Linglong Kong - RankMe

101 papers A* 24A 6B 1Journal 63Unranked 7

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Bayesian Bootstrap Framework for Mutual Information Neural Estimation: Bridging Classical Mutual Information Learning and Bayesian Nonparametric Learning. Trans. Mach. Learn. Res. Forough Fazeli-Asl, Michael Minyi Zhang, Linglong Kong, Bei Jiang
2026	J	`jnl`	Fast online ℓ0 elastic net subspace clustering via a novel dictionary update strategy. Pattern Recognit. Wentao Qu, Lingchen Kong, Linglong Kong, Bei Jiang
2026	J	`jnl`	FedFask: Fast Sketching Distributed PCA for Large-Scale Federated Data. IEEE Trans. Pattern Anal. Mach. Intell. Xingcai Zhou, Guang Yang, Haotian Zheng, Linglong Kong, Jinde Cao
2026	J	`jnl`	Phase Transition for Budgeted Multi-Agent Synergy. CoRR Bang Liu, Linglong Kong, Jian Pei
2026	J	`jnl`	Principled Fast and Meta Knowledge Learners for Continual Reinforcement Learning. CoRR Ke Sun, Hongming Zhang, Jun Jin, Chao Gao, Xi Chen, Wulong Liu, Linglong Kong
2026	J	`jnl`	Resolving data imbalance in transfer learning: a simple random sampling approach. Stat. Comput. Andrei Afilipoaei, Venkata Shreya Kala, Jainish Mehta, Ce Zhang, Bei Jiang, Linglong Kong
2025	J	`jnl`	A Bayesian hierarchical model for orthogonal Tucker decomposition with oblivious tensor compression. Knowl. Inf. Syst. Matthew Pietrosanu, Bei Jiang, Linglong Kong
2025	J	`jnl`	A Consensus Privacy Metrics Framework for Synthetic Data. CoRR Lisa Pilgram, Fida K. Dankar, Jörg Drechsler, Mark Elliot, Josep Domingo-Ferrer, Paul Francis, Murat Kantarcioglu, Linglong Kong, Bradley A. Malin, Krishnamurty Muralidhar, Puja Myles, Fabian Prasser, Jean Louis Raisaro, Chao Yan, Khaled El Emam
2025	J	`jnl`	A Deep Bayesian Nonparametric Framework for Robust Mutual Information Estimation. CoRR Forough Fazeli-Asl, Michael Minyi Zhang, Bei Jiang, Linglong Kong
2025	J	`jnl`	A consensus privacy metrics framework for synthetic data. Patterns Lisa Pilgram, Fida Kamal Dankar, Jörg Drechsler, Mark Elliot, Josep Domingo-Ferrer, Paul Francis, Murat Kantarcioglu, Linglong Kong, Bradley A. Malin, Krishnamurty Muralidhar, Puja Myles, Fabian Prasser, Jean Louis Raisaro, Chao Yan, Khaled El Emam
2025	—	`conf`	Adaptive Conformal Prediction Intervals for Invariant Learning. KDD (2) Shuxin Liang, Yihan Xiao, Linglong Kong, Wenlu Tang
2025	A	`conf`	Advancing Fairness in Precision Medicine: A Universal Framework for Optimal Treatment Estimation in Censored Data. AISTATS Hongni Wang, Junxi Zhang, Na Li, Linglong Kong, Bei Jiang, Xiaodong Yan
2025	A*	`conf`	CBMA: Improving Conformal Prediction through Bayesian Model Averaging. ICLR Pankaj Bhagwat, Linglong Kong, Bei Jiang
2025	J	`jnl`	Deep Fair Learning: A Unified Framework for Fine-tuning Representations with Sufficient Networks. CoRR Enze Shi, Linglong Kong, Bei Jiang
2025	A*	`conf`	Differentially Private Analysis for Binary Response Models: Optimality, Estimation, and Inference. ICML Ce Zhang, Yixin Han, Yafei Wang, Xiaodong Yan, Linglong Kong, Ting Li, Bei Jiang
