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2020 – today
- 2023
[j11]Yihao Fang
, Mu Niu, Pokman Cheung, Lizhen Lin:
Extrinsic Bayesian Optimization on Manifolds. Algorithms 16(2): 117 (2023)- [j10]Minwoo Chae, Dongha Kim, Yongdai Kim, Lizhen Lin:
A Likelihood Approach to Nonparametric Estimation of a Singular Distribution Using Deep Generative Models. J. Mach. Learn. Res. 24: 77:1-77:42 (2023) - [i21]Yihao Fang, Ilsang Ohn, Vijay Gupta, Lizhen Lin:
Intrinsic and extrinsic deep learning on manifolds. CoRR abs/2302.08606 (2023) - [i20]Forough Fazeli-Asl, Michael Minyi Zhang, Lizhen Lin:
A Semi-Bayesian Nonparametric Hypothesis Test Using Maximum Mean Discrepancy with Applications in Generative Adversarial Networks. CoRR abs/2303.02637 (2023) - [i19]Steven Winter, Trevor Campbell, Lizhen Lin, Sanvesh Srivastava, David B. Dunson:
Machine Learning and the Future of Bayesian Computation. CoRR abs/2304.11251 (2023) - [i18]Ilsang Ohn, Lizhen Lin, Yongdai Kim:
A Bayesian sparse factor model with adaptive posterior concentration. CoRR abs/2305.18488 (2023) - [i17]Nathaniel Josephs, Arash A. Amini, Marina S. Paez, Lizhen Lin:
Nested stochastic block model for simultaneously clustering networks and nodes. CoRR abs/2307.09210 (2023) - 2022
- [j9]Yihao Hu, Tong Zhao, Shixin Xu, Lizhen Lin, Zhiliang Xu:
Neural-PDE: a RNN based neural network for solving time dependent PDEs. Commun. Inf. Syst. 22(2): 223-245 (2022) - [j8]Dong Quan Ngoc Nguyen, Lin Xing, Phuong Dong Tan Le, Lizhen Lin:
A graph-theoretical approach to DNA similarity analysis. Commun. Inf. Syst. 22(3): 383-400 (2022) - [j7]Bernardo Aquino, Arash Rahnama, Peter J. Seiler, Lizhen Lin, Vijay Gupta:
Robustness Against Adversarial Attacks in Neural Networks Using Incremental Dissipativity. IEEE Control. Syst. Lett. 6: 2341-2346 (2022) - [j6]Brian St. Thomas, Kisung You, Lizhen Lin, Lek-Heng Lim, Sayan Mukherjee:
Learning Subspaces of Different Dimensions. J. Comput. Graph. Stat. 31(1): 337-350 (2022) - [c9]Dianbin Bao, Kisung You, Lizhen Lin:
Network Distance based on Laplacian Flows on Graphs. IEEE Big Data 2022: 715-720 - [c8]Dong Quan Ngoc Nguyen, Phuong Dong Tan Le, Lin Xing, Lizhen Lin:
A Topological Characterization of DNA Sequences Based on Chaos Geometry and Persistent Homology. CSCI 2022: 1591-1597 - [i16]Luyi Shen, Arash A. Amini, Nathaniel Josephs, Lizhen Lin:
Bayesian community detection for networks with covariates. CoRR abs/2203.02090 (2022) - [i15]Yihao Fang, Mu Niu, Pokman Cheung, Lizhen Lin:
Extrinsic Bayesian Optimizations on Manifolds. CoRR abs/2212.13886 (2022) - 2021
- [c7]Ziqing Hu, Yihao Fang, Lizhen Lin:
Training Graph Neural Networks by Graphon Estimation. IEEE BigData 2021: 5153-5162 - [i14]Minwoo Chae, Dongha Kim, Yongdai Kim, Lizhen Lin:
A likelihood approach to nonparametric estimation of a singular distribution using deep generative models. CoRR abs/2105.04046 (2021) - [i13]Ziqing Hu, Yihao Fang, Lizhen Lin:
Training Graph Neural Networks by Graphon Estimation. CoRR abs/2109.01918 (2021) - [i12]Bernardo Aquino, Arash Rahnama, Peter Seiler, Lizhen Lin, Vijay Gupta:
Robustness against Adversarial Attacks in Neural Networks using Incremental Dissipativity. CoRR abs/2111.12906 (2021) - 2020
- [j5]Li Chen, Lizhen Lin, Jie Zhou:
A Hypothesis Testing for Large Weighted Networks With Applications to Functional Neuroimaging Data. IEEE Access 8: 191815-191825 (2020) - [c6]Mohammad Rasool Izadi, Yihao Fang, Robert Stevenson, Lizhen Lin:
