Cover Image

Privately Learning Decision Lists and a Differentially Private Winnow

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Bun, Mark, Fang, William
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Cryptography and Security
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Differentially Private Release and Learning of Threshold Functions
by: Bun, Mark, et al.
Published: (2015)

Oracle-Efficient Differentially Private Learning with Public Data
by: Block, Adam, et al.
Published: (2024)

Private PAC Learning May be Harder than Online Learning
by: Bun, Mark, et al.
Published: (2024)

S-BDT: Distributed Differentially Private Boosted Decision Trees
by: Peinemann, Thorsten, et al.
Published: (2023)

Not All Learnable Distribution Classes are Privately Learnable
by: Bun, Mark, et al.
Published: (2024)

Private and Communication-Efficient Federated Learning based on Differentially Private Sketches
by: Zhang, Meifan, et al.
Published: (2024)

Smooth Sensitivity for Learning Differentially-Private yet Accurate Rule Lists
by: Ly, Timothée, et al.
Published: (2024)

Differentially Private Learned Indexes
by: Du, Jianzhang, et al.
Published: (2024)

Correlated Noise Mechanisms for Differentially Private Learning
by: Pillutla, Krishna, et al.
Published: (2025)

Differentially Private Decentralized Learning with Random Walks
by: Cyffers, Edwige, et al.
Published: (2024)

Delving into Differentially Private Transformer
by: Ding, Youlong, et al.
Published: (2024)

Differentially Private Attention Computation
by: Gao, Yeqi, et al.
Published: (2023)

Differentially Private Diffusion Models
by: Dockhorn, Tim, et al.
Published: (2022)

Differentially Private Federated Learning: A Systematic Review
by: Fu, Jie, et al.
Published: (2024)

Differentially Private Conditional Independence Testing
by: Kalemaj, Iden, et al.
Published: (2023)

Differentially Private Random Feature Model
by: Liao, Chunyang, et al.
Published: (2024)

Noise-Aware Differentially Private Regression via Meta-Learning
by: Räisä, Ossi, et al.
Published: (2024)

Bayesian Pseudo Posterior Mechanism for Differentially Private Machine Learning
by: Chew, Robert, et al.
Published: (2025)

Differentially Private Relational Learning with Entity-level Privacy Guarantees
by: Huang, Yinan, et al.
Published: (2025)

DPDR: Gradient Decomposition and Reconstruction for Differentially Private Deep Learning
by: Liu, Yixuan, et al.
Published: (2024)

Calibrating Practical Privacy Risks for Differentially Private Machine Learning
by: Gu, Yuechun, et al.
Published: (2024)

Differentially Private E-Values
by: Csillag, Daniel, et al.
Published: (2025)

Differentially Private Log-Location-Scale Regression Using Functional Mechanism
by: Sheng, Jiewen, et al.
Published: (2024)

Differentially Private Domain Adaptation with Theoretical Guarantees
by: Bassily, Raef, et al.
Published: (2023)

Practical Differentially Private Hyperparameter Tuning with Subsampling
by: Koskela, Antti, et al.
Published: (2023)

Scaling Laws for Differentially Private Language Models
by: McKenna, Ryan, et al.
Published: (2025)

Differentially Private Learners for Heterogeneous Treatment Effects
by: Schröder, Maresa, et al.
Published: (2025)

Differentially Private Training of Mixture of Experts Models
by: Tholoniat, Pierre, et al.
Published: (2024)

Revisiting Differentially Private Hyper-parameter Tuning
by: Xiang, Zihang, et al.
Published: (2024)

Noise-Aware Differentially Private Variational Inference
by: Alrawajfeh, Talal, et al.
Published: (2024)

Towards Reliable and Generalizable Differentially Private Machine Learning (Extended Version)
by: Bao, Wenxuan, et al.
Published: (2025)

Nearly Tight Black-Box Auditing of Differentially Private Machine Learning
by: Annamalai, Meenatchi Sundaram Muthu Selva, et al.
Published: (2024)

Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation
by: Tang, Xinyu, et al.
Published: (2023)

Differentially Private Active Learning: Balancing Effective Data Selection and Privacy
by: Schwethelm, Kristian, et al.
Published: (2024)

Differentially Private Federated Learning without Noise Addition: When is it Possible?
by: Zhang, Jiang, et al.
Published: (2024)

PrivORL: Differentially Private Synthetic Dataset for Offline Reinforcement Learning
by: Gong, Chen, et al.
Published: (2025)

ALI-DPFL: Differentially Private Federated Learning with Adaptive Local Iterations
by: Ling, Xinpeng, et al.
Published: (2023)

Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining
by: Tramèr, Florian, et al.
Published: (2022)

Graph Reconstruction from Differentially Private GNN Explanations
by: Sahoo, Rishi Raj, et al.
Published: (2026)

Efficient Differentially Private Fine-Tuning of Diffusion Models
by: Liu, Jing, et al.
Published: (2024)