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Journal Articles
- 2024
[j20]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): 2323-2341 (2024)- [j19]Hongni Wang, Na Li, Yanqiu Zhou, Jingxin Yan, Bei Jiang, Linglong Kong, Xiaodong Yan:
Fast Fusion Clustering via Double Random Projection. Entropy 26(5): 376 (2024) - 2023
- [j18]Mei Li, Lingchen Kong, Bo Pan, Linglong Kong:
Algorithmic generalization ability of PALM for double sparse regularized regression. Appl. Intell. 53(24): 30566-30579 (2023) - [j17]Qingguo Tang, Wei Tu, Linglong Kong:
Estimation for partial functional partially linear additive model. Comput. Stat. Data Anal. 177: 107584 (2023) - [j16]Peijun Sang, Adam B. Kashlak, Linglong Kong:
A Reproducing Kernel Hilbert Space Framework for Functional Classification. J. Comput. Graph. Stat. 32(3): 1000-1008 (2023) - [j15]Haihan Xie, Linglong Kong:
Gaussian copula function-on-scalar regression in reproducing kernel Hilbert space. J. Multivar. Anal. 198: 105226 (2023) - 2022
- [j14]Gaurav Agarwal, Wei Tu, Ying Sun, Linglong Kong:
Flexible quantile contour estimation for multivariate functional data: Beyond convexity. Comput. Stat. Data Anal. 168: 107400 (2022) - [j13]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): 232 (2022) - [j12]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: 805713 (2022) - [j11]Bang Liu, Hanlin Zhang, Linglong Kong, Di Niu:
Factorizing Historical User Actions for Next-Day Purchase Prediction. ACM Trans. Web 16(1): 1:1-1:26 (2022) - 2021
- [j10]Matthew Pietrosanu, Jueyu Gao, Linglong Kong, Bei Jiang, Di Niu:
Advanced algorithms for penalized quantile and composite quantile regression. Comput. Stat. 36(1): 333-346 (2021) - [j9]Tingyu Lai, Zhongzhan Zhang, Yafei Wang, Linglong Kong:
Testing independence of functional variables by angle covariance. J. Multivar. Anal. 182: 104711 (2021) - 2020
- [j8]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): 1257 (2020) - [j7]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): 31:1-31:28 (2020) - 2019
- [j6]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: 12-29 (2019) - 2018
- [j5]Li Zhang, Dana Cobzas, Alan H. Wilman, Linglong Kong:
Significant Anatomy Detection Through Sparse Classification: A Comparative Study. IEEE Trans. Medical Imaging 37(1): 128-137 (2018) - 2016
- [j4]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: 222-239 (2016) - [j3]Dengdeng Yu, Linglong Kong, Ivan Mizera:
Partial functional linear quantile regression for neuroimaging data analysis. Neurocomputing 195: 74-87 (2016) - 2011
- [j2]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): 1412-1425 (2011) - 2010
- [j1]Linglong Kong, Yijun Zuo:
Smooth depth contours characterize the underlying distribution. J. Multivar. Anal. 101(9): 2222-2226 (2010)
Conference and Workshop Papers
- 2024
- [c30]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: 21206-21213 - [c29]Shanshan Zhao, Wenhai Cui, Bei Jiang, Linglong Kong, Xiaodong Yan:
Responsible Bandit Learning via Privacy-Protected Mean-Volatility Utility. AAAI 2024: 21815-21822 - [c28]Yi Liu, Qirui Hu, Linglong Kong:
Tuning-free Estimation and Inference of Cumulative Distribution Function under Local Differential Privacy. ICML 2024 - [c27]Yafei Wang, Bo Pan, Mei Li, Jianya Lu, Lingchen Kong, Bei Jiang, Linglong Kong:
Sample Average Approximation for Conditional Stochastic Optimization with Dependent Data. ICML 2024 - [c26]Enze Shi, Lei Ding, Linglong Kong, Bei Jiang:
Debiasing with Sufficient Projection: A General Theoretical Framework for Vector Representations. NAACL-HLT 2024: 5960-5975 - 2023
