2020 - IEEE Fellow For contributions to the methodology and application of machine learning and data mining
2020 - ACM Distinguished Member
His primary scientific interests are in Artificial intelligence, Pattern recognition, Mathematical optimization, Machine learning and Dimensionality reduction. His research brings together the fields of Multi-task learning and Artificial intelligence. His research in Pattern recognition intersects with topics in Conditional probability, Conditional probability distribution and Marginal distribution.
Jieping Ye combines subjects such as Regularization, Matrix completion and Convex optimization with his study of Mathematical optimization. His research integrates issues of Dementia and Cognition in his study of Machine learning. Jieping Ye has included themes like Algorithm, Scatter matrix and Computer vision in his Dimensionality reduction study.
Artificial intelligence, Machine learning, Pattern recognition, Mathematical optimization and Data mining are his primary areas of study. His study in Multi-task learning extends to Artificial intelligence with its themes. His work focuses on many connections between Machine learning and other disciplines, such as Neuroimaging, that overlap with his field of interest in Disease.
Feature extraction and Principal component analysis are among the areas of Pattern recognition where Jieping Ye concentrates his study. His Mathematical optimization study combines topics in areas such as Convex optimization and Algorithm, Regularization. His studies deal with areas such as Singular value decomposition and QR decomposition as well as Linear discriminant analysis.
Jieping Ye mostly deals with Artificial intelligence, Machine learning, Deep learning, Reinforcement learning and Key. His Artificial intelligence study integrates concerns from other disciplines, such as Margin and Pattern recognition. His Pattern recognition research incorporates themes from Frame and Background noise.
His studies in Machine learning integrate themes in fields like Neuroimaging, Coding, Inference and Cognitive decline. The Deep learning study combines topics in areas such as Intelligent transportation system, Data mining, Artificial neural network, Scale and Estimated time of arrival. The concepts of his Key study are interwoven with issues in Matching, Price elasticity of demand, Supply and demand and Social optimum.
His primary areas of study are Artificial intelligence, Reinforcement learning, Key, Graph and Machine learning. His Artificial intelligence research is multidisciplinary, incorporating elements of Margin and Pattern recognition. His biological study spans a wide range of topics, including Order, Combinatorial optimization and Bipartite graph.
Jieping Ye focuses mostly in the field of Key, narrowing it down to topics relating to Matching and, in certain cases, Mathematical optimization, Interval and Control variable. The study incorporates disciplines such as Theoretical computer science, Data mining and Graph in addition to Graph. His Machine learning research integrates issues from Neuroimaging, Speedup and Heuristic.
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Tensor completion for estimating missing values in visual data
Ji Liu;Przemyslaw Musialski;Peter Wonka;Jieping Ye.
international conference on computer vision (2009)
Two-Dimensional Linear Discriminant Analysis
Jieping Ye;Ravi Janardan;Qi Li.
neural information processing systems (2004)
Multi-task feature learning via efficient l 2, 1 -norm minimization
Jun Liu;Shuiwang Ji;Jieping Ye.
uncertainty in artificial intelligence (2009)
Fast and Accurate Matrix Completion via Truncated Nuclear Norm Regularization
Yao Hu;Debing Zhang;Jieping Ye;Xuelong Li.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2013)
Generalized Low Rank Approximations of Matrices
Machine Learning (2005)
An accelerated gradient method for trace norm minimization
Shuiwang Ji;Jieping Ye.
international conference on machine learning (2009)
SLEP: Sparse Learning with Efficient Projections
Jun Liu;Shuiwang Ji;Jieping Ye.
Object Detection in 20 Years: A Survey
Zhengxia Zou;Zhenwei Shi;Yuhong Guo;Jieping Ye.
arXiv: Computer Vision and Pattern Recognition (2019)
Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction
Huaxiu Yao;Fei Wu;Jintao Ke;Xianfeng Tang.
national conference on artificial intelligence (2018)
Partial Least Squares
Liang Sun;Shuiwang Ji;Jieping Ye.
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