8 citations · 19 across the 15 of their papers we have counts for
16 papers
Safe Screening Rules for Group SLOPE
Runxue Bao, Quanchao Lu, Yanfu Zhang
Variable selection is a challenging problem in high-dimensional sparse learning, especially when group structures exist. Group SLOPE performs well for the adaptive selection of gro…
Any Large Language Model Can Be a Reliable Judge: Debiasing with a Reasoning-based Bias Detector
Haoyan Yang, Runxue Bao, Cao Xiao +4
LLM-as-a-Judge has emerged as a promising tool for automatically evaluating generated outputs, but its reliability is often undermined by potential biases in judgment. Existing eff…
Safe Screening Rules for Group OWL Models
Runxue Bao, Quanchao Lu, Yanfu Zhang
Group Ordered Weighted -Norm (Group OWL) regularized models have emerged as a useful procedure for high-dimensional sparse multi-task learning with correlated features. Prox…
All-in-One Tuning and Structural Pruning for Domain-Specific LLMs
Lei Lu, Zhepeng Wang, Runxue Bao +7
Existing pruning techniques for large language models (LLMs) targeting domain-specific applications typically follow a two-stage process: pruning the pretrained general-purpose LLM…
A Self-guided Multimodal Approach to Enhancing Graph Representation Learning for Alzheimer's Diseases
Zhepeng Wang, Runxue Bao, Yawen Wu +6
Graph neural networks (GNNs) are powerful machine learning models designed to handle irregularly structured data. However, their generic design often proves inadequate for analyzin…
Dynamic Uncertainty Ranking: Enhancing Retrieval-Augmented In-Context Learning for Long-Tail Knowledge in LLMs
Shuyang Yu, Runxue Bao, Parminder Bhatia +3
Large language models (LLMs) can learn vast amounts of knowledge from diverse domains during pre-training. However, long-tail knowledge from specialized domains is often scarce and…
Transfer Learning with Clinical Concept Embeddings from Large Language Models
Yuhe Gao, Runxue Bao, Yuelyu Ji +4
Knowledge sharing is crucial in healthcare, especially when leveraging data from multiple clinical sites to address data scarcity, reduce costs, and enable timely interventions. Tr…
Unlocking Memorization in Large Language Models with Dynamic Soft Prompting
Zhepeng Wang, Runxue Bao, Yawen Wu +6
Pretrained large language models (LLMs) have revolutionized natural language processing (NLP) tasks such as summarization, question answering, and translation. However, LLMs pose s…
Pruning as a Domain-specific LLM Extractor
Nan Zhang, Yanchi Liu, Xujiang Zhao +5
Large Language Models (LLMs) have exhibited remarkable proficiency across a wide array of NLP tasks. However, the escalation in model size also engenders substantial deployment cos…
Auto-Train-Once: Controller Network Guided Automatic Network Pruning from Scratch
Xidong Wu, Shangqian Gao, Zeyu Zhang +5
Current techniques for deep neural network (DNN) pruning often involve intricate multi-step processes that require domain-specific expertise, making their widespread adoption chall…
Online Transfer Learning for RSV Case Detection
Yiming Sun, Yuhe Gao, Runxue Bao +7
Transfer learning has become a pivotal technique in machine learning and has proven to be effective in various real-world applications. However, utilizing this technique for classi…
A Recent Survey of Heterogeneous Transfer Learning
Runxue Bao, Yiming Sun, Yuhe Gao +4
The application of transfer learning, leveraging knowledge from source domains to enhance model performance in a target domain, has significantly grown, supporting diverse real-wor…
Prediction of COVID-19 Patients' Emergency Room Revisit using Multi-Source Transfer Learning
Yuelyu Ji, Yuhe Gao, Runxue Bao +4
The coronavirus disease 2019 (COVID-19) has led to a global pandemic of significant severity. In addition to its high level of contagiousness, COVID-19 can have a heterogeneous cli…
An Accelerated Doubly Stochastic Gradient Method with Faster Explicit Model Identification
Runxue Bao, Bin Gu, Heng Huang
Sparsity regularized loss minimization problems play an important role in various fields including machine learning, data mining, and modern statistics. Proximal gradient descent m…
Sampling Through the Lens of Sequential Decision Making
Jason Xiaotian Dou, Alvin Qingkai Pan, Runxue Bao +3
Sampling is ubiquitous in machine learning methodologies. Due to the growth of large datasets and model complexity, we want to learn and adapt the sampling process while training a…
Fast OSCAR and OWL Regression via Safe Screening Rules
Runxue Bao, Bin Gu, Heng Huang
Ordered Weighted (OWL) regularized regression is a new regression analysis for high-dimensional sparse learning. Proximal gradient methods are used as standard approaches t…