1 citations · 1 across the 8 of their papers we have counts for
9 papers
Cascade: Hierarchical Recoverability Control for Large Language Model Unlearning
Qingchen Yu, Shiying Duan, Xiaodong Li +5
Large Language Model (LLM) unlearning is essential for removing sensitive or copyrighted knowledge while preserving general utility. Existing methods often leave residual knowledge…
ATLAS: Scaffold-Free Algorithm Synthesis by LLMs via Embedding-Guided Quality-Diversity Search
Danial Yazdani, Mohammad Nabi Omidvar, Yuan Sun +2
Most LLM-based automated algorithm design methods optimize a designated component within a human-specified scaffold, fixing overall organization and component interactions. We pres…
Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach
Yuhua Wang, Xiaodong Li, Yihao Guo +6
Federated prompt tuning (FPT) enables collaborative adaptation of vision--language models (VLMs) using lightweight prompts. Existing methods often address heterogeneity and privacy…
Taming Noise-Induced Prototype Degradation for Privacy-Preserving Personalized Federated Fine-Tuning
Yuhua Wang, Qinnan Zhang, Xiaodong Li +6
Prototype-based Personalized Federated Learning (ProtoPFL) enables efficient multi-domain adaptation by communicating compact class prototypes, but directly sharing them poses priv…
PORTAL: Controllable Landscape Generator for Continuous Optimization-Part I: Framework
Danial Yazdani, Mai Peng, Delaram Yazdani +6
Benchmarking is central to optimization research, yet existing test suites for continuous optimization remain limited: classical collections are fixed and rigid, while previous gen…
On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and Fairness
Shengkun Zhu, Jinshan Zeng, Sheng Wang +4
Statistical heterogeneity is a root cause of tension among accuracy, fairness, and robustness of federated learning (FL), and is key in paving a path forward. Personalized FL (PFL)…
Machine Learning-Enhanced Ant Colony Optimization for Column Generation
Hongjie Xu, Yunzhuang Shen, Yuan Sun +1
Column generation (CG) is a powerful technique for solving optimization problems that involve a large number of variables or columns. This technique begins by solving a smaller pro…
Efficient k-means with Individual Fairness via Exponential Tilting
Shengkun Zhu, Jinshan Zeng, Yuan Sun +3
In location-based resource allocation scenarios, the distances between each individual and the facility are desired to be approximately equal, thereby ensuring fairness. Individual…
Adaptive Stabilization Based on Machine Learning for Column Generation
Yunzhuang Shen, Yuan Sun, Xiaodong Li +3
Column generation (CG) is a well-established method for solving large-scale linear programs. It involves iteratively optimizing a subproblem containing a subset of columns and usin…