:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Zhu, Zehan, Zhao, Heng, Huang, Yan, Zhou, Joey Tianyi, Ji, Shouling, Xu, Jinming
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	https://arxiv.org/abs/2512.13583
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee
by: Zhu, Zehan, et al.
Published: (2025)

PrivSGP-VR: Differentially Private Variance-Reduced Stochastic Gradient Push with Tight Utility Bounds
by: Zhu, Zehan, et al.
Published: (2024)

DP-FedPGN: Finding Global Flat Minima for Differentially Private Federated Learning via Penalizing Gradient Norm
by: Liu, Junkang, et al.
Published: (2025)

To Compress or Not? Pushing the Frontier of Lossless GenAI Model Weights Compression with Exponent Concentration
by: Yang, Zeyu, et al.
Published: (2025)

DP-OPD: Differentially Private On-Policy Distillation for Language Models
by: Khadem, Fatemeh, et al.
Published: (2026)

Differentially Private Integrated Decision Gradients (IDG-DP) for Radar-based Human Activity Recognition
by: Zakariyya, Idris, et al.
Published: (2024)

DP-TabICL: In-Context Learning with Differentially Private Tabular Data
by: Carey, Alycia N., et al.
Published: (2024)

DP-FedAdamW: An Efficient Optimizer for Differentially Private Federated Large Models
by: Liu, Jin, et al.
Published: (2026)

DP-Fusion: Token-Level Differentially Private Inference for Large Language Models
by: Thareja, Rushil, et al.
Published: (2025)

Too Good to be True? Turn Any Model Differentially Private With DP-Weights
by: Zagardo, David
Published: (2024)

DP-DCAN: Differentially Private Deep Contrastive Autoencoder Network for Single-cell Clustering
by: Li, Huifa, et al.
Published: (2023)

Enhancing DP-SGD through Non-monotonous Adaptive Scaling Gradient Weight
by: Huang, Tao, et al.
Published: (2024)

Differentially Private Multimodal Laplacian Dropout (DP-MLD) for EEG Representative Learning
by: Fu, Xiaowen, et al.
Published: (2024)

Certification for Differentially Private Prediction in Gradient-Based Training
by: Wicker, Matthew, et al.
Published: (2024)

Cognitive Inception: Agentic Reasoning against Visual Deceptions by Injecting Skepticism
by: Zhao, Yinjie, et al.
Published: (2025)

Learning with Differentially Private (Sliced) Wasserstein Gradients
by: Rodríguez-Vítores, David, et al.
Published: (2025)

DP-LAC: Lightweight Adaptive Clipping for Differentially Private Federated Fine-tuning of Language Models
by: Mehmood, Haaris, et al.
Published: (2026)

Agentic Spatio-Temporal Grounding via Collaborative Reasoning
by: Zhao, Heng, et al.
Published: (2026)

Differentially Private Block-wise Gradient Shuffle for Deep Learning
by: Zagardo, David
Published: (2024)

On the Sample Complexity of Differentially Private Policy Optimization
by: He, Yi, et al.
Published: (2025)

QMGeo: Differentially Private Federated Learning via Stochastic Quantization with Mixed Truncated Geometric Distribution
by: Wang, Zixi, et al.
Published: (2023)

Towards Differentially Private Reinforcement Learning with General Function Approximation
by: He, Yi, et al.
Published: (2026)

DR-Encoder: Encode Low-rank Gradients with Random Prior for Large Language Models Differentially Privately
by: Wu, Huiwen, et al.
Published: (2024)

Gradients Look Alike: Sensitivity is Often Overestimated in DP-SGD
by: Thudi, Anvith, et al.
Published: (2023)

Differentially Private Two-Stage Gradient Descent for Instrumental Variable Regression
by: Liang, Haodong, et al.
Published: (2025)

Weighted Stochastic Differential Equation to Implement Wasserstein-Fisher-Rao Gradient Flow
by: Rahimi, Herlock
Published: (2025)

Defending against Adversarial Malware Attacks on ML-based Android Malware Detection Systems
by: He, Ping, et al.
Published: (2025)

Differentially Private Worst-group Risk Minimization
by: Zhou, Xinyu, et al.
Published: (2024)

Tensor-Compressed and Fully-Quantized Training of Neural PDE Solvers
by: Lu, Jinming, et al.
Published: (2025)

Learning to Diagnose Privately: DP-Powered LLMs for Radiology Report Classification
by: Bhattacharjee, Payel, et al.
Published: (2025)

Gaussian DP for Reporting Differential Privacy Guarantees in Machine Learning
by: Gomez, Juan Felipe, et al.
Published: (2025)

Towards Faster Decentralized Stochastic Optimization with Communication Compression
by: Islamov, Rustem, et al.
Published: (2024)

Selective Learning: Towards Robust Calibration with Dynamic Regularization
by: Han, Zongbo, et al.
Published: (2024)

Differentially Private and Communication Efficient Large Language Model Split Inference via Stochastic Quantization and Soft Prompt
by: Gu, Yujie, et al.
Published: (2026)

TS-DP: Reinforcement Speculative Decoding For Temporal Adaptive Diffusion Policy Acceleration
by: Li, Ye, et al.
Published: (2025)

Efficient Utility-Preserving Machine Unlearning with Implicit Gradient Surgery
by: Zhou, Shiji, et al.
Published: (2025)

Unifying Bayesian Flow Networks and Diffusion Models through Stochastic Differential Equations
by: Xue, Kaiwen, et al.
Published: (2024)

DC-SGD: Differentially Private SGD with Dynamic Clipping through Gradient Norm Distribution Estimation
by: Wei, Chengkun, et al.
Published: (2025)

Differentially Private Kernel Density Estimation
by: Liu, Erzhi, et al.
Published: (2024)

The Eminence in Shadow: Exploiting Feature Boundary Ambiguity for Robust Backdoor Attacks
by: Feng, Zhou, et al.
Published: (2025)