Recent progress in the nonparametric estimation of monotone curves - With applications to bioassay and environmental risk assessment. - Rabi Bhattacharya; Lizhen Lin (Comput. Stat. Data Anal., 2013)

Scalable and Robust Bayesian Inference via the Median Posterior. - Stanislav Minsker; Sanvesh Srivastava; Lizhen Lin; David B. Dunson (ICML, 2014)

On clustering network-valued data. - Soumendu Sundar Mukherjee; Purnamrita Sarkar; Lizhen Lin (NIPS, 2017)

Robust and Scalable Bayes via a Median of Subset Posterior Measures. - Stanislav Minsker; Sanvesh Srivastava; Lizhen Lin; David B. Dunson (J. Mach. Learn. Res., 2017)

Scale and curvature effects in principal geodesic analysis. - Drew Lazar; Lizhen Lin (J. Multivar. Anal., 2017)

Communication Efficient Parallel Algorithms for Optimization on Manifolds. - Bayan Saparbayeva; Michael Minyi Zhang; Lizhen Lin (NeurIPS, 2018)

Intrinsic Gaussian processes on complex constrained domains. - Mu Niu; Pokman Cheung; Lizhen Lin; Zhenwen Dai; Neil D. Lawrence; David B. Dunson (CoRR, 2018)

Robust and parallel Bayesian model selection. - Michael Minyi Zhang; Henry Lam; Lizhen Lin (Comput. Stat. Data Anal., 2018)

Exact slice sampler for Hierarchical Dirichlet Processes. - Arash A. Amini; Marina S. Paez; Lizhen Lin; Zahra S. Razaee (CoRR, 2019)

Hierarchical Stochastic Block Model for Community Detection in Multiplex Networks. - Marina S. Paez; Arash A. Amini; Lizhen Lin (CoRR, 2019)

Optimization of Graph Neural Networks with Natural Gradient Descent. - Mohammad Rasool Izadi; Yihao Fang; Robert Stevenson; Lizhen Lin (IEEE BigData, 2020)

Weight Prediction for Variants of Weighted Directed Networks. - Dong Quan Ngoc Nguyen; Lin Xing; Lizhen Lin (IEEE BigData, 2020)

Community Detection, Pattern Recognition, and Hypergraph-Based Learning: Approaches Using Metric Geometry and Persistent Homology. - Dong Quan Ngoc Nguyen; Lin Xing; Lizhen Lin (FSDM, 2020)

A Hypothesis Testing for Large Weighted Networks With Applications to Functional Neuroimaging Data. - Li Chen; Lizhen Lin; Jie Zhou 002 (IEEE Access, 2020)

Neural Time-Dependent Partial Differential Equation. - Yihao Hu 003; Tong Zhao 003; Zhiliang Xu; Lizhen Lin (CoRR, 2020)

Training Graph Neural Networks by Graphon Estimation. - Ziqing Hu; Yihao Fang; Lizhen Lin (IEEE BigData, 2021)

Network Distance based on Laplacian Flows on Graphs. - Dianbin Bao; Kisung You; Lizhen Lin (IEEE Big Data, 2022)

A Topological Characterization of DNA Sequences Based on Chaos Geometry and Persistent Homology. - Dong Quan Ngoc Nguyen; Phuong Dong Tan Le; Lin Xing; Lizhen Lin (CSCI, 2022)

Neural-PDE: a RNN based neural network for solving time dependent PDEs. - Yihao Hu 003; Tong Zhao 003; Shixin Xu; Lizhen Lin; Zhiliang Xu (Commun. Inf. Syst., 2022)

A graph-theoretical approach to DNA similarity analysis. - Dong Quan Ngoc Nguyen; Lin Xing; Phuong Dong Tan Le; Lizhen Lin (Commun. Inf. Syst., 2022)

Bayesian community detection for networks with covariates. - Luyi Shen; Arash A. Amini; Nathaniel Josephs; Lizhen Lin (CoRR, 2022)

Extrinsic Bayesian Optimizations on Manifolds. - Yihao Fang; Mu Niu; Pokman Cheung; Lizhen Lin (CoRR, 2022)

Robustness Against Adversarial Attacks in Neural Networks Using Incremental Dissipativity. - Bernardo Aquino; Arash Rahnama; Peter J. Seiler; Lizhen Lin; Vijay Gupta 001 (IEEE Control. Syst. Lett., 2022)

Learning Subspaces of Different Dimensions. - Brian St. Thomas; Kisung You; Lizhen Lin; Lek-Heng Lim; Sayan Mukherjee 001 (J. Comput. Graph. Stat., 2022)

Intrinsic and extrinsic deep learning on manifolds. - Yihao Fang; Ilsang Ohn; Vijay Gupta 001; Lizhen Lin (CoRR, 2023)

A Semi-Bayesian Nonparametric Hypothesis Test Using Maximum Mean Discrepancy with Applications in Generative Adversarial Networks. - Forough Fazeli-Asl; Michael Minyi Zhang; Lizhen Lin (CoRR, 2023)

Machine Learning and the Future of Bayesian Computation. - Steven Winter; Trevor Campbell; Lizhen Lin; Sanvesh Srivastava; David B. Dunson (CoRR, 2023)

A Bayesian sparse factor model with adaptive posterior concentration. - Ilsang Ohn; Lizhen Lin; Yongdai Kim (CoRR, 2023)

Nested stochastic block model for simultaneously clustering networks and nodes. - Nathaniel Josephs; Arash A. Amini; Marina S. Paez; Lizhen Lin (CoRR, 2023)

A Likelihood Approach to Nonparametric Estimation of a Singular Distribution Using Deep Generative Models. - Minwoo Chae; Dongha Kim; Yongdai Kim; Lizhen Lin (J. Mach. Learn. Res., 2023)

Extrinsic Bayesian Optimization on Manifolds. - Yihao Fang; Mu Niu; Pokman Cheung; Lizhen Lin (Algorithms, 2023)

Classification of Directed Networks With Application to Neuroimaging Data. - Li Chen; Yuncheng Liu; Lizhen Lin; Dongpei Zhang (IEEE Access, 2024)

A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models. - Shivam Kumar; Yun Yang; Lizhen Lin (CoRR, 2024)

A Semi-Bayesian Nonparametric Estimator of the Maximum Mean Discrepancy Measure: Applications in Goodness-of-Fit Testing and Generative Adversarial Networks. - Forough Fazeli-Asl; Michael Minyi Zhang; Lizhen Lin (Trans. Mach. Learn. Res., 2024)

Conditional Diffusion Models are Minimax-Optimal and Manifold-Adaptive for Conditional Distribution Estimation. - Rong Tang; Lizhen Lin; Yun Yang (ICLR, 2025)

Accelerated algorithms for convex and non-convex optimization on manifolds. - Lizhen Lin; Bayan Saparbayeva; Michael Minyi Zhang; David B. Dunson (Mach. Learn., 2025)

Robust and Scalable Variational Bayes. - Carlos Misael Madrid Padilla; Shitao Fan; Lizhen Lin (CoRR, 2025)

Schreier-Coset Graph Propagation. - Aryan Mishra; Lizhen Lin (CoRR, 2025)

Posterior Contraction for Sparse Neural Networks in Besov Spaces with Intrinsic Dimensionality. - Kyeongwon Lee; Lizhen Lin; Jaewoo Park; Seonghyun Jeong (CoRR, 2025)