4 citations · 5 across the 5 of their papers we have counts for
5 papers
Rethinking Parameter Sharing for LLM Fine-Tuning with Multiple LoRAs
Hao Ban, Kaiyi Ji
Large language models are often adapted using parameter-efficient techniques such as Low-Rank Adaptation (LoRA), formulated as , where is the pre-trained para…
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation
Hao Ban, Gokul Ram Subramani, Kaiyi Ji
Multi-task learning (MTL) enables a joint model to capture commonalities across multiple tasks, reducing computation costs and improving data efficiency. However, a major challenge…
Tuning-Free Bilevel Optimization: New Algorithms and Convergence Analysis
Yifan Yang, Hao Ban, Minhui Huang +2
Bilevel optimization has recently attracted considerable attention due to its abundant applications in machine learning problems. However, existing methods rely on prior knowledge…
Theoretical Study of Conflict-Avoidant Multi-Objective Reinforcement Learning
Yudan Wang, Peiyao Xiao, Hao Ban +2
Multi-task reinforcement learning (MTRL) has shown great promise in many real-world applications. Existing MTRL algorithms often aim to learn a policy that optimizes individual obj…
Fair Resource Allocation in Multi-Task Learning
Hao Ban, Kaiyi Ji
By jointly learning multiple tasks, multi-task learning (MTL) can leverage the shared knowledge across tasks, resulting in improved data efficiency and generalization performance.…