Inferring Brain Networks through Graphical Models with Hidden Variables. - Justin Dauwels; Hang Yu 002; Xueou Wang; François B. Vialatte; Charles-François Vincent Latchoumane; Jaeseung Jeong 002; Andrzej Cichocki (MLINI, 2011)

Modeling extreme events in spatial domain by copula graphical models. - Hang Yu 002; Zheng Choo; Wayne Isaac T. Uy; Justin Dauwels; Philip Jonathan (FUSION, 2012)

Copula Gaussian multiscale graphical models with application to geophysical modeling. - Hang Yu 002; Justin Dauwels; Xu Zhang; Shiyan Xu; Wayne Isaac T. Uy (FUSION, 2012)

Copula Gaussian graphical models with hidden variables. - Hang Yu 002; Justin Dauwels; Xueou Wang (ICASSP, 2012)

A Time-Delay-Integration CMOS image sensor with pipelined charge transfer architecture. - Hang Yu 002; Xinyuan Qian; Shoushun Chen; Kay Soon Low (ISCAS, 2012)

Modeling spatially-dependent extreme events with Markov random field priors. - Hang Yu 002; Zheng Choo; Justin Dauwels; Philip Jonathan; Qiao Zhou (ISIT, 2012)

A bio-inspired feedforward system for categorization of AER motion events. - Bo Zhao 018; Qiang Yu 005; Hang Yu 002; Shoushun Chen; Huajin Tang (BioCAS, 2013)

Copula Gaussian graphical model for discrete data. - Justin Dauwels; Hang Yu 002; Shiyan Xu; Xueou Wang (ICASSP, 2013)

A dual-exposure in-pixel charge subtraction CTIA CMOS image sensor for centroid measurement in star trackers. - Xinyuan Qian; Menghan Guo; Hang Yu 002; Shoushun Chen; Kay Soon Low (APCCAS, 2014)

Sensor fault detection by sparsity optimization. - Bingxuan Li; Hang Yu 002; Justin Dauwels; Kay Soon Low (ICASSP, 2014)

Extreme-value graphical models with multiple covariates. - Hang Yu 002; Jingjing Cheng; Justin Dauwels (ICASSP, 2014)

Network inference and change point detection for piecewise-stationary time series. - Hang Yu 002; Chenyang Li; Justin Dauwels (ICASSP, 2014)

Design and characterization of radiation-tolerant CMOS 4T Active Pixel Sensors. - Xinyuan Qian; Hang Yu 002; Shoushun Chen; Kay Soon Low (ISIC, 2014)

Spatio-temporal graphical models for extreme events. - Hang Yu 002; Liaofan Zhang; Justin Dauwels (ISIT, 2014)

Extreme-Value Graphical Models With Multiple Covariates. - Hang Yu 002; Justin Dauwels; Philip Jonathan (IEEE Trans. Signal Process., 2014)

Variational inference for graphical models of multivariate piecewise-stationary time series. - Hang Yu 002; Justin Dauwels (FUSION, 2015)

Variational Bayes learning of multiscale graphical models. - Hang Yu 002; Justin Dauwels (ICASSP, 2015)

An 8-stage time delay integration CMOS image sensor with on-chip polarization pixels. - Hang Yu 002; Vigil Varghese; Xinyuan Qian; Menghan Guo; Shoushun Chen; Kay Soon Low (ISCAS, 2015)

Variational Bayes learning of graphical models with hidden variables. - Hang Yu 002; Justin Dauwels (MLSP, 2015)

A time delay integration CMOS image sensor with online deblurring algorithm. - Hang Yu 002; Xinyuan Qian; Menghan Guo; Shoushun Chen; Kay Soon Low (VLSI-DAT, 2015)

Outlier-insensitive Kalman smoothing and marginal message passing. - Federico Wadehn; Lukas Bruderer; Justin Dauwels; Vijay Sahdeva; Hang Yu 002; Hans-Andrea Loeliger (EUSIPCO, 2016)

Variational Bayesian dynamic compressive sensing. - Hongwei Wang 005; Hang Yu 002; Michael Hoy; Justin Dauwels; Heping Wang (ISIT, 2016)

Latent tree ensemble of pairwise copulas for spatial extremes analysis. - Hang Yu 002; Junwei Huang; Justin Dauwels (ISIT, 2016)

Variational bayes learning of time-varying graphical models. - Hang Yu 002; Justin Dauwels (MLSP, 2016)

A Global-Shutter Centroiding Measurement CMOS Image Sensor With Star Region SNR Improvement for Star Trackers. - Xinyuan Qian; Hang Yu 002; Shoushun Chen (IEEE Trans. Circuits Syst. Video Technol., 2016)