2025	J	`jnl`	Distribution-on-Scalar Single-Index Quantile Regression Model for Handling Tumor Heterogeneity. Technometrics Xingcai Zhou, Shengxian Ding, Jiangyan Wang, Rongjie Liu, Linglong Kong, Chao Huang
2025	J	`jnl`	Non-Asymptotic Analysis of Online Local Private Learning with SGD. CoRR Enze Shi, Jinhan Xie, Bei Jiang, Linglong Kong, Xuming He
2025	A	`conf`	Oblivious Johnson-Lindenstrauss embeddings for compressed Tucker decompositions. CIKM Matthew Pietrosanu, Bei Jiang, Linglong Kong
2025	J	`jnl`	Online federated learning framework for classification. CoRR Wenxing Guo, Jinhan Xie, Jianya Lu, Bei Jiang, Hongsheng Dai, Linglong Kong
2025	J	`jnl`	Spilt conformal prediction with missing response. Stat. Comput. Zhimiao Cao, Ce Zhang, Beibei Lian, Bei Jiang, Linglong Kong, Xiaodong Yan
2025	J	`jnl`	Understanding Fairness and Prediction Error through Subspace Decomposition and Influence Analysis. CoRR Enze Shi, Pankaj Bhagwat, Zhixian Yang, Linglong Kong, Bei Jiang
2024	A*	`conf`	A Bayesian Hierarchical Model for Orthogonal Tucker Decomposition with Oblivious Tensor Compression. ICDM Matthew Pietrosanu, Bei Jiang, Linglong Kong
2024	J	`jnl`	A Distance-based Anomaly Detection Framework for Deep Reinforcement Learning. Trans. Mach. Learn. Res. Hongming Zhang, Ke Sun, Bo Xu, Linglong Kong, Martin Müller
2024	A*	`conf`	Analysis of Differentially Private Synthetic Data: A Measurement Error Approach. AAAI Yangdi Jiang, Yi Liu, Xiaodong Yan, Anne-Sophie Charest, Linglong Kong, Bei Jiang
2024	—	`conf`	Debiasing with Sufficient Projection: A General Theoretical Framework for Vector Representations. NAACL-HLT Enze Shi, Lei Ding, Linglong Kong, Bei Jiang
2024	A*	`conf`	Distributional Reinforcement Learning with Regularized Wasserstein Loss. NeurIPS Ke Sun, Yingnan Zhao, Wulong Liu, Bei Jiang, Linglong Kong
2024	J	`jnl`	Evaluation of OpenAI o1: Opportunities and Challenges of AGI. CoRR 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
2024	J	`jnl`	Fast Fusion Clustering via Double Random Projection. Entropy Hongni Wang, Na Li, Yanqiu Zhou, Jingxin Yan, Bei Jiang, Linglong Kong, Xiaodong Yan
2024	J	`jnl`	FedSTaS: Client Stratification and Client Level Sampling for Efficient Federated Learning. CoRR Jordan Slessor, Dezheng Kong, Xiaofen Tang, Zheng En Than, Linglong Kong
2024	J	`jnl`	Inference on High-dimensional Single-index Models with Streaming Data. J. Mach. Learn. Res. Dongxiao Han, Jinhan Xie, Jin Liu, Liuquan Sun, Jian Huang, Bei Jiang, Linglong Kong
2024	J	`jnl`	Predicting Bitcoin Market Trends with Enhanced Technical Indicator Integration and Classification Models. CoRR Abdelatif Hafid, Mohamed Rahouti, Linglong Kong, Maad Ebrahim, Mohamed Adel Serhani
2024	A*	`conf`	Probing Social Bias in Labor Market Text Generation by ChatGPT: A Masked Language Model Approach. NeurIPS Lei Ding, Yang Hu, Nicole Denier, Enze Shi, Junxi Zhang, Qirui Hu, Karen D. Hughes, Linglong Kong, Bei Jiang
