Multivariate Varying Coefficient Models for DTI Tract Statistics. - Hongtu Zhu; Martin Styner; Yimei Li; Linglong Kong; Yundi Shi; Weili Lin; Christopher L. Coe; John H. Gilmore (MICCAI (1), 2010)

Smooth depth contours characterize the underlying distribution. - Linglong Kong; Yijun Zuo (J. Multivar. Anal., 2010)

FADTTS: Functional analysis of diffusion tensor tract statistics. - Hongtu Zhu; Linglong Kong; Runze Li 001; Martin Styner; Guido Gerig; Weili Lin; John H. Gilmore (NeuroImage, 2011)

Functional Nonlinear Mixed Effects Models for Longitudinal Image Data. - Xinchao Luo; Lixing Zhu; Linglong Kong; Hongtu Zhu (IPMI, 2015)

House Price Modeling over Heterogeneous Regions with Hierarchical Spatial Functional Analysis. - Bang Liu; Borislav Mavrin; Di Niu; Linglong Kong (ICDM, 2016)

Local Region Sparse Learning for Image-on-Scalar Regression. - Yao Chen; Xiao Wang 045; Linglong Kong; Hongtu Zhu (CoRR, 2016)

Regularized quantile regression under heterogeneous sparsity with application to quantitative genetic traits. - Qianchuan He; Linglong Kong; Yanhua Wang; Sijian Wang; Timothy A. Chan; Eric Holland (Comput. Stat. Data Anal., 2016)

Partial functional linear quantile regression for neuroimaging data analysis. - Dengdeng Yu; Linglong Kong; Ivan Mizera (Neurocomputing, 2016)

Expectile Matrix Factorization for Skewed Data Analysis. - Rui Zhu 007; Di Niu; Linglong Kong; Zongpeng Li (AAAI, 2017)

Growing Story Forest Online from Massive Breaking News. - Bang Liu; Di Niu; Kunfeng Lai; Linglong Kong; Yu Xu (CIKM, 2017)

Recover Fine-Grained Spatial Data from Coarse Aggregation. - Bang Liu; Borislav Mavrin; Linglong Kong; Di Niu (ICDM, 2017)

An Unbiased Penalty for Sparse Classification with Application to Neuroimaging Data. - Li Zhang; Dana Cobzas; Alan H. Wilman; Linglong Kong (MICCAI (3), 2017)

QUOTA: The Quantile Option Architecture for Reinforcement Learning. - Shangtong Zhang; Borislav Mavrin; Linglong Kong; Bo Liu 006; Hengshuai Yao (CoRR, 2018)

Significant Anatomy Detection Through Sparse Classification: A Comparative Study. - Li Zhang; Dana Cobzas; Alan H. Wilman; Linglong Kong (IEEE Trans. Medical Imaging, 2018)

Exploration in the Face of Parametric and Intrinsic Uncertainties. - Borislav Mavrin; Shangtong Zhang; Hengshuai Yao; Linglong Kong (AAMAS, 2019)

M-estimation in Low-Rank Matrix Factorization: A General Framework. - Wei Tu; Peng Liu 048; Jingyu Zhao 001; Yi Liu 062; Linglong Kong; Guodong Li; Bei Jiang; Guangjian Tian; Hengshuai Yao (ICDM, 2019)

Distributional Reinforcement Learning for Efficient Exploration. - Borislav Mavrin; Hengshuai Yao; Linglong Kong; Kaiwen Wu; Yaoliang Yu (ICML, 2019)

Ensemble-based Ultrahigh-dimensional Variable Screening. - Wei Tu; Dong Yang; Linglong Kong; Menglu Che; Qian Shi; Guodong Li; Guangjian Tian (IJCAI, 2019)

Deep Reinforcement Learning with Decorrelation. - Borislav Mavrin; Hengshuai Yao; Linglong Kong (CoRR, 2019)

Distributional Reinforcement Learning for Efficient Exploration. - Borislav Mavrin; Shangtong Zhang; Hengshuai Yao; Linglong Kong; Kaiwen Wu; Yaoliang Yu (CoRR, 2019)

Learning Privately over Distributed Features: An ADMM Sharing Approach. - Yaochen Hu 001; Peng Liu 048; Linglong Kong; Di Niu (CoRR, 2019)

Sparse wavelet estimation in quantile regression with multiple functional predictors. - Dengdeng Yu; Li Zhang 091; Ivan Mizera; Bei Jiang; Linglong Kong (Comput. Stat. Data Anal., 2019)

