1 citations · 1 across the 14 of their papers we have counts for
Showing 2025Show all
3 papers · 1 filter
cs.LG2025
On the Convergence Rate of LoRA Gradient Descent
Siqiao Mu, Diego Klabjan
The low-rank adaptation (LoRA) algorithm for fine-tuning large models has grown popular in recent years due to its remarkable performance and low computational requirements. LoRA t…
cs.LG2025
Descend or Rewind? Stochastic Gradient Descent Unlearning
Siqiao Mu, Diego Klabjan
Machine unlearning algorithms aim to remove the impact of selected training data from a model without the computational expenses of retraining from scratch. Two such algorithms are…
cs.CL2025
A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models
Shuyang Wang, Somayeh Moazeni, Diego Klabjan
Designing effective prompts is essential to guiding large language models (LLMs) toward desired responses. Automated prompt engineering aims to reduce reliance on manual effort by…