Optimization of Graph Neural Networks with Natural Gradient Descent. IEEE BigData 2020: 171-179 - [c5]Dong Quan Ngoc Nguyen, Lin Xing, Lizhen Lin:
Weight Prediction for Variants of Weighted Directed Networks. IEEE BigData 2020: 5156-5163 - [c4]Dong Quan Ngoc Nguyen, Lin Xing, Lizhen Lin:
Community Detection, Pattern Recognition, and Hypergraph-Based Learning: Approaches Using Metric Geometry and Persistent Homology. FSDM 2020: 457-473 - [i11]Mohammad Rasool Izadi, Yihao Fang, Robert Stevenson, Lizhen Lin:
Optimization of Graph Neural Networks with Natural Gradient Descent. CoRR abs/2008.09624 (2020) - [i10]Yihao Hu, Tong Zhao, Zhiliang Xu, Lizhen Lin:
Neural Time-Dependent Partial Differential Equation. CoRR abs/2009.03892 (2020) - [i9]Dong Quan Ngoc Nguyen, Lin Xing, Lizhen Lin:
Weight Prediction for Variants of Weighted Directed Networks. CoRR abs/2009.14311 (2020) - [i8]Dong Quan Ngoc Nguyen, Lin Xing, Lizhen Lin:
Community detection, pattern recognition, and hypergraph-based learning: approaches using metric geometry and persistent homology. CoRR abs/2010.00435 (2020) - [i7]Lizhen Lin, Bayan Saparbayeva, Michael Minyi Zhang, David B. Dunson:
Accelerated Algorithms for Convex and Non-Convex Optimization on Manifolds. CoRR abs/2010.08908 (2020)
2010 – 2019
- 2019
- [i6]Arash A. Amini, Marina S. Paez, Lizhen Lin, Zahra S. Razaee:
Exact slice sampler for Hierarchical Dirichlet Processes. CoRR abs/1903.08829 (2019) - [i5]Marina S. Paez, Arash A. Amini, Lizhen Lin:
Hierarchical Stochastic Block Model for Community Detection in Multiplex Networks. CoRR abs/1904.05330 (2019) - 2018
- [j4]Michael Minyi Zhang, Henry Lam, Lizhen Lin:
Robust and parallel Bayesian model selection. Comput. Stat. Data Anal. 127: 229-247 (2018) - [c3]Bayan Saparbayeva, Michael Minyi Zhang, Lizhen Lin:
Communication Efficient Parallel Algorithms for Optimization on Manifolds. NeurIPS 2018: 3578-3588 - [i4]Mu Niu, Pokman Cheung, Lizhen Lin, Zhenwen Dai, Neil D. Lawrence, David B. Dunson:
Intrinsic Gaussian processes on complex constrained domains. CoRR abs/1801.01061 (2018) - [i3]Dianbin Bao, Kisung You, Lizhen Lin:
Network Distance Based on Laplacian Flows on Graphs. CoRR abs/1810.02906 (2018) - [i2]Bayan Saparbayeva, Michael Minyi Zhang, Lizhen Lin:
Communication Efficient Parallel Algorithms for Optimization on Manifolds. CoRR abs/1810.11155 (2018) - 2017
- [j3]Stanislav Minsker, Sanvesh Srivastava, Lizhen Lin, David B. Dunson:
Robust and Scalable Bayes via a Median of Subset Posterior Measures. J. Mach. Learn. Res. 18: 124:1-124:40 (2017) - [j2]Drew Lazar, Lizhen Lin:
Scale and curvature effects in principal geodesic analysis. J. Multivar. Anal. 153: 64-82 (2017) - [c2]Soumendu Sundar Mukherjee, Purnamrita Sarkar, Lizhen Lin:
On clustering network-valued data. NIPS 2017: 7071-7081 - 2014
- [c1]Stanislav Minsker, Sanvesh Srivastava, Lizhen Lin, David B. Dunson:
Scalable and Robust Bayesian Inference via the Median Posterior. ICML 2014: 1656-1664 - [i1]Stanislav Minsker, Sanvesh Srivastava, Lizhen Lin, David B. Dunson:
Robust and scalable Bayes via a median of subset posterior measures. CoRR abs/1403.2660 (2014) - 2013
- [j1]Rabi Bhattacharya, Lizhen Lin:
Recent progress in the nonparametric estimation of monotone curves - With applications to bioassay and environmental risk assessment. Comput. Stat. Data Anal. 63: 63-80 (2013)
Coauthor Index
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