Linglong Kong

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 65

Top Topics

Riemannian Manifolds

Reinforcement Learning

Quantile Regression

Information Preserving

Top Venues

Comput. Stat. Data Anal.

Publications

Shanshan Zhao, Wenhai Cui, Bei Jiang, Linglong Kong, Xiaodong Yan
Responsible Bandit Learning via Privacy-Protected Mean-Volatility Utility. AAAI (2024)
Wenxing Guo, Xueying Zhang, Bei Jiang, Linglong Kong, Yaozhong Hu
Wavelet-based Bayesian approximate kernel method for high-dimensional data analysis. Comput. Stat. 39 (4) (2024)
Yangdi Jiang, Yi Liu, Xiaodong Yan, Anne-Sophie Charest, Linglong Kong, Bei Jiang
Analysis of Differentially Private Synthetic Data: A Measurement Error Approach. AAAI (2024)
Hongni Wang, Na Li, Yanqiu Zhou, Jingxin Yan, Bei Jiang, Linglong Kong, Xiaodong Yan
Fast Fusion Clustering via Double Random Projection. Entropy 26 (5) (2024)
Enze Shi, Lei Ding, Linglong Kong, Bei Jiang
Debiasing with Sufficient Projection: A General Theoretical Framework for Vector Representations. NAACL-HLT (2024)
Vahid Partovi Nia, Guojun Zhang, Ivan Kobyzev, Michael R. Metel, Xinlin Li, Ke Sun, Sobhan Hemati, Masoud Asgharian, Linglong Kong, Wulong Liu, Boxing Chen
Mathematical Challenges in Deep Learning. CoRR (2023)
Haihan Xie, Linglong Kong
Gaussian copula function-on-scalar regression in reproducing kernel Hilbert space. J. Multivar. Anal. 198 (2023)
Peijun Sang, Adam B. Kashlak, Linglong Kong
A Reproducing Kernel Hilbert Space Framework for Functional Classification. J. Comput. Graph. Stat. 32 (3) (2023)
Johannes Kiechle, Dylan Miller, Jordan Slessor, Matthew Pietrosanu, Linglong Kong, Christian Beaulieu, Dana Cobzas
Explaining Anatomical Shape Variability: Supervised Disentangling with A Variational Graph Autoencoder. ISBI (2023)
Yangdi Jiang, Xiaotian Chang, Yi Liu, Lei Ding, Linglong Kong, Bei Jiang
Gaussian Differential Privacy on Riemannian Manifolds. CoRR (2023)
Yangdi Jiang, Xiaotian Chang, Yi Liu, Lei Ding, Linglong Kong, Bei Jiang
Gaussian Differential Privacy on Riemannian Manifolds. NeurIPS (2023)
Yi Liu, Qirui Hu, Lei Ding, Linglong Kong
Online Local Differential Private Quantile Inference via Self-normalization. ICML (2023)
Peng Liu, Yi Liu, Rui Zhu, Linglong Kong, Bei Jiang, Di Niu
Optimal Smooth Approximation for Quantile Matrix Factorization. SDM (2023)
Qingguo Tang, Wei Tu, Linglong Kong
Estimation for partial functional partially linear additive model. Comput. Stat. Data Anal. 177 (2023)
Ke Sun, Yingnan Zhao, Shangling Jui, Linglong Kong
Exploring the Training Robustness of Distributional Reinforcement Learning Against Noisy State Observations. ECML/PKDD (5) (2023)
Wenhai Cui, Xiaoting Ji, Linglong Kong, Xiaodong Yan
Opposite Online Learning via Sequentially Integrated Stochastic Gradient Descent Estimators. AAAI (2023)
Mei Li, Lingchen Kong, Bo Pan, Linglong Kong
Algorithmic generalization ability of PALM for double sparse regularized regression. Appl. Intell. 53 (24) (2023)