2024	A*	`conf`	Responsible Bandit Learning via Privacy-Protected Mean-Volatility Utility. AAAI Shanshan Zhao, Wenhai Cui, Bei Jiang, Linglong Kong, Xiaodong Yan
2024	A*	`conf`	Sample Average Approximation for Conditional Stochastic Optimization with Dependent Data. ICML Yafei Wang, Bo Pan, Mei Li, Jianya Lu, Lingchen Kong, Bei Jiang, Linglong Kong
2024	J	`jnl`	Statistical Undersampling with Mutual Information and Support Points. CoRR Alex Mak, Shubham Sahoo, Shivani Pandey, Yidan Yue, Linglong Kong
2024	A*	`conf`	Tuning-free Estimation and Inference of Cumulative Distribution Function under Local Differential Privacy. ICML Yi Liu, Qirui Hu, Linglong Kong
2024	J	`jnl`	Wavelet-based Bayesian approximate kernel method for high-dimensional data analysis. Comput. Stat. Wenxing Guo, Xueying Zhang, Bei Jiang, Linglong Kong, Yaozhong Hu
2023	J	`jnl`	A Reproducing Kernel Hilbert Space Framework for Functional Classification. J. Comput. Graph. Stat. Peijun Sang, Adam B. Kashlak, Linglong Kong
2023	J	`jnl`	Algorithmic generalization ability of PALM for double sparse regularized regression. Appl. Intell. Mei Li, Lingchen Kong, Bo Pan, Linglong Kong
2023	J	`jnl`	Estimation for partial functional partially linear additive model. Comput. Stat. Data Anal. Qingguo Tang, Wei Tu, Linglong Kong
2023	—	`conf`	Explaining Anatomical Shape Variability: Supervised Disentangling with A Variational Graph Autoencoder. ISBI Johannes Kiechle, Dylan Miller, Jordan Slessor, Matthew Pietrosanu, Linglong Kong, Christian Beaulieu, Dana Cobzas
2023	—	`conf`	Exploring the Training Robustness of Distributional Reinforcement Learning Against Noisy State Observations. ECML/PKDD (5) Ke Sun, Yingnan Zhao, Shangling Jui, Linglong Kong
2023	A*	`conf`	Gaussian Differential Privacy on Riemannian Manifolds. NeurIPS Yangdi Jiang, Xiaotian Chang, Yi Liu, Lei Ding, Linglong Kong, Bei Jiang
2023	J	`jnl`	Gaussian Differential Privacy on Riemannian Manifolds. CoRR Yangdi Jiang, Xiaotian Chang, Yi Liu, Lei Ding, Linglong Kong, Bei Jiang
2023	J	`jnl`	Gaussian copula function-on-scalar regression in reproducing kernel Hilbert space. J. Multivar. Anal. Haihan Xie, Linglong Kong
2023	J	`jnl`	Mathematical Challenges in Deep Learning. CoRR Vahid Partovi Nia, Guojun Zhang, Ivan Kobyzev, Michael R. Metel, Xinlin Li, Ke Sun, Sobhan Hemati, Masoud Asgharian, Linglong Kong, Wulong Liu, Boxing Chen
2023	A*	`conf`	Online Local Differential Private Quantile Inference via Self-normalization. ICML Yi Liu, Qirui Hu, Lei Ding, Linglong Kong
2023	A*	`conf`	Opposite Online Learning via Sequentially Integrated Stochastic Gradient Descent Estimators. AAAI Wenhai Cui, Xiaoting Ji, Linglong Kong, Xiaodong Yan
2023	A	`conf`	Optimal Smooth Approximation for Quantile Matrix Factorization. SDM Peng Liu, Yi Liu, Rui Zhu, Linglong Kong, Bei Jiang, Di Niu
