An efficient surrogate-based method for computing rare failure probability. - Jing Li; Jinglai Li; Dongbin Xiu (J. Comput. Phys., 2011)

Extracting Solitons from Noisy Pulses. - Jinglai Li; William L. Kath (SIAM J. Appl. Math., 2012)

Adaptive Construction of Surrogates for the Bayesian Solution of Inverse Problems. - Jinglai Li; Youssef M. Marzouk (SIAM J. Sci. Comput., 2014)

A frozen Gaussian approximation-based multi-level particle swarm optimization for seismic inversion. - Jinglai Li; Guang Lin 001; Xu Yang 018 (J. Comput. Phys., 2015)

Gaussian process surrogates for failure detection: A Bayesian experimental design approach. - Hongqiao Wang; Guang Lin 001; Jinglai Li (J. Comput. Phys., 2016)

A surrogate accelerated multicanonical Monte Carlo method for uncertainty quantification. - Keyi Wu; Jinglai Li (J. Comput. Phys., 2016)

A subset multicanonical Monte Carlo method for simulating rare failure events. - Xinjuan Chen; Jinglai Li (J. Comput. Phys., 2017)

On an adaptive preconditioned Crank-Nicolson MCMC algorithm for infinite dimensional Bayesian inference. - Zixi Hu; Zhewei Yao; Jinglai Li (J. Comput. Phys., 2017)

A Hybrid Adaptive MCMC Algorithm in Function Spaces. - Qingping Zhou; Zixi Hu; Zhewei Yao; Jinglai Li (SIAM/ASA J. Uncertain. Quantification, 2017)

Adaptive Gaussian Process Approximation for Bayesian Inference with Expensive Likelihood Functions. - Hongqiao Wang; Jinglai Li (Neural Comput., 2018)

An Adaptive Independence Sampler MCMC Algorithm for Bayesian Inferences of Functions. - Zhe Feng 004; Jinglai Li (SIAM J. Sci. Comput., 2018)

A Hierarchical Neural Hybrid Method for Failure Probability Estimation. - Ke Li 039; Kejun Tang; Jinglai Li; Tianfan Wu; Qifeng Liao (IEEE Access, 2019)

An adaptive reduced basis ANOVA method for high-dimensional Bayesian inverse problems. - Qifeng Liao; Jinglai Li (J. Comput. Phys., 2019)

An approximate KLD based experimental design for models with intractable likelihoods. - Ziqiao Ao; Jinglai Li (AISTATS, 2020)

Bayesian Optimization with Local Search. - Yuzhou Gao; Tengchao Yu; Jinglai Li (LOD (2), 2020)

A Defensive Marginal Particle Filtering Method for Data Assimilation. - Linjie Wen; Jiangqi Wu; Linjun Lu; Jinglai Li (SIAM/ASA J. Uncertain. Quantification, 2020)

Bayesian Inference and Uncertainty Quantification for Medical Image Reconstruction with Poisson Data. - Qingping Zhou; Tengchao Yu; Xiaoqun Zhang; Jinglai Li (SIAM J. Imaging Sci., 2020)

Linear-Mapping based Variational Ensemble Kalman Filter. - Linjie Wen; Jinglai Li (CoRR, 2021)

Maximum Conditional Entropy Hamiltonian Monte Carlo Sampler. - Tengchao Yu; Hongqiao Wang; Jinglai Li (SIAM J. Sci. Comput., 2021)

Entropy Estimation via Normalizing Flow. - Ziqiao Ao; Jinglai Li (AAAI, 2022)

Clustered active-subspace based local Gaussian Process emulator for high-dimensional and complex computer models. - Junda Xiong; Xin Cai; Jinglai Li (J. Comput. Phys., 2022)

Affine-mapping based variational ensemble Kalman filter. - Linjie Wen; Jinglai Li (Stat. Comput., 2022)

Ensemble Kalman filter based sequential Monte Carlo sampler for sequential Bayesian inference. - Jiangqi Wu; Linjie Wen; Peter L. Green; Jinglai Li; Simon Maskell (Stat. Comput., 2022)

Approximate Primal-Dual Fixed-Point based Langevin Algorithms for Non-smooth Convex Potentials. - Ziruo Cai; Jinglai Li; Xiaoqun Zhang (CoRR, 2023)

NF-ULA: Langevin Monte Carlo with Normalizing Flow Prior for Imaging Inverse Problems. - Ziruo Cai; Junqi Tang; Subhadip Mukherjee; Jinglai Li; Carola-Bibiane Schönlieb; Xiaoqun Zhang (CoRR, 2023)

Deep Unrolling Networks with Recurrent Momentum Acceleration for Nonlinear Inverse Problems. - Qingping Zhou; Jiayu Qian; Junqi Tang; Jinglai Li (CoRR, 2023)

On multilevel Monte Carlo methods for deterministic and uncertain hyperbolic systems. - Junpeng Hu; Shi Jin 002; Jinglai Li; Lei Zhang 184 (J. Comput. Phys., 2023)

Entropy estimation via uniformization. - Ziqiao Ao; Jinglai Li (Artif. Intell., 2023)

Multicanonical sequential Monte Carlo sampler for uncertainty quantification. - Robert Millar; Hui Li; Jinglai Li (Reliab. Eng. Syst. Saf., 2023)

VI-DGP: A Variational Inference Method with Deep Generative Prior for Solving High-Dimensional Inverse Problems. - Yingzhi Xia; Qifeng Liao; Jinglai Li (J. Sci. Comput., 2023)

On Estimating the Gradient of the Expected Information Gain in Bayesian Experimental Design. - Ziqiao Ao; Jinglai Li (AAAI, 2024)

ODEs Learn to Walk: ODE-Net based Data-Driven Modeling for Crowd Dynamics. - Chen Cheng; Jinglai Li (AAMAS, 2024)

Sketched Equivariant Imaging Regularization and Deep Internal Learning for Inverse Problems. - Guixian Xu; Jinglai Li; Junqi Tang (CoRR, 2024)

Domain-decomposed Bayesian inversion based on local Karhunen-Loève expansions. - Zhihang Xu; Qifeng Liao; Jinglai Li (J. Comput. Phys., 2024)

NF-ULA: Normalizing Flow-Based Unadjusted Langevin Algorithm for Imaging Inverse Problems. - Ziruo Cai; Junqi Tang; Subhadip Mukherjee; Jinglai Li; Carola-Bibiane Schönlieb; Xiaoqun Zhang (SIAM J. Imaging Sci., 2024)

Sampling-based adaptive design strategy for failure probability estimation. - Tiexin Guo; Hongji Wang; Jinglai Li; Hongqiao Wang (Reliab. Eng. Syst. Saf., 2024)

Bayesian Optimization for CVaR-based portfolio optimization. - Robert Millar; Jinglai Li (GECCO, 2025)

Gaussian Processes Regression for Uncertainty Quantification: An Introductory Tutorial. - Jinglai Li; Hongqiao Wang (CoRR, 2025)

Stochastic gradient descent based variational inference for infinite-dimensional inverse problems. - Jiaming Sui; Junxiong Jia; Jinglai Li (CoRR, 2025)

Fast Equivariant Imaging: Acceleration for Unsupervised Learning via Augmented Lagrangian and Auxiliary PnP Denoisers. - Guixian Xu; Jinglai Li; Junqi Tang (CoRR, 2025)