Sparse Multicategory Generalized Distance Weighted Discrimination in Ultra-High Dimensions. - Tong Su; Yafei Wang; Yi Liu 062; William G. Branton; Eugene Asahchop; Christopher Power; Bei Jiang; Linglong Kong; Niansheng Tang (Entropy, 2020)

Story Forest: Extracting Events and Telling Stories from Breaking News. - Bang Liu; Fred X. Han; Di Niu; Linglong Kong; Kunfeng Lai; Yu Xu (ACM Trans. Knowl. Discov. Data, 2020)

L2NAS: Learning to Optimize Neural Architectures via Continuous-Action Reinforcement Learning. - Keith G. Mills; Fred X. Han; Mohammad Salameh; Seyed Saeed Changiz Rezaei; Linglong Kong; Wei Lu 023; Shuo Lian; Shangling Jui; Di Niu (CIKM, 2021)

Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization. - Ke Sun 013; Yafei Wang; Yi Liu 062; Yingnan Zhao; Bo Pan; Shangling Jui; Bei Jiang; Linglong Kong (NeurIPS, 2021)

Exploring the Robustness of Distributional Reinforcement Learning against Noisy State Observations. - Ke Sun 013; Yi Liu 062; Yingnan Zhao; Hengshuai Yao; Shangling Jui; Linglong Kong (CoRR, 2021)

A Simple Unified Framework for Anomaly Detection in Deep Reinforcement Learning. - Hongming Zhang 003; Ke Sun 013; Bo Xu 002; Linglong Kong; Martin Müller 003 (CoRR, 2021)

L$^{2}$NAS: Learning to Optimize Neural Architectures via Continuous-Action Reinforcement Learning. - Keith G. Mills; Fred X. Han; Mohammad Salameh; Seyed Saeed Changiz Rezaei; Linglong Kong; Wei Lu 023; Shuo Lian; Shangling Jui; Di Niu (CoRR, 2021)

Towards Understanding Distributional Reinforcement Learning: Regularization, Optimization, Acceleration and Sinkhorn Algorithm. - Ke Sun 013; Yingnan Zhao; Yi Liu 062; Enze Shi; Yafei Wang; Aref Sadeghi; Xiaodong Yan; Bei Jiang; Linglong Kong (CoRR, 2021)

Advanced algorithms for penalized quantile and composite quantile regression. - Matthew Pietrosanu; Jueyu Gao; Linglong Kong; Bei Jiang; Di Niu (Comput. Stat., 2021)

Testing independence of functional variables by angle covariance. - Tingyu Lai; Zhongzhan Zhang; Yafei Wang; Linglong Kong (J. Multivar. Anal., 2021)

Word Embeddings via Causal Inference: Gender Bias Reducing and Semantic Information Preserving. - Lei Ding 013; Dengdeng Yu; Jinhan Xie; Wenxing Guo; Shenggang Hu; Meichen Liu; Linglong Kong; Hongsheng Dai; Yanchun Bao; Bei Jiang (AAAI, 2022)

Sample Average Approximation for Stochastic Optimization with Dependent Data: Performance Guarantees and Tractability. - Yafei Wang; Bo Pan; Wei Tu; Peng Liu 048; Bei Jiang; Chao Gao; Wei Lu 023; Shangling Jui; Linglong Kong (AAAI, 2022)

MTGnet: Multi-Task Spatiotemporal Graph Convolutional Networks for Air Quality Prediction. - Dan Lu 004; Rui Chen 012; Shanshan Sui; Qilong Han; Linglong Kong; Yichen Wang (IJCNN, 2022)

TAG: Toward Accurate Social Media Content Tagging with a Concept Graph. - Jiuding Yang; Weidong Guo; Bang Liu; Yakun Yu; Chaoyue Wang; Jinwen Luo; Linglong Kong; Di Niu; Zhen Wen (KDD, 2022)

Identification, Amplification and Measurement: A bridge to Gaussian Differential Privacy. - Yi Liu 062; Ke Sun 013; Bei Jiang; Linglong Kong (NeurIPS, 2022)

Conformalized Fairness via Quantile Regression. - Meichen Liu; Lei Ding 013; Dengdeng Yu; Wulong Liu; Linglong Kong; Bei Jiang (NeurIPS, 2022)

Distributional Reinforcement Learning via Sinkhorn Iterations. - Ke Sun 013; Yingnan Zhao; Yi Liu 062; Bei Jiang; Linglong Kong (CoRR, 2022)

