102 citations · 547 across the 60 of their papers we have counts for
15 papers · 1 filter
Differentially Private Low-Rank Adaptation of Large Language Model Using Federated Learning
Xiao-Yang Liu, Rongyi Zhu, Daochen Zha +4
The surge in interest and application of large language models (LLMs) has sparked a drive to fine-tune these models to suit specific applications, such as finance and medical scien…
KnowGPT: Knowledge Graph based Prompting for Large Language Models
Qinggang Zhang, Junnan Dong, Hao Chen +3
Large Language Models (LLMs) have demonstrated remarkable capabilities in many real-world applications. Nonetheless, LLMs are often criticized for their tendency to produce halluci…
Enhanced Generalization through Prioritization and Diversity in Self-Imitation Reinforcement Learning over Procedural Environments with Sparse Rewards
Alain Andres, Daochen Zha, Javier Del Ser
Exploration poses a fundamental challenge in Reinforcement Learning (RL) with sparse rewards, limiting an agent's ability to learn optimal decision-making due to a lack of informat…
DiscoverPath: A Knowledge Refinement and Retrieval System for Interdisciplinarity on Biomedical Research
Yu-Neng Chuang, Guanchu Wang, Chia-Yuan Chang +8
The exponential growth in scholarly publications necessitates advanced tools for efficient article retrieval, especially in interdisciplinary fields where diverse terminologies are…
Tackling Diverse Minorities in Imbalanced Classification
Kwei-Herng Lai, Daochen Zha, Huiyuan Chen +5
Imbalanced datasets are commonly observed in various real-world applications, presenting significant challenges in training classifiers. When working with large datasets, the imbal…
FinGPT: Democratizing Internet-scale Data for Financial Large Language Models
Xiao-Yang Liu, Guoxuan Wang, Hongyang Yang +1
Large language models (LLMs) have demonstrated remarkable proficiency in understanding and generating human-like texts, which may potentially revolutionize the finance industry. Ho…
OpenGSL: A Comprehensive Benchmark for Graph Structure Learning
Zhiyao Zhou, Sheng Zhou, Bochao Mao +7
Graph Neural Networks (GNNs) have emerged as the de facto standard for representation learning on graphs, owing to their ability to effectively integrate graph topology and node at…
Pre-train and Search: Efficient Embedding Table Sharding with Pre-trained Neural Cost Models
Daochen Zha, Louis Feng, Liang Luo +8
Sharding a large machine learning model across multiple devices to balance the costs is important in distributed training. This is challenging because partitioning is NP-hard, and…
Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language Model
Zirui Liu, Guanchu Wang, Shaochen Zhong +8
With the rapid growth in model size, fine-tuning the large pre-trained language model has become increasingly difficult due to its extensive memory usage. Previous works usually fo…
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…
Interactive System-wise Anomaly Detection
Guanchu Wang, Ninghao Liu, Daochen Zha +1
Anomaly detection, where data instances are discovered containing feature patterns different from the majority, plays a fundamental role in various applications. However, it is cha…
Data-centric Artificial Intelligence: A Survey
Daochen Zha, Zaid Pervaiz Bhat, Kwei-Herng Lai +4
Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler of its great success is the availability of abundant and high-quality data for buil…
Towards Personalized Preprocessing Pipeline Search
Diego Martinez, Daochen Zha, Qiaoyu Tan +1
Feature preprocessing, which transforms raw input features into numerical representations, is a crucial step in automated machine learning (AutoML) systems. However, the existing s…
Fairly Predicting Graft Failure in Liver Transplant for Organ Assigning
Sirui Ding, Ruixiang Tang, Daochen Zha +4
Liver transplant is an essential therapy performed for severe liver diseases. The fact of scarce liver resources makes the organ assigning crucial. Model for End-stage Liver Diseas…
Data-centric AI: Perspectives and Challenges
Daochen Zha, Zaid Pervaiz Bhat, Kwei-Herng Lai +2
The role of data in building AI systems has recently been significantly magnified by the emerging concept of data-centric AI (DCAI), which advocates a fundamental shift from model…