2022	J	`jnl`	Associations between Longitudinal Gestational Weight Gain and Scalar Infant Birth Weight: A Bayesian Joint Modeling Approach. Entropy Matthew Pietrosanu, Linglong Kong, Yan Yuan, Rhonda C. Bell, Nicole Letourneau, Bei Jiang
2022	J	`jnl`	Balancing Gender Bias in Job Advertisements With Text-Level Bias Mitigation. Frontiers Big Data 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
2022	A*	`conf`	Conformalized Fairness via Quantile Regression. NeurIPS Meichen Liu, Lei Ding, Dengdeng Yu, Wulong Liu, Linglong Kong, Bei Jiang
2022	J	`jnl`	Conformalized Fairness via Quantile Regression. CoRR Meichen Liu, Lei Ding, Dengdeng Yu, Wulong Liu, Linglong Kong, Bei Jiang
2022	J	`jnl`	Distributional Reinforcement Learning via Sinkhorn Iterations. CoRR Ke Sun, Yingnan Zhao, Yi Liu, Bei Jiang, Linglong Kong
2022	J	`jnl`	Factorizing Historical User Actions for Next-Day Purchase Prediction. ACM Trans. Web Bang Liu, Hanlin Zhang, Linglong Kong, Di Niu
2022	J	`jnl`	Flexible quantile contour estimation for multivariate functional data: Beyond convexity. Comput. Stat. Data Anal. Gaurav Agarwal, Wei Tu, Ying Sun, Linglong Kong
2022	J	`jnl`	How Does Value Distribution in Distributional Reinforcement Learning Help Optimization? CoRR Ke Sun, Bei Jiang, Linglong Kong
2022	A*	`conf`	Identification, Amplification and Measurement: A bridge to Gaussian Differential Privacy. NeurIPS Yi Liu, Ke Sun, Bei Jiang, Linglong Kong
2022	J	`jnl`	Identification, Amplification and Measurement: A bridge to Gaussian Differential Privacy. CoRR Yi Liu, Ke Sun, Linglong Kong, Bei Jiang
2022	B	`conf`	MTGnet: Multi-Task Spatiotemporal Graph Convolutional Networks for Air Quality Prediction. IJCNN Dan Lu, Rui Chen, Shanshan Sui, Qilong Han, Linglong Kong, Yichen Wang
2022	A*	`conf`	Sample Average Approximation for Stochastic Optimization with Dependent Data: Performance Guarantees and Tractability. AAAI Yafei Wang, Bo Pan, Wei Tu, Peng Liu, Bei Jiang, Chao Gao, Wei Lu, Shangling Jui, Linglong Kong
2022	A*	`conf`	TAG: Toward Accurate Social Media Content Tagging with a Concept Graph. KDD Jiuding Yang, Weidong Guo, Bang Liu, Yakun Yu, Chaoyue Wang, Jinwen Luo, Linglong Kong, Di Niu, Zhen Wen
2022	A*	`conf`	Word Embeddings via Causal Inference: Gender Bias Reducing and Semantic Information Preserving. AAAI Lei Ding, Dengdeng Yu, Jinhan Xie, Wenxing Guo, Shenggang Hu, Meichen Liu, Linglong Kong, Hongsheng Dai, Yanchun Bao, Bei Jiang
2021	J	`jnl`	A Simple Unified Framework for Anomaly Detection in Deep Reinforcement Learning. CoRR Hongming Zhang, Ke Sun, Bo Xu, Linglong Kong, Martin Müller
2021	J	`jnl`	Advanced algorithms for penalized quantile and composite quantile regression. Comput. Stat. Matthew Pietrosanu, Jueyu Gao, Linglong Kong, Bei Jiang, Di Niu
2021	A*	`conf`	Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization. NeurIPS Ke Sun, Yafei Wang, Yi Liu, Yingnan Zhao, Bo Pan, Shangling Jui, Bei Jiang, Linglong Kong