How Does Value Distribution in Distributional Reinforcement Learning Help Optimization? - Ke Sun 013; Bei Jiang; Linglong Kong (CoRR, 2022)

Flexible quantile contour estimation for multivariate functional data: Beyond convexity. - Gaurav Agarwal; Wei Tu; Ying Sun 002; Linglong Kong (Comput. Stat. Data Anal., 2022)

Associations between Longitudinal Gestational Weight Gain and Scalar Infant Birth Weight: A Bayesian Joint Modeling Approach. - Matthew Pietrosanu; Linglong Kong; Yan Yuan; Rhonda C. Bell; Nicole Letourneau; Bei Jiang (Entropy, 2022)

Balancing Gender Bias in Job Advertisements With Text-Level Bias Mitigation. - Shenggang Hu; Jabir Alshehabi Al-Ani; Karen D. Hughes; Nicole Denier; Alla Konnikov; Lei Ding 013; Jinhan Xie; Yang Hu; Monideepa Tarafdar; Bei Jiang; Linglong Kong; Hongsheng Dai (Frontiers Big Data, 2022)

Factorizing Historical User Actions for Next-Day Purchase Prediction. - Bang Liu; Hanlin Zhang; Linglong Kong; Di Niu (ACM Trans. Web, 2022)

Opposite Online Learning via Sequentially Integrated Stochastic Gradient Descent Estimators. - Wenhai Cui; Xiaoting Ji; Linglong Kong; Xiaodong Yan (AAAI, 2023)

Online Local Differential Private Quantile Inference via Self-normalization. - Yi Liu; Qirui Hu; Lei Ding 013; Linglong Kong (ICML, 2023)

Explaining Anatomical Shape Variability: Supervised Disentangling with A Variational Graph Autoencoder. - Johannes Kiechle; Dylan Miller; Jordan Slessor; Matthew Pietrosanu; Linglong Kong; Christian Beaulieu; Dana Cobzas (ISBI, 2023)

Gaussian Differential Privacy on Riemannian Manifolds. - Yangdi Jiang; Xiaotian Chang; Yi Liu; Lei Ding 013; Linglong Kong; Bei Jiang (NeurIPS, 2023)

Exploring the Training Robustness of Distributional Reinforcement Learning Against Noisy State Observations. - Ke Sun 013; Yingnan Zhao; Shangling Jui; Linglong Kong (ECML/PKDD (5), 2023)

Optimal Smooth Approximation for Quantile Matrix Factorization. - Peng Liu 048; Yi Liu 062; Rui Zhu 007; Linglong Kong; Bei Jiang; Di Niu (SDM, 2023)

Mathematical Challenges in Deep Learning. - Vahid Partovi Nia; Guojun Zhang; Ivan Kobyzev; Michael R. Metel; Xinlin Li; Ke Sun 013; Sobhan Hemati; Masoud Asgharian; Linglong Kong; Wulong Liu; Boxing Chen (CoRR, 2023)

Estimation for partial functional partially linear additive model. - Qingguo Tang; Wei Tu; Linglong Kong (Comput. Stat. Data Anal., 2023)

A Reproducing Kernel Hilbert Space Framework for Functional Classification. - Peijun Sang; Adam B. Kashlak; Linglong Kong (J. Comput. Graph. Stat., 2023)

Gaussian copula function-on-scalar regression in reproducing kernel Hilbert space. - Haihan Xie; Linglong Kong (J. Multivar. Anal., 2023)

Algorithmic generalization ability of PALM for double sparse regularized regression. - Mei Li; Lingchen Kong; Bo Pan; Linglong Kong (Appl. Intell., 2023)

Analysis of Differentially Private Synthetic Data: A Measurement Error Approach. - Yangdi Jiang; Yi Liu; Xiaodong Yan; Anne-Sophie Charest; Linglong Kong; Bei Jiang (AAAI, 2024)

Responsible Bandit Learning via Privacy-Protected Mean-Volatility Utility. - Shanshan Zhao; Wenhai Cui; Bei Jiang; Linglong Kong; Xiaodong Yan (AAAI, 2024)

Tuning-free Estimation and Inference of Cumulative Distribution Function under Local Differential Privacy. - Yi Liu; Qirui Hu; Linglong Kong (ICML, 2024)

Sample Average Approximation for Conditional Stochastic Optimization with Dependent Data. - Yafei Wang; Bo Pan; Mei Li; Jianya Lu; Lingchen Kong; Bei Jiang; Linglong Kong (ICML, 2024)

