On Optimal Hyperparameters for Differentially Private Deep Transfer Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Rehn, Aki, Zhao, Linzh, Heikkilä, Mikko A., Honkela, Antti |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Mitigating Disparate Impact of Differentially Private Learning through Bounded Adaptive Clipping
by: Zhao, Linzh, et al.
Published: (2025)
by: Zhao, Linzh, et al.
Published: (2025)
An Interactive Framework for Finding the Optimal Trade-off in Differential Privacy
by: Yang, Yaohong, et al.
Published: (2025)
by: Yang, Yaohong, et al.
Published: (2025)
Privacy Leakage via Output Label Space and Differentially Private Continual Learning
by: Tobaben, Marlon, et al.
Published: (2024)
by: Tobaben, Marlon, et al.
Published: (2024)
Beyond Square Roots: Explicit Memory-Efficient Factorization for Multi-Epoch Private Learning
by: Kalinin, Nikita P., et al.
Published: (2026)
by: Kalinin, Nikita P., et al.
Published: (2026)
On Using Secure Aggregation in Differentially Private Federated Learning with Multiple Local Steps
by: Heikkilä, Mikko A.
Published: (2024)
by: Heikkilä, Mikko A.
Published: (2024)
Noise-Aware Differentially Private Variational Inference
by: Alrawajfeh, Talal, et al.
Published: (2024)
by: Alrawajfeh, Talal, et al.
Published: (2024)
Subsampling is not Magic: Why Large Batch Sizes Work for Differentially Private Stochastic Optimisation
by: Räisä, Ossi, et al.
Published: (2024)
by: Räisä, Ossi, et al.
Published: (2024)
Practical Differentially Private Hyperparameter Tuning with Subsampling
by: Koskela, Antti, et al.
Published: (2023)
by: Koskela, Antti, et al.
Published: (2023)
Efficient and Scalable Implementation of Differentially Private Deep Learning without Shortcuts
by: Beltran, Sebastian Rodriguez, et al.
Published: (2024)
by: Beltran, Sebastian Rodriguez, et al.
Published: (2024)
Hyperparameters in Score-Based Membership Inference Attacks
by: Pradhan, Gauri, et al.
Published: (2025)
by: Pradhan, Gauri, et al.
Published: (2025)
Empirical Comparison of Membership Inference Attacks in Deep Transfer Learning
by: Bai, Yuxuan, et al.
Published: (2025)
by: Bai, Yuxuan, et al.
Published: (2025)
Impact of Dataset Properties on Membership Inference Vulnerability of Deep Transfer Learning
by: Tobaben, Marlon, et al.
Published: (2024)
by: Tobaben, Marlon, et al.
Published: (2024)
A Bias-Variance Decomposition for Ensembles over Multiple Synthetic Datasets
by: Räisä, Ossi, et al.
Published: (2024)
by: Räisä, Ossi, et al.
Published: (2024)
Noise-Aware Differentially Private Regression via Meta-Learning
by: Räisä, Ossi, et al.
Published: (2024)
by: Räisä, Ossi, et al.
Published: (2024)
Beyond Membership: Limitations of Add/Remove Adjacency in Differential Privacy
by: Pradhan, Gauri, et al.
Published: (2025)
by: Pradhan, Gauri, et al.
Published: (2025)
DP-HYPE: Distributed Differentially Private Hyperparameter Search
by: Liebenow, Johannes, et al.
Published: (2025)
by: Liebenow, Johannes, et al.
Published: (2025)
Differentially Private Hyperparameter Tuning using Local Bayesian Optimization
by: Sopa, Getoar, et al.
Published: (2025)
by: Sopa, Getoar, et al.
Published: (2025)
On Reliability of Efficient Membership Inference Vulnerability Evaluation
by: Jälkö, Joonas, et al.
Published: (2026)
by: Jälkö, Joonas, et al.
Published: (2026)
Differentially Private In-Context Learning with Nearest Neighbor Search
by: Koskela, Antti, et al.
Published: (2025)
by: Koskela, Antti, et al.
Published: (2025)
Gaussian DP for Reporting Differential Privacy Guarantees in Machine Learning
by: Gomez, Juan Felipe, et al.
Published: (2025)
by: Gomez, Juan Felipe, et al.
Published: (2025)
Does Differentially Private Synthetic Data Lead to Synthetic Discoveries?
by: Perez, Ileana Montoya, et al.
Published: (2024)
by: Perez, Ileana Montoya, et al.
