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Bei Jiang
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2020 – today
- 2024
- [j14]Xin Zeng, Fan-Fang Meng, Xin Li, Kai-Yang Zhong, Bei Jiang, Yi Li:
GHGPR-PPIS: A graph convolutional network for identifying protein-protein interaction site using heat kernel with Generalized PageRank techniques and edge self-attention feature processing block. Comput. Biol. Medicine 168: 107683 (2024) - [j13]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) - [j12]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) - [j11]Kai-Yang Zhong, Meng-Liang Wen, Fan-Fang Meng, Xin Li, Bei Jiang, Xin Zeng, Yi Li:
MMDTA: A Multimodal Deep Model for Drug-Target Affinity with a Hybrid Fusion Strategy. J. Chem. Inf. Model. 64(7): 2878-2888 (2024) - [c16]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 - [c15]Shanshan Zhao, Wenhai Cui, Bei Jiang, Linglong Kong, Xiaodong Yan:
Responsible Bandit Learning via Privacy-Protected Mean-Volatility Utility. AAAI 2024: 21815-21822 - [c14]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 - [c13]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
- [j10]Jun Lin, Pengfei Luo, Xinpei Duan, Wujun Zhang, Chao Ma, Tong Bu, Wanhan Su, Bei Jiang, Guoli Li, Xuming Zou, Ting Yu, Lei Liao, Xingqiang Liu:
Ultrahigh gain hot-electron tunneling transistor approaching the collection limit. Sci. China Inf. Sci. 66(6) (2023) - [c12]Xing Chen, Dongcui Diao, Hechang Chen, Hengshuai Yao, Haiyin Piao, Zhixiao Sun, Zhiwei Yang, Randy Goebel, Bei Jiang, Yi Chang:
The Sufficiency of Off-Policyness and Soft Clipping: PPO Is Still Insufficient according to an Off-Policy Measure. AAAI 2023: 7078-7086 - [c11]Yangdi Jiang, Xiaotian Chang, Yi Liu, Lei Ding, Linglong Kong, Bei Jiang:
Gaussian Differential Privacy on Riemannian Manifolds. NeurIPS 2023 - [c10]Peng Liu, Yi Liu, Rui Zhu, Linglong Kong, Bei Jiang, Di Niu:
Optimal Smooth Approximation for Quantile Matrix Factorization. SDM 2023: 595-603 - [i13]Yangdi Jiang, Xiaotian Chang, Yi Liu, Lei Ding, Linglong Kong, Bei Jiang:
Gaussian Differential Privacy on Riemannian Manifolds. CoRR abs/2311.10101 (2023) - 2022
- [j9]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) - [j8]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) - [j7]Yunyuan Huang, Jiqun Wang, Jiaqi Liu, Donglei Shi, Xiaokang Li, Manjiong Wang, Taotao Lu, Bei Jiang, Conglong Xia, Houwen Lin, Yixiang Xu, Jian Li:
Rapid Repurposing of Novel Combination Drugs for the Treatment of Heart Failure via a Computationally Guided Network Screening Approach. J. Chem. Inf. Model. 62(21): 5223-5232 (2022) - [c9]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 - [c8]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 - [c7]Yi Liu, Ke Sun, Bei Jiang, Linglong Kong:
Identification, Amplification and Measurement: A bridge to Gaussian Differential Privacy. NeurIPS 2022 - [c6]Meichen Liu, Lei Ding, Dengdeng Yu, Wulong Liu, Linglong Kong, Bei Jiang:
Conformalized Fairness via Quantile Regression. NeurIPS 2022 - [i12]Ke Sun, Yingnan Zhao, Yi Liu, Bei Jiang, Linglong Kong:
Distributional Reinforcement Learning via Sinkhorn Iterations. CoRR abs/2202.00769 (2022) - [i11]Xing Chen, Dongcui Diao, Hechang Chen, Hengshuai Yao, Jielong Yang, Haiyin Piao, Zhixiao Sun, Bei Jiang, Yi Chang:
Sigmoidally Preconditioned Off-policy Learning: a new exploration method for reinforcement learning. CoRR abs/2205.10047 (2022) - [i10]Ke Sun, Bei Jiang, Linglong Kong:
How Does Value Distribution in Distributional Reinforcement Learning Help Optimization? CoRR abs/2209.14513 (2022) - [i9]Meichen Liu, Lei Ding, Dengdeng Yu, Wulong Liu, Linglong Kong, Bei Jiang:
Conformalized Fairness via Quantile Regression. CoRR abs/2210.02015 (2022) - [i8]Yi Liu, Ke Sun, Linglong Kong, Bei Jiang:
Identification, Amplification and Measurement: A bridge to Gaussian Differential Privacy. CoRR abs/2210.09269 (2022) - [i7]Dongcui Diao, Hengshuai Yao, Bei Jiang:
Class Interference of Deep Neural Networks. CoRR abs/2211.01370 (2022) - 2021
- [j6]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) - [j5]Chenglin Li, Carrie Lu Tong, Di Niu, Bei Jiang, Xiao Zuo, Lei Cheng, Jian Xiong, Jianming Yang:
Similarity Embedding Networks for Robust Human Activity Recognition. ACM Trans. Knowl. Discov. Data 15(6): 98:1-98:17 (2021) - [c5]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 - [c4]Chenglin Li, Di Niu, Bei Jiang, Xiao Zuo, Jianming Yang:
Meta-HAR: Federated Representation Learning for Human Activity Recognition. WWW 2021: 912-922 - [i6]Chenglin Li, Di Niu, Bei Jiang, Xiao Zuo, Jianming Yang:
Meta-HAR: Federated Representation Learning for Human Activity Recognition. CoRR abs/2106.00615 (2021) - [i5]Chenglin Li, Carrie Lu Tong, Di Niu, Bei Jiang, Xiao Zuo, Lei Cheng, Jian Xiong, Jianming Yang:
Similarity Embedding Networks for Robust Human Activity Recognition. CoRR abs/2106.15283 (2021) - [i4]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) - [i3]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) - [i2]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
- [j4]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)
2010 – 2019
- 2019
- [j3]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) - [c3]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 - 2018
- [j2]Wanzeng Kong, Bei Jiang, Qiaonan Fan, Li Zhu, Xuehui Wei:
Personal Identification Based on Brain Networks of EEG Signals. Int. J. Appl. Math. Comput. Sci. 28(4): 745-757 (2018) - [i1]Donglai Zhu, Hengshuai Yao, Bei Jiang, Peng Yu:
Negative Log Likelihood Ratio Loss for Deep Neural Network Classification. CoRR abs/1804.10690 (2018) - 2017
- [j1]Wanzeng Kong, Zhanpeng Zhou, Bei Jiang, Fabio Babiloni, Gianluca Borghini:
Assessment of driving fatigue based on intra/inter-region phase synchronization. Neurocomputing 219: 474-482 (2017) - [c2]Wanzeng Kong, Qiaonan Fan, Luyun Wang, Bei Jiang, Yong Peng, Yanbin Zhang:
Task-Free Brainprint Recognition Based on Degree of Brain Networks. ICONIP (2) 2017: 709-717 - 2016
- [c1]Wanzeng Kong, Yan Liu, Bei Jiang, Guojun Dai, Lin Xu:
A New EEG Signal Processing Method Based on Low-Rank and Sparse Decomposition. ICCSIP 2016: 556-564
Coauthor Index
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last updated on 2024-10-07 21:21 CEST by the dblp team
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