Runxue Bao   Github   Google Scholar   LinkedIn Email: baorunxue[at]gmail[dot]com

I am an AI Scientist at GE Healthcare. I obtained my Ph.D. degree in Computer Engineering from the University of Pittsburgh. Prior to this, I received my Honours B.S. degree in Electrical Engineering from the University of Science and Technology of China.


Feel free to get in touch if you would like to collaborate with me!

Research Interests

Large language models, Generative AI, deep learning, large-scale optimization, recommender systems, transfer learning, and self-supervised learning.

News

• [03/24] One paper on LLM Pruning was accepted by NAACL 2024!
• [03/24] One paper on Online Transfer Learning was accepted by IEEE ICHI 2024!
• [02/24] One paper on Model Compression was accepted by CVPR 2024!
• [02/24] One paper on Enhancing LLMs with Knowledge Graphs was on arXiv!
• [01/24] Two papers on Transfer Learning and Multimodal Learning were accepted by ISBI 2024!
• [10/23] One survey paper on Heterogeneous Transfer Learning was on arXiv!
• [08/23] One paper on Online Pairwise Learning was accepted by IEEE TNNLS 2023!
• [06/23] Serve as Program Committee (PC) for WSDM 2024!
• [04/23] Serve as Reviewer for NeurIPS 2023!
• [03/23] One paper on Multi-Source Transfer Learning was accepted by IEEE ICHI 2023!

Selected Publications

• InfuserKI: Enhancing Large Language Models with Knowledge Graphs via Infuser-Guided Knowledge Integration [Paper]
  Fali Wang, Runxue Bao, Suhang Wang, Wenchao Yu, Yanchi Liu, Wei Cheng, Haifeng Chen.
  arXiv 2024

• A Survey of Heterogeneous Transfer Learning [Paper]
  Runxue Bao*, Yiming Sun*, Yuhe Gao, Jindong Wang, Qiang Yang, Haifeng Chen, Zhi-Hong Mao, Ye Ye.
  arXiv 2023

• New Scalable and Efficient Online Pairwise Learning Algorithm
  Bin Gu, Runxue Bao, Chenkang Zhang, Heng Huang.
  IEEE Transactions on Neural Networks and Learning Systems (TNNLS 2023)

• Prediction of COVID-19 Patientsʼ Emergency Room Revisit using Multi-Source Transfer Learning [Paper]
  Yuelyu Ji*, Yuhe Gao*, Runxue Bao*, Qi Li, Disheng Liu, Yiming Sun, and Ye Ye.
  ICHI 2023 (* denotes equal contribution)

• An Accelerated Doubly Stochastic Gradient Method with Faster Explicit Model Identification [Paper] [Slides]
  Runxue Bao, Bin Gu, Heng Huang.
  CIKM 2022

• Doubly Sparse Asynchronous Learning for Stochastic Composite Optimization. [Paper] [Slides]
  Runxue Bao, Xidong Wu, Wenhan Xian, Heng Huang.
  IJCAI 2022

• Toward Unified Data and Algorithm Fairness via Adversarial Data Augmentation and Adaptive Model Fine-tuning. [Paper]
  Yanfu Zhang, Runxue Bao, Jian Pei, Heng Huang.
  ICDM 2022

• Fast OSCAR and OWL Regression via Safe Screening Rules. [Paper] [Code] [Slides]
  Runxue Bao, Bin Gu, Heng Huang.
  ICML 2020

• Efficient Approximate Solution Path Algorithm for Ordered Weighted L_1-Norm with Accuracy Guarantee. [Paper]
  Runxue Bao, Bin Gu, Heng Huang.
  ICDM 2019

Services

• Conference Program Committee or Reviewer :
  • 2024: ICML, ICLR, CVPR, ECCV, AAAI, IJCAI, WACV, WSDM (PC), SDM, IEEE CAI, AutoML
  • 2023: ICLR, ICML, NeurIPS, NeurIPS Datasets and Benchmarks Track, KDD (PC), IJCAI (PC), ECAI, WACV, AMIA, IEEE CEC, FUZZ-IEEE, AutoML, LOG
  • 2022: ICML, NeurIPS, NeurIPS Datasets and Benchmarks Track, ICLR, AISTATS, IEEE WCCI, LOG
  • 2021: ICML, NeurIPS
  • 2020: KDD (PC), MLSP
  • 2019: MLSP
• Journal Reviewer:
• Artificial Intelligence in Medicine
• IEEE Transactions on Big Data
• Patterns
• Knowledge and Information Systems (KAIS)
• Journal of Combinatorial Optimization
• IEEE Open Journal of the Computer Society (OJCS)
• Machine Learning Research (MLR)