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2020 – today
- 2024
[j36]Peiyi Zhang, Tianning Dong, Faming Liang
:
An extended Langevinized ensemble Kalman filter for non-Gaussian dynamic systems. Comput. Stat. 39(6): 3347-3372 (2024)- [j35]Tianning Dong, Yan Sun, Faming Liang:
Deep network embedding with dimension selection. Neural Networks 179: 106512 (2024) - [c13]Yaxin Fang, Faming Liang:
Causal-StoNet: Causal Inference for High-Dimensional Complex Data. ICLR 2024 - [c12]Frank Shih, Faming Liang:
Fast Value Tracking for Deep Reinforcement Learning. ICLR 2024 - [i18]Frank Shih, Faming Liang:
Fast Value Tracking for Deep Reinforcement Learning. CoRR abs/2403.13178 (2024) - [i17]Yaxin Fang, Faming Liang:
Causal-StoNet: Causal Inference for High-Dimensional Complex Data. CoRR abs/2403.18994 (2024) - [i16]Faming Liang, Sehwan Kim, Yan Sun:
Extended Fiducial Inference: Toward an Automated Process of Statistical Inference. CoRR abs/2407.21622 (2024) - 2023
- [j34]Fabrizio Cicala, Weicheng Wang, Tianhao Wang, Ninghui Li, Elisa Bertino, Faming Liang, Yang Yang:
PURE: A Framework for Analyzing Proximity-based Contact Tracing Protocols. ACM Comput. Surv. 55(2): 3:1-3:36 (2023) - [j33]Tianning Dong, Peiyi Zhang, Faming Liang:
A Stochastic Approximation-Langevinized Ensemble Kalman Filter Algorithm for State Space Models with Unknown Parameters. J. Comput. Graph. Stat. 32(2): 448-469 (2023) - [c11]Wei Deng, Qian Zhang, Qi Feng, Faming Liang, Guang Lin:
Non-reversible Parallel Tempering for Deep Posterior Approximation. AAAI 2023: 7332-7339 - [c10]Mingxuan Zhang, Yan Sun, Faming Liang:
Sparse Deep Learning for Time Series Data: Theory and Applications. NeurIPS 2023 - [i15]Sehwan Kim, Qifan Song, Faming Liang:
A New Paradigm for Generative Adversarial Networks based on Randomized Decision Rules. CoRR abs/2306.13641 (2023) - [i14]Mingxuan Zhang, Yan Sun, Faming Liang:
Sparse Deep Learning for Time Series Data: Theory and Applications. CoRR abs/2310.03243 (2023) - 2022
- [j32]Wei Deng, Guang Lin, Faming Liang:
An adaptively weighted stochastic gradient MCMC algorithm for Monte Carlo simulation and global optimization. Stat. Comput. 32(4): 58 (2022) - [c9]Wei Deng, Siqi Liang, Botao Hao, Guang Lin, Faming Liang:
Interacting Contour Stochastic Gradient Langevin Dynamics. ICLR 2022 - [c8]Siqi Liang, Yan Sun, Faming Liang:
Nonlinear Sufficient Dimension Reduction with a Stochastic Neural Network. NeurIPS 2022 - [i13]Yan Sun, Faming Liang:
A Kernel-Expanded Stochastic Neural Network. CoRR abs/2201.05319 (2022) - [i12]Wei Deng, Siqi Liang, Botao Hao, Guang Lin, Faming Liang:
Interacting Contour Stochastic Gradient Langevin Dynamics. CoRR abs/2202.09867 (2022) - [i11]Siqi Liang, Yan Sun, Faming Liang:
Nonlinear Sufficient Dimension Reduction with a Stochastic Neural Network. CoRR abs/2210.04349 (2022) - [i10]Wei Deng, Qian Zhang, Qi Feng, Faming Liang, Guang Lin:
Non-reversible Parallel Tempering for Deep Posterior Approximation. CoRR abs/2211.10837 (2022) - 2021
- [j31]Yao Chen, Qingyi Gao, Faming Liang, Xiao Wang:
Nonlinear Variable Selection via Deep Neural Networks. J. Comput. Graph. Stat. 30(2): 484-492 (2021) - [c7]Wei Deng, Qi Feng, Georgios Karagiannis, Guang Lin, Faming Liang:
Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction. ICLR 2021 - [c6]Yan Sun, Wenjun Xiong, Faming Liang:
Sparse Deep Learning: A New Framework Immune to Local Traps and Miscalibration. NeurIPS 2021: 22301-22312 - [i9]Yan Sun, Qifan Song, Faming Liang:
Consistent Sparse Deep Learning: Theory and Computation. CoRR abs/2102.13229 (2021) - [i8]Yan Sun, Wenjun Xiong, Faming Liang:
Sparse Deep Learning: A New Framework Immune to Local Traps and Miscalibration. CoRR abs/2110.00653 (2021) - 2020