An Antivibration Time-Delay Integration CMOS Image Sensor With Online Deblurring Algorithm. - Hang Yu 002; Xinyuan Qian; Menghan Guo; Shoushun Chen (IEEE Trans. Circuits Syst. Video Technol., 2016)

Modeling Spatio-Temporal Extreme Events Using Graphical Models. - Hang Yu 002; Justin Dauwels (IEEE Trans. Signal Process., 2016)

Linear-complexity stochastic variational Bayes inference for SLAM. - Xiaoyue Jiang; Michael Hoy; Hang Yu 002; Justin Dauwels (ITSC, 2017)

A Two-Step Prediction ADC Architecture for Integrated Low Power Image Sensors. - Hang Yu 002; Wei Tang 002; Menghan Guo; Shoushun Chen (IEEE Trans. Circuits Syst. I Regul. Pap., 2017)

Modeling Spatial Extremes via Ensemble-of-Trees of Pairwise Copulas. - Hang Yu 002; Wayne Isaac T. Uy; Justin Dauwels (IEEE Trans. Signal Process., 2017)

Efficient Stochastic Subgradient Descent Algorithms for High-dimensional Semi-sparse Graphical Model Selection. - Songwei Wu; Hang Yu 002; Justin Dauwels (ICASSP, 2019)

Variational Bayesian Point Set Registration. - Xiaoyue Jiang; Hang Yu 002; Michael Hoy; Justin Dauwels (VTC Fall, 2019)

Robust Linear-Complexity Approach to Full SLAM Problems: Stochastic Variational Bayes Inference. - Xiaoyue Jiang; Hang Yu 002; Michael Hoy; Justin Dauwels (VTC Fall, 2019)

Variational Wishart Approximation for Graphical Model Selection: Monoscale and Multiscale Models. - Hang Yu 002; Luyin Xin; Justin Dauwels (IEEE Trans. Signal Process., 2019)

Primal-Dual Stochastic Subgradient Method For Log-Determinant Optimization. - Songwei Wu; Hang Yu 002; Justin Dauwels (ICASSP, 2020)

Efficient Variational Bayesian Structure Learning of Dynamic Graphical Models. - Hang Yu 002; Justin Dauwels (CoRR, 2020)

Fast Bayesian Inference of Sparse Networks with Automatic Sparsity Determination. - Hang Yu 002; Songwei Wu; Luyin Xin; Justin Dauwels (J. Mach. Learn. Res., 2020)

Context Model for Pedestrian Intention Prediction Using Factored Latent-Dynamic Conditional Random Fields. - Satyajit Neogi; Michael Hoy; Kang Dang; Hang Yu 002; Justin Dauwels (IEEE Trans. Intell. Transp. Syst., 2021)

Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting. - Shizhan Liu; Hang Yu 002; Cong Liao; Jianguo Li; Weiyao Lin; Alex X. Liu; Schahram Dustdar (ICLR, 2022)

ZeroAE: Pre-trained Language Model based Autoencoder for Transductive Zero-shot Text Classification. - Kaihao Guo; Hang Yu 002; Cong Liao; Jianguo Li; Haipeng Zhang (ACL (Findings), 2023)

Twin Graph-Based Anomaly Detection via Attentive Multi-Modal Learning for Microservice System. - Jun Huang 003; Yang Yang; Hang Yu 002; Jianguo Li; Xiao Zheng (ASE, 2023)

BasisFormer: Attention-based Time Series Forecasting with Learnable and Interpretable Basis. - Zelin Ni; Hang Yu 002; Shizhan Liu; Jianguo Li; Weiyao Lin (NeurIPS, 2023)

MFTCoder: Boosting Code LLMs with Multitask Fine-Tuning. - Bingchang Liu; Chaoyu Chen; Cong Liao; Zi Gong; Huan Wang; Zhichao Lei; Ming Liang; Dajun Chen; Min Shen; Hailian Zhou; Hang Yu 002; Jianguo Li (CoRR, 2023)

A Survey on Language Models for Code. - Ziyin Zhang; Chaoyu Chen; Bingchang Liu; Cong Liao; Zi Gong; Hang Yu 002; Jianguo Li; Rui Wang 015 (CoRR, 2023)

BALANCE: Bayesian Linear Attribution for Root Cause Localization. - Chaoyu Chen; Hang Yu 002; Zhichao Lei; Jianguo Li; Shaokang Ren; Tingkai Zhang; Silin Hu; Jianchao Wang; Wenhui Shi (Proc. ACM Manag. Data, 2023)