- [c25]Wenhai Cui, Xiaoting Ji, Linglong Kong, Xiaodong Yan:
Opposite Online Learning via Sequentially Integrated Stochastic Gradient Descent Estimators. AAAI 2023: 7270-7278 - [c24]Yi Liu, Qirui Hu, Lei Ding, Linglong Kong:
Online Local Differential Private Quantile Inference via Self-normalization. ICML 2023: 21698-21714 - [c23]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: 1-5 - [c22]Yangdi Jiang, Xiaotian Chang, Yi Liu, Lei Ding, Linglong Kong, Bei Jiang:
Gaussian Differential Privacy on Riemannian Manifolds. NeurIPS 2023 - [c21]Ke Sun, Yingnan Zhao, Shangling Jui, Linglong Kong:
Exploring the Training Robustness of Distributional Reinforcement Learning Against Noisy State Observations. ECML/PKDD (5) 2023: 36-51 - [c20]Peng Liu, Yi Liu, Rui Zhu, Linglong Kong, Bei Jiang, Di Niu:
Optimal Smooth Approximation for Quantile Matrix Factorization. SDM 2023: 595-603 - 2022
- [c19]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: 3859-3867 - [c18]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: 11864-11872 - [c17]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: 1-8 - [c16]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: 4332-4341 - [c15]Yi Liu, Ke Sun, Bei Jiang, Linglong Kong:
Identification, Amplification and Measurement: A bridge to Gaussian Differential Privacy. NeurIPS 2022 - [c14]Meichen Liu, Lei Ding, Dengdeng Yu, Wulong Liu, Linglong Kong, Bei Jiang:
Conformalized Fairness via Quantile Regression. NeurIPS 2022 - 2021
- [c13]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: 1284-1293 - [c12]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: 3732-3743 - 2019
- [c11]Borislav Mavrin, Shangtong Zhang, Hengshuai Yao, Linglong Kong:
Exploration in the Face of Parametric and Intrinsic Uncertainties. AAMAS 2019: 2117-2119 - [c10]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: 568-577 - [c9]Borislav Mavrin, Hengshuai Yao, Linglong Kong, Kaiwen Wu, Yaoliang Yu:
Distributional Reinforcement Learning for Efficient Exploration. ICML 2019: 4424-4434 - [c8]Wei Tu, Dong Yang, Linglong Kong, Menglu Che, Qian Shi, Guodong Li, Guangjian Tian:
Ensemble-based Ultrahigh-dimensional Variable Screening. IJCAI 2019: 3613-3619 - 2017
- [c7]Rui Zhu, Di Niu, Linglong Kong, Zongpeng Li:
Expectile Matrix Factorization for Skewed Data Analysis. AAAI 2017: 259-266 - [c6]Bang Liu, Di Niu, Kunfeng Lai, Linglong Kong, Yu Xu:
Growing Story Forest Online from Massive Breaking News. CIKM 2017: 777-785 - [c5]Bang Liu, Borislav Mavrin, Linglong Kong, Di Niu:
Recover Fine-Grained Spatial Data from Coarse Aggregation. ICDM 2017: 961-966 - [c4]Li Zhang, Dana Cobzas, Alan H. Wilman, Linglong Kong:
An Unbiased Penalty for Sparse Classification with Application to Neuroimaging Data. MICCAI (3) 2017: 55-63 - 2016
- [c3]Bang Liu, Borislav Mavrin, Di Niu, Linglong Kong:
House Price Modeling over Heterogeneous Regions with Hierarchical Spatial Functional Analysis. ICDM 2016: 1047-1052 - 2015
- [c2]Xinchao Luo, Lixing Zhu, Linglong Kong, Hongtu Zhu:
Functional Nonlinear Mixed Effects Models for Longitudinal Image Data. IPMI 2015: 794-805 - 2010
- [c1]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: 690-697
Informal and Other Publications
- 2024