- [j30]Aiying Zhang, Biao Cai, Wenxing Hu, Bochao Jia, Faming Liang, Tony W. Wilson, Julia M. Stephen, Vince D. Calhoun, Yu-Ping Wang:
Joint Bayesian-Incorporating Estimation of Multiple Gaussian Graphical Models to Study Brain Connectivity Development in Adolescence. IEEE Trans. Medical Imaging 39(2): 357-365 (2020) - [c5]Wei Deng, Qi Feng, Liyao Gao, Faming Liang, Guang Lin:
Non-convex Learning via Replica Exchange Stochastic Gradient MCMC. ICML 2020: 2474-2483 - [c4]Wei Deng, Guang Lin, Faming Liang:
A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions. NeurIPS 2020 - [i7]Qifan Song, Yan Sun, Mao Ye, Faming Liang:
Extended Stochastic Gradient MCMC for Large-Scale Bayesian Variable Selection. CoRR abs/2002.02919 (2020) - [i6]Wei Deng, Qi Feng, Liyao Gao, Faming Liang, Guang Lin:
Non-convex Learning via Replica Exchange Stochastic Gradient MCMC. CoRR abs/2008.05367 (2020) - [i5]Sehwan Kim, Qifan Song, Faming Liang:
Stochastic Gradient Langevin Dynamics Algorithms with Adaptive Drifts. CoRR abs/2009.09535 (2020) - [i4]Wei Deng, Qi Feng, Georgios Karagiannis, Guang Lin, Faming Liang:
Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction. CoRR abs/2010.01084 (2020) - [i3]Wei Deng, Guang Lin, Faming Liang:
A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions. CoRR abs/2010.09800 (2020) - [i2]Fabrizio Cicala, Weicheng Wang, Tianhao Wang, Ninghui Li, Elisa Bertino, Faming Liang, Yang Yang:
PURE: A Framework for Analyzing Proximity-based Contact Tracing Protocols. CoRR abs/2012.09520 (2020)
2010 – 2019
- 2019
- [j29]Suwa Xu, Bochao Jia, Faming Liang:
Learning Moral Graphs in Construction of High-Dimensional Bayesian Networks for Mixed Data. Neural Comput. 31(6): 1183-1214 (2019) - [j28]Jingnan Xue, Faming Liang:
Double-Parallel Monte Carlo for Bayesian analysis of big data. Stat. Comput. 29(1): 23-32 (2019) - [j27]Aiying Zhang, Jian Fang, Faming Liang, Vince D. Calhoun, Yu-Ping Wang:
Aberrant Brain Connectivity in Schizophrenia Detected via a Fast Gaussian Graphical Model. IEEE J. Biomed. Health Informatics 23(4): 1479-1489 (2019) - [c3]Wei Deng, Xiao Zhang, Faming Liang, Guang Lin:
An Adaptive Empirical Bayesian Method for Sparse Deep Learning. NeurIPS 2019: 5564-5574 - [i1]Wei Deng, Xiao Zhang, Faming Liang, Guang Lin:
An Adaptive Empirical Bayesian Method for Sparse Deep Learning. CoRR abs/1910.10791 (2019) - 2017
- [j26]Donghyeon Yu, Johan Lim, Xinlei Wang, Faming Liang, Guanghua Xiao:
Enhanced construction of gene regulatory networks using hub gene information. BMC Bioinform. 18(1): 186:1-186:20 (2017) - [j25]Tianzhou Ma, Faming Liang, Steffi Oesterreich, George C. Tseng:
A Joint Bayesian Model for Integrating Microarray and RNA Sequencing Transcriptomic Data. J. Comput. Biol. 24(7): 647-662 (2017) - [j24]Georgios Karagiannis, Bledar A. Konomi, Guang Lin, Faming Liang:
Parallel and interacting stochastic approximation annealing algorithms for global optimisation. Stat. Comput. 27(4): 927-945 (2017) - 2016
- [j23]Yichen Cheng, Faming Liang:
Comment: "Modeling an Augmented Lagrangian for Blackbox Constrained Optimization" by Gramacy et al. Technometrics 58(1): 15-17 (2016) - [j22]Faming Liang, Jinsu Kim, Qifan Song:
A Bootstrap Metropolis-Hastings Algorithm for Bayesian Analysis of Big Data. Technometrics 58(3): 304-318 (2016) - 2014
- [j21]Ick-Hoon Jin, Faming Liang:
Use of SAMC for Bayesian analysis of statistical models with intractable normalizing constants. Comput. Stat. Data Anal. 71: 402-416 (2014) - [j20]Yichen Cheng, Xin Gao, Faming Liang:
Bayesian Peak Picking for NMR Spectra. Genom. Proteom. Bioinform. 12(1): 39-47 (2014) - [j19]Sooyoung Cheon, Faming Liang, Yuguo Chen, Kai Yu:
