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2020 – today
- 2023
[j15]Qingguo Tang, Wei Tu, Linglong Kong:
Estimation for partial functional partially linear additive model. Comput. Stat. Data Anal. 177: 107584 (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) - [c17]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 - [c16]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 - [c15]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 - [c14]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 - [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
- [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) - [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 - [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) - 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)
2010 – 2019
- 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) - [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 - [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
- [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) - [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) - 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
- [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) - [c3]Bang Liu, Borislav Mavrin, Di Niu, Linglong Kong:
House Price Modeling over Heterogeneous Regions with Hierarchical Spatial Functional Analysis. ICDM 2016: 1047-1052 - [i1]Yao Chen, Xiao Wang, Linglong Kong, Hongtu Zhu:
Local Region Sparse Learning for Image-on-Scalar Regression. CoRR abs/1605.08501 (2016) - 2015
- [c2]Xinchao Luo, Lixing Zhu, Linglong Kong, Hongtu Zhu:
Functional Nonlinear Mixed Effects Models for Longitudinal Image Data. IPMI 2015: 794-805 - 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) - [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
Coauthor Index
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last updated on 2023-03-25 01:23 CET by the dblp team
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