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stat.ML2026
Statistical Inference for Rank Allocation in Low-Rank Adaptation
Yihang Gao, Vincent Y. F. Tan
Low-rank adaptation (LoRA) has become a widely used parameter-efficient fine-tuning method for large language models. Since different modules and layers may contribute unequally to…
cs.LG2026
FairGC: Fairness-aware Graph Condensation
Yihan Gao, Chenxi Huang, Wen Shi +5
Graph condensation (GC) has become a vital strategy for scaling Graph Neural Networks by compressing massive datasets into small, synthetic node sets. While current GC methods effe…
cs.LG2026
ODELoRA: Training Low-Rank Adaptation by Solving Ordinary Differential Equations
Yihang Gao, Vincent Y. F. Tan
Low-rank adaptation (LoRA) has emerged as a widely adopted parameter-efficient fine-tuning method in deep transfer learning, due to its reduced number of trainable parameters and l…