2021	J	`jnl`	Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization. CoRR Ke Sun, Yafei Wang, Yi Liu, Yingnan Zhao, Bo Pan, Shangling Jui, Bei Jiang, Linglong Kong
2021	J	`jnl`	Exploring the Robustness of Distributional Reinforcement Learning against Noisy State Observations. CoRR Ke Sun, Yi Liu, Yingnan Zhao, Hengshuai Yao, Shangling Jui, Linglong Kong
2021	J	`jnl`	L$^{2}$NAS: Learning to Optimize Neural Architectures via Continuous-Action Reinforcement Learning. CoRR Keith G. Mills, Fred X. Han, Mohammad Salameh, Seyed Saeed Changiz Rezaei, Linglong Kong, Wei Lu, Shuo Lian, Shangling Jui, Di Niu
2021	A	`conf`	L2NAS: Learning to Optimize Neural Architectures via Continuous-Action Reinforcement Learning. CIKM Keith G. Mills, Fred X. Han, Mohammad Salameh, Seyed Saeed Changiz Rezaei, Linglong Kong, Wei Lu, Shuo Lian, Shangling Jui, Di Niu
2021	J	`jnl`	TAG: Toward Accurate Social Media Content Tagging with a Concept Graph. CoRR Jiuding Yang, Weidong Guo, Bang Liu, Yakun Yu, Chaoyue Wang, Jinwen Luo, Linglong Kong, Di Niu, Zhen Wen
2021	J	`jnl`	Testing independence of functional variables by angle covariance. J. Multivar. Anal. Tingyu Lai, Zhongzhan Zhang, Yafei Wang, Linglong Kong
2021	J	`jnl`	Towards Understanding Distributional Reinforcement Learning: Regularization, Optimization, Acceleration and Sinkhorn Algorithm. CoRR Ke Sun, Yingnan Zhao, Yi Liu, Enze Shi, Yafei Wang, Aref Sadeghi, Xiaodong Yan, Bei Jiang, Linglong Kong
2021	J	`jnl`	Word Embeddings via Causal Inference: Gender Bias Reducing and Semantic Information Preserving. CoRR Lei Ding, Dengdeng Yu, Jinhan Xie, Wenxing Guo, Shenggang Hu, Meichen Liu, Linglong Kong, Hongsheng Dai, Yanchun Bao, Bei Jiang
2020	J	`jnl`	Sparse Multicategory Generalized Distance Weighted Discrimination in Ultra-High Dimensions. Entropy Tong Su, Yafei Wang, Yi Liu, William G. Branton, Eugene Asahchop, Christopher Power, Bei Jiang, Linglong Kong, Niansheng Tang
2020	J	`jnl`	Story Forest: Extracting Events and Telling Stories from Breaking News. ACM Trans. Knowl. Discov. Data Bang Liu, Fred X. Han, Di Niu, Linglong Kong, Kunfeng Lai, Yu Xu
2019	J	`jnl`	Deep Reinforcement Learning with Decorrelation. CoRR Borislav Mavrin, Hengshuai Yao, Linglong Kong
2019	A*	`conf`	Distributional Reinforcement Learning for Efficient Exploration. ICML Borislav Mavrin, Hengshuai Yao, Linglong Kong, Kaiwen Wu, Yaoliang Yu
2019	J	`jnl`	Distributional Reinforcement Learning for Efficient Exploration. CoRR Borislav Mavrin, Shangtong Zhang, Hengshuai Yao, Linglong Kong, Kaiwen Wu, Yaoliang Yu
2019	A*	`conf`	Ensemble-based Ultrahigh-dimensional Variable Screening. IJCAI Wei Tu, Dong Yang, Linglong Kong, Menglu Che, Qian Shi, Guodong Li, Guangjian Tian
2019	A	`conf`	Exploration in the Face of Parametric and Intrinsic Uncertainties. AAMAS Borislav Mavrin, Shangtong Zhang, Hengshuai Yao, Linglong Kong
2019	J	`jnl`	Learning Privately over Distributed Features: An ADMM Sharing Approach. CoRR Yaochen Hu, Peng Liu, Linglong Kong, Di Niu