Ke Sun, Yingnan Zhao, Yi Liu, Bei Jiang, Linglong Kong
Distributional Reinforcement Learning via Sinkhorn Iterations. CoRR (2022)
Bang Liu, Hanlin Zhang, Linglong Kong, Di Niu
Factorizing Historical User Actions for Next-Day Purchase Prediction. ACM Trans. Web 16 (1) (2022)
Matthew Pietrosanu, Linglong Kong, Yan Yuan, Rhonda C. Bell, Nicole Letourneau, Bei Jiang
Associations between Longitudinal Gestational Weight Gain and Scalar Infant Birth Weight: A Bayesian Joint Modeling Approach. Entropy 24 (2) (2022)
Shenggang Hu, Jabir Alshehabi Al-Ani, Karen D. Hughes, Nicole Denier, Alla Konnikov, Lei Ding, Jinhan Xie, Yang Hu, Monideepa Tarafdar, Bei Jiang, Linglong Kong, Hongsheng Dai
Balancing Gender Bias in Job Advertisements With Text-Level Bias Mitigation. Frontiers Big Data 5 (2022)
Lei Ding, Dengdeng Yu, Jinhan Xie, Wenxing Guo, Shenggang Hu, Meichen Liu, Linglong Kong, Hongsheng Dai, Yanchun Bao, Bei Jiang
Word Embeddings via Causal Inference: Gender Bias Reducing and Semantic Information Preserving. AAAI (2022)
Meichen Liu, Lei Ding, Dengdeng Yu, Wulong Liu, Linglong Kong, Bei Jiang
Conformalized Fairness via Quantile Regression. NeurIPS (2022)
Yi Liu, Ke Sun, Bei Jiang, Linglong Kong
Identification, Amplification and Measurement: A bridge to Gaussian Differential Privacy. NeurIPS (2022)
Yi Liu, Ke Sun, Linglong Kong, Bei Jiang
Identification, Amplification and Measurement: A bridge to Gaussian Differential Privacy. CoRR (2022)
Jiuding Yang, Weidong Guo, Bang Liu, Yakun Yu, Chaoyue Wang, Jinwen Luo, Linglong Kong, Di Niu, Zhen Wen
TAG: Toward Accurate Social Media Content Tagging with a Concept Graph. KDD (2022)
Meichen Liu, Lei Ding, Dengdeng Yu, Wulong Liu, Linglong Kong, Bei Jiang
Conformalized Fairness via Quantile Regression. CoRR (2022)
Ke Sun, Bei Jiang, Linglong Kong
How Does Value Distribution in Distributional Reinforcement Learning Help Optimization? CoRR (2022)
Gaurav Agarwal, Wei Tu, Ying Sun, Linglong Kong
Flexible quantile contour estimation for multivariate functional data: Beyond convexity. Comput. Stat. Data Anal. 168 (2022)
Dan Lu, Rui Chen, Shanshan Sui, Qilong Han, Linglong Kong, Yichen Wang
MTGnet: Multi-Task Spatiotemporal Graph Convolutional Networks for Air Quality Prediction. IJCNN (2022)
Yafei Wang, Bo Pan, Wei Tu, Peng Liu, Bei Jiang, Chao Gao, Wei Lu, Shangling Jui, Linglong Kong
Sample Average Approximation for Stochastic Optimization with Dependent Data: Performance Guarantees and Tractability. AAAI (2022)
Keith G. Mills, Fred X. Han, Mohammad Salameh, Seyed Saeed Changiz Rezaei, Linglong Kong, Wei Lu, Shuo Lian, Shangling Jui, Di Niu
L2NAS: Learning to Optimize Neural Architectures via Continuous-Action Reinforcement Learning. CIKM (2021)
Ke Sun, Yafei Wang, Yi Liu, Yingnan Zhao, Bo Pan, Shangling Jui, Bei Jiang, Linglong Kong
Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization. CoRR (2021)