Debiasing with Sufficient Projection: A General Theoretical Framework for Vector Representations. - Enze Shi; Lei Ding 013; Linglong Kong; Bei Jiang (NAACL-HLT, 2024)

Probing Social Bias in Labor Market Text Generation by ChatGPT: A Masked Language Model Approach. - Lei Ding 013; Yang Hu; Nicole Denier; Enze Shi; Junxi Zhang; Qirui Hu; Karen D. Hughes; Linglong Kong; Bei Jiang (NeurIPS, 2024)

Distributional Reinforcement Learning with Regularized Wasserstein Loss. - Ke Sun 013; Yingnan Zhao; Wulong Liu; Bei Jiang; Linglong Kong (NeurIPS, 2024)

Evaluation of OpenAI o1: Opportunities and Challenges of AGI. - Tianyang Zhong; Zhengliang Liu; Yi Pan 001; Yutong Zhang; Yifan Zhou; Shizhe Liang; Zihao Wu 001; Yanjun Lyu; Peng Shu; Xiaowei Yu; Chao Cao; Hanqi Jiang; et al. (CoRR, 2024)

Predicting Bitcoin Market Trends with Enhanced Technical Indicator Integration and Classification Models. - Abdelatif Hafid; Mohamed Rahouti; Linglong Kong; Maad Ebrahim; Mohamed Adel Serhani (CoRR, 2024)

FedSTaS: Client Stratification and Client Level Sampling for Efficient Federated Learning. - Jordan Slessor; Dezheng Kong 004; Xiaofen Tang; Zheng En Than; Linglong Kong (CoRR, 2024)

Statistical Undersampling with Mutual Information and Support Points. - Alex Mak; Shubham Sahoo; Shivani Pandey; Yidan Yue; Linglong Kong (CoRR, 2024)

A Distance-based Anomaly Detection Framework for Deep Reinforcement Learning. - Hongming Zhang 003; Ke Sun 013; Bo Xu 002; Linglong Kong; Martin Müller 003 (Trans. Mach. Learn. Res., 2024)

Fast Fusion Clustering via Double Random Projection. - Hongni Wang; Na Li; Yanqiu Zhou; Jingxin Yan; Bei Jiang; Linglong Kong; Xiaodong Yan (Entropy, 2024)

Wavelet-based Bayesian approximate kernel method for high-dimensional data analysis. - Wenxing Guo; Xueying Zhang; Bei Jiang; Linglong Kong; Yaozhong Hu (Comput. Stat., 2024)

Advancing Fairness in Precision Medicine: A Universal Framework for Optimal Treatment Estimation in Censored Data. - Hongni Wang; Junxi Zhang; Na Li; Linglong Kong; Bei Jiang; Xiaodong Yan (AISTATS, 2025)

CBMA: Improving Conformal Prediction through Bayesian Model Averaging. - Pankaj Bhagwat; Linglong Kong; Bei Jiang (ICLR, 2025)

A Consensus Privacy Metrics Framework for Synthetic Data. - Lisa Pilgram; Fida K. Dankar; Jörg Drechsler; Mark Elliot; Josep Domingo-Ferrer; Paul Francis; Murat Kantarcioglu; Linglong Kong; Bradley A. Malin; Krishnamurty Muralidhar; Puja Myles; Fabian Prasser; et al. (CoRR, 2025)

A Deep Bayesian Nonparametric Framework for Robust Mutual Information Estimation. - Forough Fazeli-Asl; Michael Minyi Zhang; Bei Jiang; Linglong Kong (CoRR, 2025)

Online federated learning framework for classification. - Wenxing Guo; Jinhan Xie; Jianya Lu; Bei Jiang; Hongsheng Dai; Linglong Kong (CoRR, 2025)

Deep Fair Learning: A Unified Framework for Fine-tuning Representations with Sufficient Networks. - Enze Shi; Linglong Kong; Bei Jiang (CoRR, 2025)

Distribution-on-Scalar Single-Index Quantile Regression Model for Handling Tumor Heterogeneity. - Xingcai Zhou; Shengxian Ding; Jiangyan Wang; Rongjie Liu; Linglong Kong; Chao Huang 005 (Technometrics, 2025)

Non-Asymptotic Analysis of Online Local Private Learning with SGD. - Enze Shi; Jinhan Xie; Bei Jiang; Linglong Kong; Xuming He (CoRR, 2025)

A Bayesian hierarchical model for orthogonal Tucker decomposition with oblivious tensor compression. - Matthew Pietrosanu; Bei Jiang; Linglong Kong (Knowl. Inf. Syst., 2025)