Faming Liang - RankMe

74 papers A* 11Journal 60Unranked 2

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Deep Survival Analysis for Competing Risk Modeling with Functional Covariates and Missing Data Imputation. CoRR Penglei Gao, Yan Zou, Abhijit Duggal, Shuaiqi Huang, Faming Liang, Xiaofeng Wang
2025	J	`jnl`	Extended Fiducial Inference for Individual Treatment Effects via Deep Neural Networks. CoRR Sehwan Kim, Faming Liang
2025	J	`jnl`	Extended fiducial inference for individual treatment effects via deep neural networks. Stat. Comput. Sehwan Kim, Faming Liang
2025	J	`jnl`	Latent Trajectory: A New Framework for Deep Actor-Critic Reinforcement Learning with Uncertainty Quantification. Trans. Mach. Learn. Res. Frank Shih, Faming Liang
2025	J	`jnl`	Uncertainty Quantification for Physics-Informed Neural Networks with Extended Fiducial Inference. CoRR Frank Shih, Zhenghao Jiang, Faming Liang
2024	J	`jnl`	An extended Langevinized ensemble Kalman filter for non-Gaussian dynamic systems. Comput. Stat. Peiyi Zhang, Tianning Dong, Faming Liang
2024	A*	`conf`	Causal-StoNet: Causal Inference for High-Dimensional Complex Data. ICLR Yaxin Fang, Faming Liang
2024	J	`jnl`	Causal-StoNet: Causal Inference for High-Dimensional Complex Data. CoRR Yaxin Fang, Faming Liang
2024	J	`jnl`	Deep network embedding with dimension selection. Neural Networks Tianning Dong, Yan Sun, Faming Liang
2024	J	`jnl`	Extended Fiducial Inference: Toward an Automated Process of Statistical Inference. CoRR Faming Liang, Sehwan Kim, Yan Sun
2024	A*	`conf`	Fast Value Tracking for Deep Reinforcement Learning. ICLR Frank Shih, Faming Liang
2024	J	`jnl`	Fast Value Tracking for Deep Reinforcement Learning. CoRR Frank Shih, Faming Liang
2024	J	`jnl`	Magnitude Pruning of Large Pretrained Transformer Models with a Mixture Gaussian Prior. CoRR Mingxuan Zhang, Yan Sun, Faming Liang
2023	J	`jnl`	A New Paradigm for Generative Adversarial Networks based on Randomized Decision Rules. CoRR Sehwan Kim, Qifan Song, Faming Liang
2023	J	`jnl`	A Stochastic Approximation-Langevinized Ensemble Kalman Filter Algorithm for State Space Models with Unknown Parameters. J. Comput. Graph. Stat. Tianning Dong, Peiyi Zhang, Faming Liang
2023	A*	`conf`	Non-reversible Parallel Tempering for Deep Posterior Approximation. AAAI Wei Deng, Qian Zhang, Qi Feng, Faming Liang, Guang Lin
2023	J	`jnl`	PURE: A Framework for Analyzing Proximity-based Contact Tracing Protocols. ACM Comput. Surv. Fabrizio Cicala, Weicheng Wang, Tianhao Wang, Ninghui Li, Elisa Bertino, Faming Liang, Yang Yang
2023	A*	`conf`	Sparse Deep Learning for Time Series Data: Theory and Applications. NeurIPS Mingxuan Zhang, Yan Sun, Faming Liang
2023	J	`jnl`	Sparse Deep Learning for Time Series Data: Theory and Applications. CoRR Mingxuan Zhang, Yan Sun, Faming Liang
2022	J	`jnl`	A Kernel-Expanded Stochastic Neural Network. CoRR Yan Sun, Faming Liang
2022	J	`jnl`	An adaptively weighted stochastic gradient MCMC algorithm for Monte Carlo simulation and global optimization. Stat. Comput. Wei Deng, Guang Lin, Faming Liang
2022	A*	`conf`	Interacting Contour Stochastic Gradient Langevin Dynamics. ICLR Wei Deng, Siqi Liang, Botao Hao, Guang Lin, Faming Liang
2022	J	`jnl`	Interacting Contour Stochastic Gradient Langevin Dynamics. CoRR Wei Deng, Siqi Liang, Botao Hao, Guang Lin, Faming Liang
2022	J	`jnl`	Non-reversible Parallel Tempering for Deep Posterior Approximation. CoRR Wei Deng, Qian Zhang, Qi Feng, Faming Liang, Guang Lin
2022	A*	`conf`	Nonlinear Sufficient Dimension Reduction with a Stochastic Neural Network. NeurIPS Siqi Liang, Yan Sun, Faming Liang