Efficient Variational Bayes Learning of Graphical Models With Smooth Structural Changes. - Hang Yu 002; Songwei Wu; Justin Dauwels (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

D2LLM: Decomposed and Distilled Large Language Models for Semantic Search. - Zihan Liao; Hang Yu 002; Jianguo Li; Jun Wang 006; Wei Zhang 056 (ACL (1), 2024)

BaSIC: BayesNet Structure Learning for Computational Scalable Neural Image Compression. - Yufeng Zhang; Hang Yu 002; Shizhan Liu; Wenrui Dai; Weiyao Lin (ECCV (25), 2024)

CoBa: Convergence Balancer for Multitask Finetuning of Large Language Models. - Zi Gong; Hang Yu 002; Cong Liao; Bingchang Liu; Chaoyu Chen; Jianguo Li (EMNLP, 2024)

AmortizedPeriod: Attention-based Amortized Inference for Periodicity Identification. - Hang Yu 002; Cong Liao; Ruolan Liu; Jianguo Li; Yun Hu; Xinzhe Wang (ICLR, 2024)

Finite-State Autoregressive Entropy Coding for Efficient Learned Lossless Compression. - Yufeng Zhang; Hang Yu 002; Jianguo Li; Weiyao Lin (ICLR, 2024)

DUPLEX: Dual GAT for Complex Embedding of Directed Graphs. - Zhaoru Ke; Hang Yu 002; Jianguo Li; Haipeng Zhang (ICML, 2024)

CodeFuse-13B: A Pretrained Multi-lingual Code Large Language Model. - Peng Di; Jianguo Li; Hang Yu 002; Wei Jiang 011; Wenting Cai; Yang Cao; Chaoyu Chen; Dajun Chen; Hongwei Chen; Liang Chen; Gang Fan; Jie Gong; et al. (ICSE-SEIP, 2024)

MFTCoder: Boosting Code LLMs with Multitask Fine-Tuning. - Bingchang Liu; Chaoyu Chen; Zi Gong; Cong Liao; Huan Wang; Zhichao Lei; Ming Liang; Dajun Chen; Min Shen; Hailian Zhou; Wei Jiang 011; Hang Yu 002; et al. (KDD, 2024)

SQLfuse: Enhancing Text-to-SQL Performance through Comprehensive LLM Synergy. - Tingkai Zhang; Chaoyu Chen; Cong Liao; Jun Wang 006; Xudong Zhao; Hang Yu 002; Jianchao Wang; Jianguo Li; Wenhui Shi (CoRR, 2024)

GALLa: Graph Aligned Large Language Models for Improved Source Code Understanding. - Ziyin Zhang; Hang Yu 002; Shijie Li; Peng Di; Jianguo Li; Rui Wang 015 (CoRR, 2024)

Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code. - Ziyin Zhang; Chaoyu Chen; Bingchang Liu; Cong Liao; Zi Gong; Hang Yu 002; Jianguo Li; Rui Wang 015 (Trans. Mach. Learn. Res., 2024)

GALLa: Graph Aligned Large Language Models for Improved Source Code Understanding. - Ziyin Zhang; Hang Yu 002; Sage Lee; Peng Di; Jianguo Li; Rui Wang 015 (ACL (1), 2025)

Rodimus*: Breaking the Accuracy-Efficiency Trade-Off with Efficient Attentions. - Zhihao He; Hang Yu 002; Zi Gong; Shizhan Liu; Jianguo Li; Weiyao Lin (ICLR, 2025)

Every Sample Matters: Leveraging Mixture-of-Experts and High-Quality Data for Efficient and Accurate Code LLM. - Codefuse; Wenting Cai; Yuchen Cao; Chaoyu Chen; Chen Chen; Siba Chen; Qing Cui; Peng Di; Junpeng Fang; Zi Gong; Ting Guo; Zhengyu He; et al. (CoRR, 2025)

Code Graph Model (CGM): A Graph-Integrated Large Language Model for Repository-Level Software Engineering Tasks. - Hongyuan Tao; Ying Zhang; Zhenhao Tang; Hongen Peng; Xukun Zhu; Bingchang Liu; Yingguang Yang; Ziyin Zhang; Zhaogui Xu; Haipeng Zhang; Linchao Zhu; Rui Wang 015; et al. (CoRR, 2025)

ABC: Adaptive BayesNet Structure Learning for Computational Scalable Multi-task Image Compression. - Yufeng Zhang; Wenrui Dai; Hang Yu 002; Shizhan Liu; Junhui Hou; Jianguo Li; Weiyao Lin (CoRR, 2025)