count":0,"area":null},{"id":"2508.15141","title":"Towards Reliable and Generalizable Differentially Private Machine Learning (Extended Version)","created":"2025-08-21","cited_by_count":0,"area":null},{"id":"2609.40335","title":"Is Weight Tying Still Beneficial for Decoder-Only LLMs in Private Settings Under DP-SGD?","created":"2026-09-30","cited_by_count":0,"area":null},{"id":"2610.00436","title":"Every Batch Is Its Own Validation Set: Leave-One-Out Gradient Matching for Online Data Selection in LLM Fine-Tuning","created":"2026-09-30","cited_by_count":0,"area":null}],"next_cursor":null}