- [i22]Tianyang Zhong, Zhengliang Liu, Yi Pan, Yutong Zhang, Yifan Zhou, Shizhe Liang, Zihao Wu, Yanjun Lyu, Peng Shu, Xiaowei Yu, Chao Cao, Hanqi Jiang, Hanxu Chen, Yiwei Li, Junhao Chen, Huawen Hu, Yihen Liu, Huaqin Zhao, Shaochen Xu, Haixing Dai, Lin Zhao, Ruidong Zhang, Wei Zhao, Zhenyuan Yang, Jingyuan Chen, Peilong Wang, Wei Ruan, Hui Wang, Huan Zhao, Jing Zhang, Yiming Ren, Shihuan Qin, Tong Chen, Jiaxi Li, Arif Hassan Zidan, Afrar Jahin, Minheng Chen, Sichen Xia, Jason Holmes, Yan Zhuang, Jiaqi Wang, Bochen Xu, Weiran Xia, Jichao Yu, Kaibo Tang, Yaxuan Yang, Bolun Sun, Tao Yang, Guoyu Lu, Xianqiao Wang, Lilong Chai, He Li, Jin Lu, Lichao Sun, Xin Zhang, Bao Ge, Xintao Hu, Lian Zhang, Hua Zhou, Lu Zhang, Shu Zhang, Ninghao Liu, Bei Jiang, Linglong Kong, Zhen Xiang, Yudan Ren, Jun Liu, Xi Jiang, Yu Bao, Wei Zhang, Xiang Li, Gang Li, Wei Liu, Dinggang Shen, Andrea Sikora, Xiaoming Zhai, Dajiang Zhu, Tianming Liu:
Evaluation of OpenAI o1: Opportunities and Challenges of AGI. CoRR abs/2409.18486 (2024) - 2023
- [i21]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 abs/2303.15464 (2023) - [i20]Yangdi Jiang, Xiaotian Chang, Yi Liu, Lei Ding, Linglong Kong, Bei Jiang:
Gaussian Differential Privacy on Riemannian Manifolds. CoRR abs/2311.10101 (2023) - 2022
- [i19]Ke Sun, Yingnan Zhao, Yi Liu, Bei Jiang, Linglong Kong:
Distributional Reinforcement Learning via Sinkhorn Iterations. CoRR abs/2202.00769 (2022) - [i18]Ke Sun, Bei Jiang, Linglong Kong:
How Does Value Distribution in Distributional Reinforcement Learning Help Optimization? CoRR abs/2209.14513 (2022) - [i17]Meichen Liu, Lei Ding, Dengdeng Yu, Wulong Liu, Linglong Kong, Bei Jiang:
Conformalized Fairness via Quantile Regression. CoRR abs/2210.02015 (2022) - [i16]Yi Liu, Ke Sun, Linglong Kong, Bei Jiang:
Identification, Amplification and Measurement: A bridge to Gaussian Differential Privacy. CoRR abs/2210.09269 (2022) - 2021
- [i15]Ke Sun, Yi Liu, Yingnan Zhao, Hengshuai Yao, Shangling Jui, Linglong Kong:
Exploring the Robustness of Distributional Reinforcement Learning against Noisy State Observations. CoRR abs/2109.08776 (2021) - [i14]Hongming Zhang, Ke Sun, Bo Xu, Linglong Kong, Martin Müller:
A Simple Unified Framework for Anomaly Detection in Deep Reinforcement Learning. CoRR abs/2109.09889 (2021) - [i13]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 abs/2109.12425 (2021) - [i12]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 abs/2110.03155 (2021) - [i11]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 abs/2110.06892 (2021) - [i10]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 abs/2110.08896 (2021) - [i9]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 abs/2112.05194 (2021) - 2019
- [i8]Borislav Mavrin, Hengshuai Yao, Linglong Kong:
Deep Reinforcement Learning with Decorrelation. CoRR abs/1903.07765 (2019) - [i7]Borislav Mavrin, Shangtong Zhang, Hengshuai Yao, Linglong Kong, Kaiwen Wu, Yaoliang Yu:
Distributional Reinforcement Learning for Efficient Exploration. CoRR abs/1905.06125 (2019) - [i6]Yaochen Hu, Peng Liu, Linglong Kong, Di Niu:
Learning Privately over Distributed Features: An ADMM Sharing Approach. CoRR abs/1907.07735 (2019) - 2018
- [i5]Bang Liu, Di Niu, Kunfeng Lai, Linglong Kong, Yu Xu:
Growing Story Forest Online from Massive Breaking News. CoRR abs/1803.00189 (2018) - [i4]Bang Liu, Borislav Mavrin, Linglong Kong, Di Niu:
Recover Fine-Grained Spatial Data from Coarse Aggregation. CoRR abs/1803.00192 (2018) - [i3]Bang Liu, Borislav Mavrin, Di Niu, Linglong Kong:
House Price Modeling over Heterogeneous Regions with Hierarchical Spatial Functional Analysis. CoRR abs/1803.00919 (2018) - [i2]Shangtong Zhang, Borislav Mavrin, Linglong Kong, Bo Liu, Hengshuai Yao:
QUOTA: The Quantile Option Architecture for Reinforcement Learning. CoRR abs/1811.02073 (2018) - 2016
- [i1]Yao Chen, Xiao Wang, Linglong Kong, Hongtu Zhu:
Local Region Sparse Learning for Image-on-Scalar Regression. CoRR abs/1605.08501 (2016)
Coauthor Index
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