Linglong Kong

According to our database, Linglong Kong authored at least 56 papers between 2010 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Responsible Bandit Learning via Privacy-Protected Mean-Volatility Utility.

Shanshan Zhao, Wenhai Cui, Bei Jiang, Linglong Kong, Xiaodong Yan

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

Analysis of Differentially Private Synthetic Data: A Measurement Error Approach.

Yangdi Jiang, Yi Liu, Xiaodong Yan, Anne-Sophie Charest, Linglong Kong, Bei Jiang

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Algorithmic generalization ability of PALM for double sparse regularized regression.

Mei Li, Lingchen Kong, Bo Pan, Linglong Kong

Appl. Intell., December, 2023

Gaussian copula function-on-scalar regression in reproducing kernel Hilbert space.

Haihan Xie, Linglong Kong

J. Multivar. Anal., 2023

Estimation for partial functional partially linear additive model.

Qingguo Tang, Wei Tu, Linglong Kong

Comput. Stat. Data Anal., 2023

Mathematical Challenges in Deep Learning.

Vahid Partovi Nia, Guojun Zhang, Ivan Kobyzev, Michael R. Metel, Xinlin Li, Ke Sun, Sobhan Hemati, Masoud Asgharian, Linglong Kong, Wulong Liu, Boxing Chen

CoRR, 2023

Optimal Smooth Approximation for Quantile Matrix Factorization.

Peng Liu, Yi Liu, Rui Zhu, Linglong Kong, Bei Jiang, Di Niu

Proceedings of the 2023 SIAM International Conference on Data Mining, 2023

Exploring the Training Robustness of Distributional Reinforcement Learning Against Noisy State Observations.

Ke Sun, Yingnan Zhao, Shangling Jui, Linglong Kong

Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

Gaussian Differential Privacy on Riemannian Manifolds.

Yangdi Jiang, Xiaotian Chang, Yi Liu, Lei Ding, Linglong Kong, Bei Jiang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 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

Proceedings of the 20th IEEE International Symposium on Biomedical Imaging, 2023

Online Local Differential Private Quantile Inference via Self-normalization.

Yi Liu, Qirui Hu, Lei Ding, Linglong Kong

Proceedings of the International Conference on Machine Learning, 2023

Opposite Online Learning via Sequentially Integrated Stochastic Gradient Descent Estimators.

Wenhai Cui, Xiaoting Ji, Linglong Kong, Xiaodong Yan

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Factorizing Historical User Actions for Next-Day Purchase Prediction.

Bang Liu, Hanlin Zhang, Linglong Kong, Di Niu

ACM Trans. Web, 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, Jinhan Xie, Yang Hu, Monideepa Tarafdar, Bei Jiang, Linglong Kong, Hongsheng Dai

Frontiers Big Data, 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

Flexible quantile contour estimation for multivariate functional data: Beyond convexity.

Gaurav Agarwal, Wei Tu, Ying Sun, Linglong Kong

Comput. Stat. Data Anal., 2022

How Does Value Distribution in Distributional Reinforcement Learning Help Optimization?

Ke Sun, Bei Jiang, Linglong Kong

CoRR, 2022

Distributional Reinforcement Learning via Sinkhorn Iterations.

Ke Sun, Yingnan Zhao, Yi Liu, Bei Jiang, Linglong Kong

CoRR, 2022

Conformalized Fairness via Quantile Regression.

Meichen Liu, Lei Ding, Dengdeng Yu, Wulong Liu, Linglong Kong, Bei Jiang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Identification, Amplification and Measurement: A bridge to Gaussian Differential Privacy.

Yi Liu, Ke Sun, Bei Jiang, Linglong Kong

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 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

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

MTGnet: Multi-Task Spatiotemporal Graph Convolutional Networks for Air Quality Prediction.

Dan Lu, Rui Chen, Shanshan Sui, Qilong Han, Linglong Kong, Yichen Wang

Proceedings of the International Joint Conference on Neural Networks, 2022

Sample Average Approximation for Stochastic Optimization with Dependent Data: Performance Guarantees and Tractability.

Yafei Wang, Bo Pan, Wei Tu, Peng Liu, Bei Jiang, Chao Gao, Wei Lu, Shangling Jui, Linglong Kong

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

Word Embeddings via Causal Inference: Gender Bias Reducing and Semantic Information Preserving.

Lei Ding, Dengdeng Yu, Jinhan Xie, Wenxing Guo, Shenggang Hu, Meichen Liu, Linglong Kong, Hongsheng Dai, Yanchun Bao, Bei Jiang

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Testing independence of functional variables by angle covariance.

Tingyu Lai, Zhongzhan Zhang, Yafei Wang, Linglong Kong

J. Multivar. Anal., 2021

Advanced algorithms for penalized quantile and composite quantile regression.

Matthew Pietrosanu, Jueyu Gao, Linglong Kong, Bei Jiang, Di Niu

Comput. Stat., 2021

Towards Understanding Distributional Reinforcement Learning: Regularization, Optimization, Acceleration and Sinkhorn Algorithm.

Ke Sun, Yingnan Zhao, Yi Liu, Enze Shi, Yafei Wang, Aref Sadeghi, Xiaodong Yan, Bei Jiang, Linglong Kong

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, Shuo Lian, Shangling Jui, Di Niu

CoRR, 2021

A Simple Unified Framework for Anomaly Detection in Deep Reinforcement Learning.

