5 papers
Quantization as a Malicious Task: Removing Quantization-Conditioned Backdoors via Task Arithmetic
Kaihsun Yang, Min-Yan Tsai, Chia-Mu Yu
Model quantization is widely adopted to reduce memory usage and inference cost when deploying deep neural networks on resource-constrained devices. However, recent studies have rev…
PR-Attack: Coordinated Prompt-RAG Attacks on Retrieval-Augmented Generation in Large Language Models via Bilevel Optimization
Yang Jiao, Xiaodong Wang, Kai Yang
Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of applications, e.g., medical question-answering, mathematical sciences, and code generat…
Unlocking TriLevel Learning with Level-Wise Zeroth Order Constraints: Distributed Algorithms and Provable Non-Asymptotic Convergence
Yang Jiao, Kai Yang, Chengtao Jian
Trilevel learning (TLL) found diverse applications in numerous machine learning applications, ranging from robust hyperparameter optimization to domain adaptation. However, existin…
Tri-Level Navigator: LLM-Empowered Tri-Level Learning for Time Series OOD Generalization
Chengtao Jian, Kai Yang, Yang Jiao
Out-of-Distribution (OOD) generalization in machine learning is a burgeoning area of study. Its primary goal is to enhance the adaptability and resilience of machine learning model…
Provably Convergent Federated Trilevel Learning
Yang Jiao, Kai Yang, Tiancheng Wu +2
Trilevel learning, also called trilevel optimization (TLO), has been recognized as a powerful modelling tool for hierarchical decision process and widely applied in many machine le…