6 citations · 6 across the 5 of their papers we have counts for
7 papers
DPOison-VL: Clean Preference Poisoning in Vision-Language Direct Preference Optimization
Chengtao Jian, Kai Yang
Direct Preference Optimization (DPO) has become a widely adopted approach for aligning vision-language models (VLMs), yet the security of multimodal preference data remains largely…
UrbanMind: Towards Urban General Intelligence via Tool-Enhanced Retrieval-Augmented Generation and Multilevel Optimization
Kai Yang, Zelin Zhu, Chengtao Jian +4
Urban general intelligence (UGI) refers to the capacity of AI systems to autonomously perceive, reason, and act within dynamic and complex urban environments. In this paper, we int…
Stable Preference Optimization: A Bilevel Approach to Catastrophic Preference Shift
Chengtao Jian, Kai Yang, Tianhao Gao +5
Direct Preference Learning has emerged as a dominant offline paradigm for preference optimization. Most of these methods are based on the Bradley-Terry (BT) model for pairwise pref…
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…
Asynchronous Distributed Bilevel Optimization
Yang Jiao, Kai Yang, Tiancheng Wu +2
Bilevel optimization plays an essential role in many machine learning tasks, ranging from hyperparameter optimization to meta-learning. Existing studies on bilevel optimization, ho…