PhD student, Texas A&M University - College Station
1 paper at NeurIPS 2025
We propose Contextual Low-Rank Adaptation (C-LoRA), an uncertainty-aware, parameter-efficient fine-tuning approach by developing lightweight, data-dependent LoRA modules that dynamically adapt uncertainties for robust and calibrated predictions.
