:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Cao, Zhiyuan, Ma, Zeyu, Yang, Chenhao, Zheng, Han, Chen, Mingang
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2601.22752
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Breaking the Trilemma of Privacy, Utility, Efficiency via Controllable Machine Unlearning
by: Liu, Zheyuan, et al.
Published: (2023)

Breaking the MoE LLM Trilemma: Dynamic Expert Clustering with Structured Compression
by: Zhu, Peijun, et al.
Published: (2025)

WordGame: Efficient & Effective LLM Jailbreak via Simultaneous Obfuscation in Query and Response
by: Zhang, Tianrong, et al.
Published: (2024)

Obfuscated Activations Bypass LLM Latent-Space Defenses
by: Bailey, Luke, et al.
Published: (2024)

A Bias-Accuracy-Privacy Trilemma for Statistical Estimation
by: Kamath, Gautam, et al.
Published: (2023)

Breaking Obfuscation: Cluster-Aware Graph with LLM-Aided Recovery for Malicious JavaScript Detection
by: Liang, Zhihong, et al.
Published: (2025)

Machine Unlearning via Null Space Calibration
by: Chen, Huiqiang, et al.
Published: (2024)

A Theoretical Analysis of Efficiency Constrained Utility-Privacy Bi-Objective Optimization in Federated Learning
by: Gu, Hanlin, et al.
Published: (2023)

NPAT Null-Space Projected Adversarial Training Towards Zero Deterioration
by: Hu, Hanyi, et al.
Published: (2024)

Mitigating the Safety Alignment Tax with Null-Space Constrained Policy Optimization
by: Niu, Yifan, et al.
Published: (2025)

Semantic Scheduling for LLM Inference
by: Hua, Wenyue, et al.
Published: (2025)

Training-Free Multi-User Generative Semantic Communications via Null-Space Diffusion Sampling
by: Grassucci, Eleonora, et al.
Published: (2024)

PrivDiffuser: Privacy-Guided Diffusion Model for Data Obfuscation in Sensor Networks
by: Yang, Xin, et al.
Published: (2024)

Breaking Semantic-Aware Watermarks via LLM-Guided Coherence-Preserving Semantic Injection
by: Gao, Zheng, et al.
Published: (2026)

Breaking Model Lock-in: Cost-Efficient Zero-Shot LLM Routing via a Universal Latent Space
by: Yan, Cheng, et al.
Published: (2026)

Good-Enough LLM Obfuscation (GELO)
by: Belikov, Anatoly, et al.
Published: (2026)

SpecOffload: Unlocking Latent GPU Capacity for LLM Inference on Resource-Constrained Devices
by: Zhuge, Xiangwen, et al.
Published: (2025)

Architecture Shape Governs QNN Trainability: Jacobian Null Space Growth and Parameter Efficiency
by: Poppel, Michael, et al.
Published: (2026)

Preserving Privacy and Utility in LLM-Based Product Recommendations
by: Khezresmaeilzadeh, Tina, et al.
Published: (2025)

Privacy for Fairness: Information Obfuscation for Fair Representation Learning with Local Differential Privacy
by: Xie, Songjie, et al.
Published: (2024)

Misclassification Rate and Privacy-Utility Trade-offs in Graph Convolutional Networks via Subsampling Stability
by: Zhang, Yexin, et al.
Published: (2026)

Enhance Sample Efficiency and Robustness of End-to-end Urban Autonomous Driving via Semantic Masked World Model
by: Gao, Zeyu, et al.
Published: (2022)

CENTAUR: Bridging the Impossible Trinity of Privacy, Efficiency, and Performance in Privacy-Preserving Transformer Inference
by: Luo, Jinglong, et al.
Published: (2024)

Enhancing Trade-offs in Privacy, Utility, and Computational Efficiency through MUltistage Sampling Technique (MUST)
by: Zhao, Xingyuan, et al.
Published: (2023)

Self-Aware Safety Augmentation: Leveraging Internal Semantic Understanding to Enhance Safety in Vision-Language Models
by: Wang, Wanying, et al.
Published: (2025)

Defending Against Alignment-Breaking Attacks via Robustly Aligned LLM
by: Cao, Bochuan, et al.
Published: (2023)

Breaking the Quality-Privacy Tradeoff in Tabular Data Generation via In-Context Learning
by: Han, Xinyan, et al.
Published: (2026)

Uncovering Causal Drivers of Energy Efficiency for Industrial Process in Foundry via Time-Series Causal Inference
by: Ma, Zhipeng, et al.
Published: (2025)

Breaking Privacy in Federated Clustering: Perfect Input Reconstruction via Temporal Correlations
by: Yang, Guang, et al.
Published: (2025)

Null-Space Filtering for Data-Free Continual Model Merging: Preserving Stability, Promoting Plasticity
by: Qiu, Zihuan, et al.
Published: (2025)

Position: The Complexity of Perfect AI Alignment -- Formalizing the RLHF Trilemma
by: Sahoo, Subramanyam, et al.
Published: (2025)

Privacy Blur: Quantifying Privacy and Utility for Image Data Release
by: Mahloujifar, Saeed, et al.
Published: (2025)

RLHFSpec: Breaking the Efficiency Bottleneck in RLHF Training via Adaptive Drafting
by: Wang, Siqi, et al.
Published: (2025)

Revisiting Privacy, Utility, and Efficiency Trade-offs when Fine-Tuning Large Language Models
by: Das, Soumi, et al.
Published: (2025)

Preempting Text Sanitization Utility in Resource-Constrained Privacy-Preserving LLM Interactions
by: Carpentier, Robin, et al.
Published: (2024)

Teleportation With Null Space Gradient Projection for Optimization Acceleration
by: Wu, Zihao, et al.
Published: (2025)

Inference of Utilities and Time Preference in Sequential Decision-Making
by: Cao, Haoyang, et al.
Published: (2024)

Selective, Interpretable, and Motion Consistent Privacy Attribute Obfuscation for Action Recognition
by: Ilic, Filip, et al.
Published: (2024)

Boost Post-Training Quantization via Null Space Optimization for Large Language Models
by: Zhao, Jiaqi, et al.
Published: (2025)

Privacy-Utility-Bias Trade-offs for Privacy-Preserving Recommender Systems
by: Parsarad, Shiva, et al.
Published: (2025)