Stochastic approximation Monte Carlo importance sampling for approximating exact conditional probabilities. Stat. Comput. 24(4): 505-520 (2014) - 2013
- [j18]Chen Zhou, Ping Yang, Andrew E. Dessler, Faming Liang:
Statistical Properties of Horizontally Oriented Plates in Optically Thick Clouds From Satellite Observations. IEEE Geosci. Remote. Sens. Lett. 10(5): 986-990 (2013) - [j17]Faming Liang, Ick-Hoon Jin:
A Monte Carlo Metropolis-Hastings Algorithm for Sampling from Distributions with Intractable Normalizing Constants. Neural Comput. 25(8): 2199-2234 (2013) - 2011
- [j16]Sooyoung Cheon, Faming Liang:
Folding small proteins via annealing stochastic approximation Monte Carlo. Biosyst. 105(3): 243-249 (2011) - [j15]Faming Liang:
Annealing evolutionary stochastic approximation Monte Carlo for global optimization. Stat. Comput. 21(3): 375-393 (2011) - 2010
- [j14]Qianxing Mo, Faming Liang:
A hidden Ising model for ChIP-chip data analysis. Bioinform. 26(6): 777-783 (2010) - [j13]G. Peter Zhang, Craig A. Hill, Yusen Xia, Faming Liang:
Modeling the Relationship Between EDI Implementation and Firm Performance Improvement With Neural Networks. IEEE Trans Autom. Sci. Eng. 7(1): 96-110 (2010)
2000 – 2009
- 2009
- [j12]Mingqi Wu, Faming Liang, Yanan Tian:
Bayesian modeling of ChIP-chip data using latent variables. BMC Bioinform. 10: 352 (2009) - [j11]Faming Liang, Jian Zhang:
Learning Bayesian networks for discrete data. Comput. Stat. Data Anal. 53(4): 865-876 (2009) - [r1]Faming Liang:
Stochastic Approximation Monte Carlo for MLP Learning. Encyclopedia of Artificial Intelligence 2009: 1482-1489 - 2008
- [j10]Sooyoung Cheon, Faming Liang:
Phylogenetic tree construction using sequential stochastic approximation Monte Carlo. Biosyst. 91(1): 94-107 (2008) - [j9]Yuan Ren, Yu Ding, Faming Liang:
Adaptive evolutionary Monte Carlo algorithm for optimization with applications to sensor placement problems. Stat. Comput. 18(4): 375-390 (2008) - 2007
- [j8]Faming Liang:
Use of SVD-based probit transformation in clustering gene expression profiles. Comput. Stat. Data Anal. 51(12): 6355-6366 (2007) - [j7]Faming Liang:
Annealing stochastic approximation Monte Carlo algorithm for neural network training. Mach. Learn. 68(3): 201-233 (2007) - [j6]Faming Liang, Naisyin Wang:
Dynamic agglomerative clustering of gene expression profiles. Pattern Recognit. Lett. 28(9): 1062-1076 (2007) - 2006
- [j5]Faming Liang, Jianhua Huang:
Statistical and Computational Inverse Problems. Technometrics 48(1): 146 (2006) - 2005
- [j4]Faming Liang, Chuanhai Liu:
Efficient MCMC estimation of discrete distributions. Comput. Stat. Data Anal. 49(4): 1039-1052 (2005) - [j3]Faming Liang:
Evidence Evaluation for Bayesian Neural Networks Using Contour Monte Carlo. Neural Comput. 17(6): 1385-1410 (2005) - [j2]Faming Liang:
Bayesian neural networks for nonlinear time series forecasting. Stat. Comput. 15(1): 13-29 (2005) - 2003
- [j1]Faming Liang:
An Effective Bayesian Neural Network Classifier with a Comparison Study to Support Vector Machine. Neural Comput. 15(8): 1959-1989 (2003) - 2000
- [c2]Jason Cong, Tianming Kong, Faming Liang, Jun S. Liu, Wing Hung Wong, Dongmin Xu:
Dynamic weighting Monte Carlo for constrained floorplan designs in mixed signal application. ASP-DAC 2000: 277-282
1990 – 1999
- 1999
- [c1]Jason Cong, Tianming Kong, Dongmin Xu, Faming Liang, Jun S. Liu, Wing Hung Wong:
Relaxed Simulated Tempering for VLSI Floorplan Designs. ASP-DAC 1999: 13-16
Coauthor Index
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last updated on 2024-10-23 21:28 CEST by the dblp team
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