Imagen de Portada

On the Optimal Regret of Locally Private Linear Contextual Bandit

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Li, Jiachun, Simchi-Levi, David, Wang, Yining
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning Cryptography and Security
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Beyond Covariance Matrix: The Statistical Complexity of Private Linear Regression
por: Chen, Fan, et al.
Publicado: (2025)

Privacy Preserving Adaptive Experiment Design
por: Li, Jiachun, et al.
Publicado: (2024)

Differentially Private Linear Bandits with Partial Distributed Feedback
por: Li, Fengjiao, et al.
Publicado: (2022)

Exactly Minimax-Optimal Locally Differentially Private Sampling
por: Park, Hyun-Young, et al.
Publicado: (2024)

Optimal Locally Private Nonparametric Classification with Public Data
por: Ma, Yuheng, et al.
Publicado: (2023)

Optimal Regret of Bernoulli Bandits under Global Differential Privacy
por: Azize, Achraf, et al.
Publicado: (2025)

Delving into Differentially Private Transformer
por: Ding, Youlong, et al.
Publicado: (2024)

Differential Privacy in Kernelized Contextual Bandits via Random Projections
por: Pavlovic, Nikola, et al.
Publicado: (2025)

Locally Differentially Private Distributed Online Learning with Guaranteed Optimality
por: Chen, Ziqin, et al.
Publicado: (2023)

Differentially Private Sparse Linear Regression with Heavy-tailed Responses
por: Tian, Xizhi, et al.
Publicado: (2025)

Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners
por: Redberg, Rachel, et al.
Publicado: (2023)

Locally Private Estimation with Public Features
por: Ma, Yuheng, et al.
Publicado: (2024)

Learning with Locally Private Examples by Inverse Weierstrass Private Stochastic Gradient Descent
por: Dufraiche, Jean, et al.
Publicado: (2026)

Private Linear Regression with Differential Privacy and PAC Privacy
por: Yang, Hillary, et al.
Publicado: (2024)

Rate-Optimal Rank Aggregation with Private Pairwise Rankings
por: Xu, Shirong, et al.
Publicado: (2024)

Nearly-Linear Time Private Hypothesis Selection with the Optimal Approximation Factor
por: Aliakbarpour, Maryam, et al.
Publicado: (2025)

Locally Differentially Private Embedding Models in Distributed Fraud Prevention Systems
por: Perez, Iker, et al.
Publicado: (2024)

Constructing Adversarial Examples for Vertical Federated Learning: Optimal Client Corruption through Multi-Armed Bandit
por: Yao, Duanyi, et al.
Publicado: (2024)

Devil's Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocols
por: He, Longzhu, et al.
Publicado: (2025)

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

Sample-Optimal Locally Private Hypothesis Selection and the Provable Benefits of Interactivity
por: Pour, Alireza F., et al.
Publicado: (2023)

Adversarial Attacks on Locally Private Graph Neural Networks
por: Varun, Matta, et al.
Publicado: (2026)

Enhancing Cybersecurity in IoT Networks: A Deep Learning Approach to Anomaly Detection
por: Pang, Yining, et al.
Publicado: (2024)

HoGS: Homophily-Oriented Graph Synthesis for Local Differentially Private GNN Training
por: Xu, Wen, et al.
Publicado: (2026)

Differentially Private High Dimensional Bandits
por: Shukla, Apurv
Publicado: (2024)

Less Random, More Private: What is the Optimal Subsampling Scheme for DP-SGD?
por: Dong, Andy, et al.
Publicado: (2026)

Data Poisoning Attacks to Locally Differentially Private Range Query Protocols
por: Liao, Ting-Wei, et al.
Publicado: (2025)

Stealthy Adversarial Attacks on Stochastic Multi-Armed Bandits
por: Wang, Zhiwei, et al.
Publicado: (2024)

Optimal Rates for Pure $\varepsilon$-Differentially Private Stochastic Convex Optimization with Heavy Tails
por: Lowy, Andrew
Publicado: (2026)

On Using Secure Aggregation in Differentially Private Federated Learning with Multiple Local Steps
por: Heikkilä, Mikko A.
Publicado: (2024)

Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation
por: Brown, Gavin, et al.
Publicado: (2024)

Bounding the Excess Risk for Linear Models Trained on Marginal-Preserving, Differentially-Private, Synthetic Data
por: Zhou, Yvonne, et al.
Publicado: (2024)

Optimal Differentially Private Model Training with Public Data
por: Lowy, Andrew, et al.
Publicado: (2023)

FedGIG: Graph Inversion from Gradient in Federated Learning
por: Xiao, Tianzhe, et al.
Publicado: (2024)

FlashDP: Private Training Large Language Models with Efficient DP-SGD
por: Wang, Liangyu, et al.
Publicado: (2025)

Back to Square Roots: An Optimal Bound on the Matrix Factorization Error for Multi-Epoch Differentially Private SGD
por: Kalinin, Nikita P., et al.
Publicado: (2025)

Optimal Fairness under Local Differential Privacy
por: Ghoukasian, Hrad, et al.
Publicado: (2025)

Privacy Profiles for Private Selection
por: Koskela, Antti, et al.
Publicado: (2024)

DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning Based on Constant-Overhead Linear Secret Resharing
por: Bienstock, Alexander, et al.
Publicado: (2024)

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