Shibo Li
栗识博
School of Computing
University of Utah
Email : shibo at cs.utah.edu
I am a fourth-year Ph.D. student in Computer Science at SoC and SCI, University of Utah. I received my M.S. degree at University of Pittsburgh and my B.E. degree at South China University of Technology(SCUT). I also spent time at Schlumberger-Doll Research as research intern and at Amazon as Applied Scientist Intern working on Privacy-preserving learning algorihtms and incontext few-shots learning with pretrained large language/multi-modality models.
My principal research interests lie in developing flexible and scalable algorithms that can learn from complex, structural, and high-dimensional data within the Bayesian framework. I am advised by Dr. Shandian Zhe and Dr. Mike Kirby. Currently, we are working on multi-fidelity, multi-task active learning/Bayesian optimization problems with deep models. Prior to joining UofU, I spent some time working on reinforcement learning problems that help automatic agents make rational decisions from temporal data under uncertainties.
Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks
Shibo Li*, Michael Penwarden*, Yiming Xu, Conor Tillinghast, Akil Narayan, Robert M. Kirby, Shandian Zhe
ICML 2023
arXiv | code(to appear)
Meta-Learning with Adjoint Methods
Shibo Li, Zheng Wang, Akil Narayan, Robert Kirby, Shandian Zhe
AISTATS 2023
Infinite-Fidelity Coregionalization for Physical Simulation
Shibo Li, Zheng Wang, Robert Kirby, Shandian Zhe
NeurIPS 2022
Batch Multi-Fidelity Active Learning with Budget Constraints
Shibo Li*, Jeff Phillips*, Xin Yu, Robert M. Kirby, and Shandian Zhe
NeurIPS 2022
Decomposing Temporal High-Order Interactions via Latent ODEs
Shibo Li, Mike Kirby, Shandian Zhe
ICML 2022
Nonparametric Embeddings of Sparse High-Order Interaction Events
Zheng Wang, Yiming Xu, Conor Tillinghast, Shibo Li, Akil Narayan, Shandian Zhe
ICML 2022
Deep Multi-Fidelity Active Learning of High-Dimensional Outputs
Shibo Li, Zheng Wang, Mike Kirby, Shandian Zhe
AISTATS 2022
Batch Multi-Fidelity Bayesian Optimization with Deep Auto-Regressive Networks
Shibo Li, Mike Kirby, Shandian Zhe
NeurIPS 2021
Multi-Fidelity Bayesian Optimization via Deep Neural Networks
Shibo Li, Wei Xing, Robert M. Kirby, Shandian Zhe
NeurIPS 2020
arXiv | code | supplementary | video | bib
Scalable Variational Gaussian Process Regression Networks
Shibo Li, Wei Xing, Robert M. Kirby, Shandian Zhe
IJCAI 2020
Analysis of Multivariate Scoring Functions for Automatic Unbiased Learning to Rank
Tao Yang, Shikai Fang, Shibo Li, Yulan Wang, Qingyao Ai
CIKM 2020
Program Committee Member: UAI 2023, AISTATS 2023, UAI 2022, AISTATS 2022, ICMLA 2022
Conference Reviewer: NeurIPS 2023, NeurIPS 2022, NeurIPS 2022 MetaLearn Workshop, ICML 2022, AISTATS 2021, ICMLA 2021, UAI 2021, AAAI 2020