Hongming Zhang, Ke Sun, Bo Xu, Linglong Kong, Martin Müller

CoRR, 2021

Exploring the Robustness of Distributional Reinforcement Learning against Noisy State Observations.

Ke Sun, Yi Liu, Yingnan Zhao, Hengshuai Yao, Shangling Jui, Linglong Kong

CoRR, 2021

Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization.

Ke Sun, Yafei Wang, Yi Liu, Yingnan Zhao, Bo Pan, Shangling Jui, Bei Jiang, Linglong Kong

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

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, Shuo Lian, Shangling Jui, Di Niu

Proceedings of the CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1, 2021

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

Sparse Multicategory Generalized Distance Weighted Discrimination in Ultra-High Dimensions.

Tong Su, Yafei Wang, Yi Liu, William G. Branton, Eugene Asahchop, Christopher Power, Bei Jiang, Linglong Kong, Niansheng Tang

Entropy, 2020

2019

Sparse wavelet estimation in quantile regression with multiple functional predictors.

Dengdeng Yu, Li Zhang, Ivan Mizera, Bei Jiang, Linglong Kong

Comput. Stat. Data Anal., 2019

Learning Privately over Distributed Features: An ADMM Sharing Approach.

Yaochen Hu, Peng Liu, Linglong Kong, Di Niu

CoRR, 2019

Distributional Reinforcement Learning for Efficient Exploration.

Borislav Mavrin, Shangtong Zhang, Hengshuai Yao, Linglong Kong, Kaiwen Wu, Yaoliang Yu

CoRR, 2019

Deep Reinforcement Learning with Decorrelation.

Borislav Mavrin, Hengshuai Yao, Linglong Kong

CoRR, 2019

Ensemble-based Ultrahigh-dimensional Variable Screening.

Wei Tu, Dong Yang, Linglong Kong, Menglu Che, Qian Shi, Guodong Li, Guangjian Tian

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Distributional Reinforcement Learning for Efficient Exploration.

Borislav Mavrin, Hengshuai Yao, Linglong Kong, Kaiwen Wu, Yaoliang Yu

Proceedings of the 36th International Conference on Machine Learning, 2019

M-estimation in Low-Rank Matrix Factorization: A General Framework.

Wei Tu, Peng Liu, Jingyu Zhao, Yi Liu, Linglong Kong, Guodong Li, Bei Jiang, Guangjian Tian, Hengshuai Yao

Proceedings of the 2019 IEEE International Conference on Data Mining, 2019

Exploration in the Face of Parametric and Intrinsic Uncertainties.

Borislav Mavrin, Shangtong Zhang, Hengshuai Yao, Linglong Kong

Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 2019

2018

Significant Anatomy Detection Through Sparse Classification: A Comparative Study.

Li Zhang, Dana Cobzas, Alan H. Wilman, Linglong Kong

IEEE Trans. Medical Imaging, 2018

QUOTA: The Quantile Option Architecture for Reinforcement Learning.

Shangtong Zhang, Borislav Mavrin, Linglong Kong, Bo Liu, Hengshuai Yao

CoRR, 2018

2017

An Unbiased Penalty for Sparse Classification with Application to Neuroimaging Data.

Li Zhang, Dana Cobzas, Alan H. Wilman, Linglong Kong

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2017, 2017

Recover Fine-Grained Spatial Data from Coarse Aggregation.

Bang Liu, Borislav Mavrin, Linglong Kong, Di Niu

Proceedings of the 2017 IEEE International Conference on Data Mining, 2017

Growing Story Forest Online from Massive Breaking News.

Bang Liu, Di Niu, Kunfeng Lai, Linglong Kong, Yu Xu

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017

Expectile Matrix Factorization for Skewed Data Analysis.

Rui Zhu, Di Niu, Linglong Kong, Zongpeng Li

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

Partial functional linear quantile regression for neuroimaging data analysis.

Dengdeng Yu, Linglong Kong, Ivan Mizera

Neurocomputing, 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

Local Region Sparse Learning for Image-on-Scalar Regression.

Yao Chen, Xiao Wang, Linglong Kong, Hongtu Zhu

CoRR, 2016

House Price Modeling over Heterogeneous Regions with Hierarchical Spatial Functional Analysis.

Bang Liu, Borislav Mavrin, Di Niu, Linglong Kong

Proceedings of the IEEE 16th International Conference on Data Mining, 2016

2015

Functional Nonlinear Mixed Effects Models for Longitudinal Image Data.

Xinchao Luo, Lixing Zhu, Linglong Kong, Hongtu Zhu

Proceedings of the Information Processing in Medical Imaging, 2015

2011

FADTTS: Functional analysis of diffusion tensor tract statistics.

Hongtu Zhu, Linglong Kong, Runze Li, Martin Styner, Guido Gerig, Weili Lin, John H. Gilmore

NeuroImage, 2011

2010

Smooth depth contours characterize the underlying distribution.

Linglong Kong, Yijun Zuo

J. Multivar. Anal., 2010

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

Proceedings of the Medical Image Computing and Computer-Assisted Intervention, 2010