2019	A*	`conf`	M-estimation in Low-Rank Matrix Factorization: A General Framework. ICDM Wei Tu, Peng Liu, Jingyu Zhao, Yi Liu, Linglong Kong, Guodong Li, Bei Jiang, Guangjian Tian, Hengshuai Yao
2019	J	`jnl`	Sparse wavelet estimation in quantile regression with multiple functional predictors. Comput. Stat. Data Anal. Dengdeng Yu, Li Zhang, Ivan Mizera, Bei Jiang, Linglong Kong
2018	J	`jnl`	Growing Story Forest Online from Massive Breaking News. CoRR Bang Liu, Di Niu, Kunfeng Lai, Linglong Kong, Yu Xu
2018	J	`jnl`	House Price Modeling over Heterogeneous Regions with Hierarchical Spatial Functional Analysis. CoRR Bang Liu, Borislav Mavrin, Di Niu, Linglong Kong
2018	J	`jnl`	QUOTA: The Quantile Option Architecture for Reinforcement Learning. CoRR Shangtong Zhang, Borislav Mavrin, Linglong Kong, Bo Liu, Hengshuai Yao
2018	J	`jnl`	Recover Fine-Grained Spatial Data from Coarse Aggregation. CoRR Bang Liu, Borislav Mavrin, Linglong Kong, Di Niu
2018	J	`jnl`	Significant Anatomy Detection Through Sparse Classification: A Comparative Study. IEEE Trans. Medical Imaging Li Zhang, Dana Cobzas, Alan H. Wilman, Linglong Kong
2017	—	`conf`	An Unbiased Penalty for Sparse Classification with Application to Neuroimaging Data. MICCAI (3) Li Zhang, Dana Cobzas, Alan H. Wilman, Linglong Kong
2017	A*	`conf`	Expectile Matrix Factorization for Skewed Data Analysis. AAAI Rui Zhu, Di Niu, Linglong Kong, Zongpeng Li
2017	A	`conf`	Growing Story Forest Online from Massive Breaking News. CIKM Bang Liu, Di Niu, Kunfeng Lai, Linglong Kong, Yu Xu
2017	A*	`conf`	Recover Fine-Grained Spatial Data from Coarse Aggregation. ICDM Bang Liu, Borislav Mavrin, Linglong Kong, Di Niu
2016	A*	`conf`	House Price Modeling over Heterogeneous Regions with Hierarchical Spatial Functional Analysis. ICDM Bang Liu, Borislav Mavrin, Di Niu, Linglong Kong
2016	J	`jnl`	Local Region Sparse Learning for Image-on-Scalar Regression. CoRR Yao Chen, Xiao Wang, Linglong Kong, Hongtu Zhu
2016	J	`jnl`	Partial functional linear quantile regression for neuroimaging data analysis. Neurocomputing Dengdeng Yu, Linglong Kong, Ivan Mizera
2016	J	`jnl`	Regularized quantile regression under heterogeneous sparsity with application to quantitative genetic traits. Comput. Stat. Data Anal. Qianchuan He, Linglong Kong, Yanhua Wang, Sijian Wang, Timothy A. Chan, Eric Holland
2015	—	`conf`	Functional Nonlinear Mixed Effects Models for Longitudinal Image Data. IPMI Xinchao Luo, Lixing Zhu, Linglong Kong, Hongtu Zhu
2011	J	`jnl`	FADTTS: Functional analysis of diffusion tensor tract statistics. NeuroImage Hongtu Zhu, Linglong Kong, Runze Li, Martin Styner, Guido Gerig, Weili Lin, John H. Gilmore
2010	—	`conf`	Multivariate Varying Coefficient Models for DTI Tract Statistics. MICCAI (1) Hongtu Zhu, Martin Styner, Yimei Li, Linglong Kong, Yundi Shi, Weili Lin, Christopher L. Coe, John H. Gilmore
2010	J	`jnl`	Smooth depth contours characterize the underlying distribution. J. Multivar. Anal. Linglong Kong, Yijun Zuo