3 accepted papers
Low-rank gradient projection (LoRP) has recently emerged as a memory-efficient alternative to low-rank adapters (LoRA) for finetuning large language models. Existing LoRP methods, however, implicitly fix the projection unit to a single gradient row, leaving the effect of grouping multiple rows (or s
Bi-level optimizaiton (BO) has become a fundamental mathematical framework for addressing hierarchical machine learning problems. As deep learning models continue to grow in size, the demand for scalable bi-level optimization has become increasingly critical. Traditional gradient-based bi-level opti…
Learning an optimal policy from offline data is notoriously challenging, which requires the evaluation of the learning policy using data pre-collected from a static logging policy. We study the policy optimization problem in offline contextual bandits using policy gradient methods. We employ a distr…