Han Yu

Federated Nonlinear Causal Discovery via Divide-and-Conquer Learning

Federated causal discovery aims to learn causal structures from distributed data without sharing raw samples. Existing federated nonlinear methods adopt a monolithic global strategy that optimizes the entire graph simultaneously, suffering from catastrophic error propagation: a single misidentified edge cascades throug...

When Gradient Boosting Meets Adapter: Exploring Weak Learners for Parameter-Efficient Fine-tuning of LLMs

This work proposes eXtreme Gradient Boosting LoRA (XGBLoRA), a novel framework grounded in gradient boosting theory that provides theoretical analysis establishing convergence guarantees and expressiveness bounds, which formally justify why weaker (lower-rank) adapters, when properly combined, can match or exceed the p...

Fisher-Based Layer-Wise Adaptive Sparsification for Efficient Pruning of Large Language Models

When Gradient Boosting Meets Adapter: Exploring Weak Learners for Parameter-Efficient Fine-tuning of LLMs

Fine-tuning Large Language Models (LLMs) has become a crucial technique for adapting pre-trained models to downstream tasks. However, the enormous size of LLMs poses significant challenges in terms of computational complexity and resource requirements. Low-Rank Adaptation (LoRA) has emerged as a promising solution, yet...

Federated Nonlinear Causal Discovery via Divide-and-Conquer Learning

DC-FNCD (Divide-and-Conquer based Federated Nonlinear Causal Discovery) is proposed, which decomposes the global problem into independent per-variable neighborhood learning tasks, enabling lossless federated aggregation without raw data exchange.