Ke Sun, Yafei Wang, Yi Liu, Yingnan Zhao, Bo Pan, Shangling Jui, Bei Jiang, Linglong Kong
Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization. NeurIPS (2021)
Ke Sun, Yi Liu, Yingnan Zhao, Hengshuai Yao, Shangling Jui, Linglong Kong
Exploring the Robustness of Distributional Reinforcement Learning against Noisy State Observations. CoRR (2021)
Matthew Pietrosanu, Jueyu Gao, Linglong Kong, Bei Jiang, Di Niu
Advanced algorithms for penalized quantile and composite quantile regression. Comput. Stat. 36 (1) (2021)
Jiuding Yang, Weidong Guo, Bang Liu, Yakun Yu, Chaoyue Wang, Jinwen Luo, Linglong Kong, Di Niu, Zhen Wen
TAG: Toward Accurate Social Media Content Tagging with a Concept Graph. CoRR (2021)
Hongming Zhang, Ke Sun, Bo Xu, Linglong Kong, Martin Müller
A Simple Unified Framework for Anomaly Detection in Deep Reinforcement Learning. CoRR (2021)
Lei Ding, Dengdeng Yu, Jinhan Xie, Wenxing Guo, Shenggang Hu, Meichen Liu, Linglong Kong, Hongsheng Dai, Yanchun Bao, Bei Jiang
Word Embeddings via Causal Inference: Gender Bias Reducing and Semantic Information Preserving. CoRR (2021)
Ke Sun, Yingnan Zhao, Yi Liu, Enze Shi, Yafei Wang, Aref Sadeghi, Xiaodong Yan, Bei Jiang, Linglong Kong
Towards Understanding Distributional Reinforcement Learning: Regularization, Optimization, Acceleration and Sinkhorn Algorithm. CoRR (2021)
Keith G. Mills, Fred X. Han, Mohammad Salameh, Seyed Saeed Changiz Rezaei, Linglong Kong, Wei Lu, Shuo Lian, Shangling Jui, Di Niu
L$^{2}$NAS: Learning to Optimize Neural Architectures via Continuous-Action Reinforcement Learning. CoRR (2021)
Tingyu Lai, Zhongzhan Zhang, Yafei Wang, Linglong Kong
Testing independence of functional variables by angle covariance. J. Multivar. Anal. 182 (2021)
Tong Su, Yafei Wang, Yi Liu, William G. Branton, Eugene Asahchop, Christopher Power, Bei Jiang, Linglong Kong, Niansheng Tang
Sparse Multicategory Generalized Distance Weighted Discrimination in Ultra-High Dimensions. Entropy 22 (11) (2020)
Bang Liu, Fred X. Han, Di Niu, Linglong Kong, Kunfeng Lai, Yu Xu
Story Forest: Extracting Events and Telling Stories from Breaking News. ACM Trans. Knowl. Discov. Data 14 (3) (2020)
Borislav Mavrin, Shangtong Zhang, Hengshuai Yao, Linglong Kong
Exploration in the Face of Parametric and Intrinsic Uncertainties. AAMAS (2019)
Wei Tu, Dong Yang, Linglong Kong, Menglu Che, Qian Shi, Guodong Li, Guangjian Tian
Ensemble-based Ultrahigh-dimensional Variable Screening. IJCAI (2019)
Borislav Mavrin, Shangtong Zhang, Hengshuai Yao, Linglong Kong, Kaiwen Wu, Yaoliang Yu
Distributional Reinforcement Learning for Efficient Exploration. CoRR (2019)
Borislav Mavrin, Hengshuai Yao, Linglong Kong, Kaiwen Wu, Yaoliang Yu
Distributional Reinforcement Learning for Efficient Exploration. ICML (2019)
Dengdeng Yu, Li Zhang, Ivan Mizera, Bei Jiang, Linglong Kong
Sparse wavelet estimation in quantile regression with multiple functional predictors. Comput. Stat. Data Anal. 136 (2019)