Published: (2024)
Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners
by: Redberg, Rachel, et al.
Published: (2023)
by: Redberg, Rachel, et al.
Published: (2023)
Differentially Private Prototypes for Imbalanced Transfer Learning
by: Wahdany, Dariush, et al.
Published: (2024)
by: Wahdany, Dariush, et al.
Published: (2024)
Near-Optimal Algorithms for Differentially Private Online Learning in a Stochastic Environment
by: Hu, Bingshan, et al.
Published: (2021)
by: Hu, Bingshan, et al.
Published: (2021)
Hyperparameter Transfer for Dense Associative Memories
by: Holtzman, Roi, et al.
Published: (2026)
by: Holtzman, Roi, et al.
Published: (2026)
Hyperparameter Transfer with Mixture-of-Expert Layers
by: Jiang, Tianze, et al.
Published: (2026)
by: Jiang, Tianze, et al.
Published: (2026)
Nearly Optimal Differentially Private ReLU Regression
by: Ding, Meng, et al.
Published: (2025)
by: Ding, Meng, et al.
Published: (2025)
DPDR: Gradient Decomposition and Reconstruction for Differentially Private Deep Learning
by: Liu, Yixuan, et al.
Published: (2024)
by: Liu, Yixuan, et al.
Published: (2024)
Revisiting Hyperparameter Tuning with Differential Privacy
by: Ding, Youlong, et al.
Published: (2022)
by: Ding, Youlong, et al.
Published: (2022)
Understanding the Mechanisms of Fast Hyperparameter Transfer
by: Ghosh, Nikhil, et al.
Published: (2025)
by: Ghosh, Nikhil, et al.
Published: (2025)
Optimal Guarantees for Auditing Rényi Differentially Private Machine Learning
by: Kim, Benjamin D., et al.
Published: (2026)
by: Kim, Benjamin D., et al.
Published: (2026)
Locally Differentially Private Distributed Online Learning with Guaranteed Optimality
by: Chen, Ziqin, et al.
Published: (2023)
by: Chen, Ziqin, et al.
Published: (2023)
Differentially Private Deep Model-Based Reinforcement Learning
by: Rio, Alexandre, et al.
Published: (2024)
by: Rio, Alexandre, et al.
Published: (2024)
Accuracy-First Rényi Differential Privacy and Post-Processing Immunity
by: Räisä, Ossi, et al.
Published: (2025)
by: Räisä, Ossi, et al.
Published: (2025)
Generalizing Differentially Private Decentralized Deep Learning with Multi-Agent Consensus
by: Bayrooti, Jasmine, et al.
Published: (2023)
by: Bayrooti, Jasmine, et al.
Published: (2023)
Exactly Minimax-Optimal Locally Differentially Private Sampling
by: Park, Hyun-Young, et al.
Published: (2024)
by: Park, Hyun-Young, et al.
Published: (2024)
Differentially Private Kernelized Contextual Bandits
by: Pavlovic, Nikola, et al.
Published: (2025)
by: Pavlovic, Nikola, et al.
Published: (2025)
Bayesian Optimisation with Unknown Hyperparameters: Regret Bounds Logarithmically Closer to Optimal
by: Ziomek, Juliusz, et al.
Published: (2024)
by: Ziomek, Juliusz, et al.
Published: (2024)
Aggregating Data for Optimal and Private Learning
by: Agarwal, Sushant, et al.
Published: (2024)
by: Agarwal, Sushant, et al.
Published: (2024)
Differentially Private Block-wise Gradient Shuffle for Deep Learning
by: Zagardo, David
Published: (2024)
by: Zagardo, David
Published: (2024)
Similar Items
-
Mitigating Disparate Impact of Differentially Private Learning through Bounded Adaptive Clipping
by: Zhao, Linzh, et al.
Published: (2025) -
An Interactive Framework for Finding the Optimal Trade-off in Differential Privacy
by: Yang, Yaohong, et al.
Published: (2025) -
Privacy Leakage via Output Label Space and Differentially Private Continual Learning
by: Tobaben, Marlon, et al.
Published: (2024) -
Beyond Square Roots: Explicit Memory-Efficient Factorization for Multi-Epoch Private Learning
by: Kalinin, Nikita P., et al.
Published: (2026) -
On Using Secure Aggregation in Differentially Private Federated Learning with Multiple Local Steps
by: Heikkilä, Mikko A.
Published: (2024)