2022	J	`jnl`	Nonlinear Sufficient Dimension Reduction with a Stochastic Neural Network. CoRR Siqi Liang, Yan Sun, Faming Liang
2021	A*	`conf`	Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction. ICLR Wei Deng, Qi Feng, Georgios Karagiannis, Guang Lin, Faming Liang
2021	J	`jnl`	Consistent Sparse Deep Learning: Theory and Computation. CoRR Yan Sun, Qifan Song, Faming Liang
2021	J	`jnl`	Nonlinear Variable Selection via Deep Neural Networks. J. Comput. Graph. Stat. Yao Chen, Qingyi Gao, Faming Liang, Xiao Wang
2021	A*	`conf`	Sparse Deep Learning: A New Framework Immune to Local Traps and Miscalibration. NeurIPS Yan Sun, Wenjun Xiong, Faming Liang
2021	J	`jnl`	Sparse Deep Learning: A New Framework Immune to Local Traps and Miscalibration. CoRR Yan Sun, Wenjun Xiong, Faming Liang
2020	A*	`conf`	A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions. NeurIPS Wei Deng, Guang Lin, Faming Liang
2020	J	`jnl`	A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions. CoRR Wei Deng, Guang Lin, Faming Liang
2020	J	`jnl`	Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction. CoRR Wei Deng, Qi Feng, Georgios Karagiannis, Guang Lin, Faming Liang
2020	J	`jnl`	Extended Stochastic Gradient MCMC for Large-Scale Bayesian Variable Selection. CoRR Qifan Song, Yan Sun, Mao Ye, Faming Liang
2020	J	`jnl`	Joint Bayesian-Incorporating Estimation of Multiple Gaussian Graphical Models to Study Brain Connectivity Development in Adolescence. IEEE Trans. Medical Imaging Aiying Zhang, Biao Cai, Wenxing Hu, Bochao Jia, Faming Liang, Tony W. Wilson, Julia M. Stephen, Vince D. Calhoun, Yu-Ping Wang
2020	A*	`conf`	Non-convex Learning via Replica Exchange Stochastic Gradient MCMC. ICML Wei Deng, Qi Feng, Liyao Gao, Faming Liang, Guang Lin
2020	J	`jnl`	Non-convex Learning via Replica Exchange Stochastic Gradient MCMC. CoRR Wei Deng, Qi Feng, Liyao Gao, Faming Liang, Guang Lin
2020	J	`jnl`	PURE: A Framework for Analyzing Proximity-based Contact Tracing Protocols. CoRR Fabrizio Cicala, Weicheng Wang, Tianhao Wang, Ninghui Li, Elisa Bertino, Faming Liang, Yang Yang
2020	J	`jnl`	Stochastic Gradient Langevin Dynamics Algorithms with Adaptive Drifts. CoRR Sehwan Kim, Qifan Song, Faming Liang
2019	J	`jnl`	Aberrant Brain Connectivity in Schizophrenia Detected via a Fast Gaussian Graphical Model. IEEE J. Biomed. Health Informatics Aiying Zhang, Jian Fang, Faming Liang, Vince D. Calhoun, Yu-Ping Wang
2019	A*	`conf`	An Adaptive Empirical Bayesian Method for Sparse Deep Learning. NeurIPS Wei Deng, Xiao Zhang, Faming Liang, Guang Lin
2019	J	`jnl`	An Adaptive Empirical Bayesian Method for Sparse Deep Learning. CoRR Wei Deng, Xiao Zhang, Faming Liang, Guang Lin
2019	J	`jnl`	Double-Parallel Monte Carlo for Bayesian analysis of big data. Stat. Comput. Jingnan Xue, Faming Liang
2019	J	`jnl`	Learning Moral Graphs in Construction of High-Dimensional Bayesian Networks for Mixed Data. Neural Comput. Suwa Xu, Bochao Jia, Faming Liang
2017	J	`jnl`	A Joint Bayesian Model for Integrating Microarray and RNA Sequencing Transcriptomic Data. J. Comput. Biol. Tianzhou Ma, Faming Liang, Steffi Oesterreich, George C. Tseng
2017	J	`jnl`	Enhanced construction of gene regulatory networks using hub gene information. BMC Bioinform. Donghyeon Yu, Johan Lim, Xinlei Wang, Faming Liang, Guanghua Xiao
2017	J	`jnl`	Parallel and interacting stochastic approximation annealing algorithms for global optimisation. Stat. Comput. Georgios Karagiannis, Bledar A. Konomi, Guang Lin, Faming Liang
2016	J	`jnl`	A Bootstrap Metropolis-Hastings Algorithm for Bayesian Analysis of Big Data. Technometrics Faming Liang, Jinsu Kim, Qifan Song