Wei Tu, Peng Liu, Jingyu Zhao, Yi Liu, Linglong Kong, Guodong Li, Bei Jiang, Guangjian Tian, Hengshuai Yao
M-estimation in Low-Rank Matrix Factorization: A General Framework. ICDM (2019)
Yaochen Hu, Peng Liu, Linglong Kong, Di Niu
Learning Privately over Distributed Features: An ADMM Sharing Approach. CoRR (2019)
Borislav Mavrin, Hengshuai Yao, Linglong Kong
Deep Reinforcement Learning with Decorrelation. CoRR (2019)
Bang Liu, Di Niu, Kunfeng Lai, Linglong Kong, Yu Xu
Growing Story Forest Online from Massive Breaking News. CoRR (2018)
Li Zhang, Dana Cobzas, Alan H. Wilman, Linglong Kong
Significant Anatomy Detection Through Sparse Classification: A Comparative Study. IEEE Trans. Medical Imaging 37 (1) (2018)
Shangtong Zhang, Borislav Mavrin, Linglong Kong, Bo Liu, Hengshuai Yao
QUOTA: The Quantile Option Architecture for Reinforcement Learning. CoRR (2018)
Bang Liu, Borislav Mavrin, Linglong Kong, Di Niu
Recover Fine-Grained Spatial Data from Coarse Aggregation. CoRR (2018)
Bang Liu, Borislav Mavrin, Di Niu, Linglong Kong
House Price Modeling over Heterogeneous Regions with Hierarchical Spatial Functional Analysis. CoRR (2018)
Li Zhang, Dana Cobzas, Alan H. Wilman, Linglong Kong
An Unbiased Penalty for Sparse Classification with Application to Neuroimaging Data. MICCAI (3) (2017)
Bang Liu, Borislav Mavrin, Linglong Kong, Di Niu
Recover Fine-Grained Spatial Data from Coarse Aggregation. ICDM (2017)
Bang Liu, Di Niu, Kunfeng Lai, Linglong Kong, Yu Xu
Growing Story Forest Online from Massive Breaking News. CIKM (2017)
Rui Zhu, Di Niu, Linglong Kong, Zongpeng Li
Expectile Matrix Factorization for Skewed Data Analysis. AAAI (2017)
Yao Chen, Xiao Wang, Linglong Kong, Hongtu Zhu
Local Region Sparse Learning for Image-on-Scalar Regression. CoRR (2016)
Qianchuan He, Linglong Kong, Yanhua Wang, Sijian Wang, Timothy A. Chan, Eric Holland
Regularized quantile regression under heterogeneous sparsity with application to quantitative genetic traits. Comput. Stat. Data Anal. 95 (2016)
Dengdeng Yu, Linglong Kong, Ivan Mizera
Partial functional linear quantile regression for neuroimaging data analysis. Neurocomputing 195 (2016)
Bang Liu, Borislav Mavrin, Di Niu, Linglong Kong
House Price Modeling over Heterogeneous Regions with Hierarchical Spatial Functional Analysis. ICDM (2016)
Xinchao Luo, Lixing Zhu, Linglong Kong, Hongtu Zhu
Functional Nonlinear Mixed Effects Models for Longitudinal Image Data. IPMI (2015)
Hongtu Zhu, Linglong Kong, Runze Li, Martin Styner, Guido Gerig, Weili Lin, John H. Gilmore
FADTTS: Functional analysis of diffusion tensor tract statistics. NeuroImage 56 (3) (2011)
Hongtu Zhu, Martin Styner, Yimei Li, Linglong Kong, Yundi Shi, Weili Lin, Christopher L. Coe, John H. Gilmore
Multivariate Varying Coefficient Models for DTI Tract Statistics. MICCAI (1) (2010)
Linglong Kong, Yijun Zuo
Smooth depth contours characterize the underlying distribution. J. Multivar. Anal. 101 (9) (2010)