56 citations · 57 across the 4 of their papers we have counts for
4 papers
FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets
Dannong Wang, Jaisal Patel, Daochen Zha +2
Low-rank adaptation (LoRA) methods show great potential for scaling pre-trained general-purpose Large Language Models (LLMs) to hundreds or thousands of use scenarios. However, the…
Dynamic Datasets and Market Environments for Financial Reinforcement Learning
Xiao-Yang Liu, Ziyi Xia, Hongyang Yang +6
The financial market is a particularly challenging playground for deep reinforcement learning due to its unique feature of dynamic datasets. Building high-quality market environmen…
UFNRec: Utilizing False Negative Samples for Sequential Recommendation
Xiaoyang Liu, Chong Liu, Pinzheng Wang +5
Sequential recommendation models are primarily optimized to distinguish positive samples from negative ones during training in which negative sampling serves as an essential compon…
Low-tubal-rank Tensor Completion using Alternating Minimization
Xiao-Yang Liu, Shuchin Aeron, Vaneet Aggarwal +1
The low-tubal-rank tensor model has been recently proposed for real-world multidimensional data. In this paper, we study the low-tubal-rank tensor completion problem, i.e., to reco…