2016	J	`jnl`	Comment: "Modeling an Augmented Lagrangian for Blackbox Constrained Optimization" by Gramacy et al. Technometrics Yichen Cheng, Faming Liang
2014	J	`jnl`	Bayesian Peak Picking for NMR Spectra. Genom. Proteom. Bioinform. Yichen Cheng, Xin Gao, Faming Liang
2014	J	`jnl`	Stochastic approximation Monte Carlo importance sampling for approximating exact conditional probabilities. Stat. Comput. Sooyoung Cheon, Faming Liang, Yuguo Chen, Kai Yu
2014	J	`jnl`	Use of SAMC for Bayesian analysis of statistical models with intractable normalizing constants. Comput. Stat. Data Anal. Ick-Hoon Jin, Faming Liang
2013	J	`jnl`	A Monte Carlo Metropolis-Hastings Algorithm for Sampling from Distributions with Intractable Normalizing Constants. Neural Comput. Faming Liang, Ick-Hoon Jin
2013	J	`jnl`	Statistical Properties of Horizontally Oriented Plates in Optically Thick Clouds From Satellite Observations. IEEE Geosci. Remote. Sens. Lett. Chen Zhou, Ping Yang, Andrew E. Dessler, Faming Liang
2011	J	`jnl`	Annealing evolutionary stochastic approximation Monte Carlo for global optimization. Stat. Comput. Faming Liang
2011	J	`jnl`	Folding small proteins via annealing stochastic approximation Monte Carlo. Biosyst. Sooyoung Cheon, Faming Liang
2010	J	`jnl`	A hidden Ising model for ChIP-chip data analysis. Bioinform. Qianxing Mo, Faming Liang
2010	J	`jnl`	Modeling the Relationship Between EDI Implementation and Firm Performance Improvement With Neural Networks. IEEE Trans Autom. Sci. Eng. G. Peter Zhang, Craig A. Hill, Yusen Xia, Faming Liang
2009	J	`jnl`	Bayesian modeling of ChIP-chip data using latent variables. BMC Bioinform. Mingqi Wu, Faming Liang, Yanan Tian
2009	J	`jnl`	Learning Bayesian networks for discrete data. Comput. Stat. Data Anal. Faming Liang, Jian Zhang
2009	—	`ch.`	Stochastic Approximation Monte Carlo for MLP Learning. Encyclopedia of Artificial Intelligence Faming Liang
2008	J	`jnl`	Adaptive evolutionary Monte Carlo algorithm for optimization with applications to sensor placement problems. Stat. Comput. Yuan Ren, Yu Ding, Faming Liang
2008	J	`jnl`	Phylogenetic tree construction using sequential stochastic approximation Monte Carlo. Biosyst. Sooyoung Cheon, Faming Liang
2007	J	`jnl`	Annealing stochastic approximation Monte Carlo algorithm for neural network training. Mach. Learn. Faming Liang
2007	J	`jnl`	Dynamic agglomerative clustering of gene expression profiles. Pattern Recognit. Lett. Faming Liang, Naisyin Wang
2007	J	`jnl`	Use of SVD-based probit transformation in clustering gene expression profiles. Comput. Stat. Data Anal. Faming Liang
2006	J	`jnl`	Statistical and Computational Inverse Problems. Technometrics Faming Liang, Jianhua Huang
2005	J	`jnl`	Bayesian neural networks for nonlinear time series forecasting. Stat. Comput. Faming Liang
2005	J	`jnl`	Efficient MCMC estimation of discrete distributions. Comput. Stat. Data Anal. Faming Liang, Chuanhai Liu
2005	J	`jnl`	Evidence Evaluation for Bayesian Neural Networks Using Contour Monte Carlo. Neural Comput. Faming Liang
2003	J	`jnl`	An Effective Bayesian Neural Network Classifier with a Comparison Study to Support Vector Machine. Neural Comput. Faming Liang
2000	—	`conf`	Dynamic weighting Monte Carlo for constrained floorplan designs in mixed signal application. ASP-DAC Jason Cong, Tianming Kong, Faming Liang, Jun S. Liu, Wing Hung Wong, Dongmin Xu
1999	—	`conf`	Relaxed Simulated Tempering for VLSI Floorplan Designs. ASP-DAC Jason Cong, Tianming Kong, Dongmin Xu, Faming Liang, Jun S. Liu, Wing Hung Wong

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.