BibSLEIGH — Jieping

Travelled to:

1 × Australia
1 × Finland
14 × USA
2 × Canada
3 × China
3 × France

Collaborated with:

S.Ji J.Chen J.Liu L.Yuan J.Wang J.Zhou W.Fan L.Sun S.Xiang P.Gong Q.Li J.Liu ∅ Z.Wang P.Wonka R.Janardan Q.Sun I.Davidson Z.Zhao S.Panchanathan S.Yang T.Xiong R.Chattopadhyay H.Liu Z.Lu C.Zhang R.Jin B.Ceran L.Tang T.Wang X.He V.A.Narayan F.Wang X.Shen H.Park Y.Wang P.M.Thompson J.Li K.Chen T.Wu E.Reiman B.Lin N.Ramakrishnan R.Fujimaki Y.Liu T.Yang X.Tong L.Yang B.Shen S.Kumar J.Yang J.Hu Y.Zhu S.Yu R.Patel S.Chakraborty V.N.Balasubramanian A.R.Sankar J.Huang Y.Hu D.Zhang Z.Xu M.R.Lyu I.King J.Wang Y.Chang C.Kuo X.Wang P.B.Walker O.T.Carmichael P.Chakraborty S.R.Mekaru J.S.Brownstein L.Zhao F.Chen C.Lu M.Lai H.Davulcu Q.Li X.Zhang J.Sun Y.Lai Y.Li Z.Zhou M.Wu H.Xiong V.Kumar M.S.Amin B.Yan C.Martell V.Markman A.Bhasin W.Zhang R.Li T.Zeng S.Huang A.Fleisher J.Bi V.Cherkassky C.Kambhamettu P.Chen D.Kadetotad Z.Xu A.Mohanty S.B.K.Vrudhula J.Seo Y.Cao S.Yu M.Bae G.E.Alexander

Talks about:

learn (25) multi (13) analysi (11) featur (11) spars (9) task (9) data (9) discrimin (8) algorithm (8) effici (8)

Person: Jieping Ye

DBLP: Ye:Jieping

Contributed to:

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2004

Wrote 71 papers:

DATE-2015-ChenKXMLYVSCY #algorithm #array #learning: Technology-design co-optimization of resistive cross-point array for accelerating learning algorithms on chip (PYC, DK, ZX, AM, BL, JY, SBKV, JsS, YC, SY), pp. 854–859.
ICML-2015-GongY #analysis #convergence #memory management: A Modified Orthant-Wise Limited Memory Quasi-Newton Method with Convergence Analysis (PG, JY), pp. 276–284.
ICML-2015-WangY #learning #matrix #multi: Safe Screening for Multi-Task Feature Learning with Multiple Data Matrices (JW, JY), pp. 1747–1756.
KDD-2015-ChakrabortyBSPY #classification #framework #learning #named #novel: BatchRank: A Novel Batch Mode Active Learning Framework for Hierarchical Classification (SC, VNB, ARS, SP, JY), pp. 99–108.
KDD-2015-KuoWWCYD #graph #multi #segmentation: Unified and Contrasting Cuts in Multiple Graphs: Application to Medical Imaging Segmentation (CTK, XW, PBW, OTC, JY, ID), pp. 617–626.
KDD-2015-SunAYMMBY #classification #learning: Transfer Learning for Bilingual Content Classification (QS, MSA, BY, CM, VM, AB, JY), pp. 2147–2156.
KDD-2015-WangCMBYR #predict: Dynamic Poisson Autoregression for Influenza-Like-Illness Case Count Prediction (ZW, PC, SRM, JSB, JY, NR), pp. 1285–1294.
KDD-2015-YangSJWDY #learning #visual notation: Structural Graphical Lasso for Learning Mouse Brain Connectivity (SY, QS, SJ, PW, ID, JY), pp. 1385–1394.
KDD-2015-ZhangLZSKYJ #analysis #biology #image #learning #modelling #multi: Deep Model Based Transfer and Multi-Task Learning for Biological Image Analysis (WZ, RL, TZ, QS, SK, JY, SJ), pp. 1475–1484.
KDD-2015-ZhaoSYCLR #learning #multi: Multi-Task Learning for Spatio-Temporal Event Forecasting (LZ, QS, JY, FC, CTL, NR), pp. 1503–1512.
ICML-c1-2014-LiuYF #algorithm #constraints: Forward-Backward Greedy Algorithms for General Convex Smooth Functions over A Cardinality Constraint (JL, JY, RF), pp. 503–511.
ICML-c2-2014-LinYHY #distance #learning: Geodesic Distance Function Learning via Heat Flow on Vector Fields (BL, JY, XH, JY), pp. 145–153.
ICML-c2-2014-LiuZWY: Safe Screening with Variational Inequalities and Its Application to Lasso (JL, ZZ, JW, JY), pp. 289–297.
ICML-c2-2014-WangLLFDY #matrix: Rank-One Matrix Pursuit for Matrix Completion (ZW, MJL, ZL, WF, HD, JY), pp. 91–99.
ICML-c2-2014-WangLYFWY #algorithm #modelling #parallel #scalability: A Highly Scalable Parallel Algorithm for Isotropic Total Variation Models (JW, QL, SY, WF, PW, JY), pp. 235–243.
ICML-c2-2014-WangWY #reduction #scalability: Scaling SVM and Least Absolute Deviations via Exact Data Reduction (JW, PW, JY), pp. 523–531.
KDD-2014-GongZFY #learning #multi #performance: Efficient multi-task feature learning with calibration (PG, JZ, WF, JY), pp. 761–770.
KDD-2014-LiuWY #algorithm #performance: An efficient algorithm for weak hierarchical lasso (YL, JW, JY), pp. 283–292.
KDD-2014-XiangYY: Simultaneous feature and feature group selection through hard thresholding (SX, TY, JY), pp. 532–541.
KDD-2014-ZhouWHY #data-driven #metaprogramming: From micro to macro: data driven phenotyping by densification of longitudinal electronic medical records (JZ, FW, JH, JY), pp. 135–144.
ICML-c1-2013-XiangTY #feature model #optimisation #performance: Efficient Sparse Group Feature Selection via Nonconvex Optimization (SX, XT, JY), pp. 284–292.
ICML-c2-2013-GongZLHY #algorithm #optimisation #problem: A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems (PG, CZ, ZL, JH, JY), pp. 37–45.
ICML-c3-2013-0002YY #linear: Guaranteed Sparse Recovery under Linear Transformation (JL, LY, JY), pp. 91–99.
ICML-c3-2013-ChattopadhyayFDPY #learning: Joint Transfer and Batch-mode Active Learning (RC, WF, ID, SP, JY), pp. 253–261.
KDD-2013-SunXY #analysis #component #robust: Robust principal component analysis via capped norms (QS, SX, JY), pp. 311–319.
KDD-2013-WangY #learning #query: Querying discriminative and representative samples for batch mode active learning (ZW, JY), pp. 158–166.
KDD-2013-XiangYFWTY #learning #multi #predict: Multi-source learning with block-wise missing data for Alzheimer’s disease prediction (SX, LY, WF, YW, PMT, JY), pp. 185–193.
KDD-2013-YangWFZWY #algorithm #multi #performance #problem: An efficient ADMM algorithm for multidimensional anisotropic total variation regularization problems (SY, JW, WF, XZ, PW, JY), pp. 641–649.
KDD-2013-ZhouLSYWY #identification #named: FeaFiner: biomarker identification from medical data through feature generalization and selection (JZ, ZL, JS, LY, FW, JY), pp. 1034–1042.
KDD-2012-ChattopadhyayWFDPY #probability: Batch mode active sampling based on marginal probability distribution matching (RC, ZW, WF, ID, SP, JY), pp. 741–749.
KDD-2012-GongYZ #learning #multi #robust: Robust multi-task feature learning (PG, JY, CZ), pp. 895–903.
KDD-2012-HuZLYH #matrix: Accelerated singular value thresholding for matrix completion (YH, DZ, JL, JY, XH), pp. 298–306.
KDD-2012-XiangZSY #rank: Optimal exact least squares rank minimization (SX, YZ, XS, JY), pp. 480–488.
KDD-2012-YangYLSWY #graph: Feature grouping and selection over an undirected graph (SY, LY, YCL, XS, PW, JY), pp. 922–930.
KDD-2012-YuanWTNY #analysis #learning #multi: Multi-source learning for joint analysis of incomplete multi-modality neuroimaging data (LY, YW, PMT, VAN, JY), pp. 1149–1157.
KDD-2012-ZhouLNY #modelling: Modeling disease progression via fused sparse group lasso (JZ, JL, VAN, JY), pp. 1095–1103.
KDD-2011-ChattopadhyayYPFD #adaptation #detection #multi: Multi-source domain adaptation and its application to early detection of fatigue (RC, JY, SP, WF, ID), pp. 717–725.
KDD-2011-ChenZY #learning #multi #rank #robust: Integrating low-rank and group-sparse structures for robust multi-task learning (JC, JZ, JY), pp. 42–50.
KDD-2011-HuangLYFCWR #effectiveness #modelling #network: Brain effective connectivity modeling for alzheimer’s disease by sparse gaussian bayesian network (SH, JL, JY, AF, KC, TW, ER), pp. 931–939.
KDD-2011-ZhouYLY #learning #multi #predict: A multi-task learning formulation for predicting disease progression (JZ, LY, JL, JY), pp. 814–822.
KDD-2010-ChenLY #learning #multi #rank: Learning incoherent sparse and low-rank patterns from multiple tasks (JC, JL, JY), pp. 1179–1188.
KDD-2010-LiuYY #algorithm #performance #problem: An efficient algorithm for a class of fused lasso problems (JL, LY, JY), pp. 323–332.
KDD-2010-SunCY #approach #reduction #scalability: A scalable two-stage approach for a class of dimensionality reduction techniques (LS, BC, JY), pp. 313–322.
ICML-2009-ChenTLY #learning #multi: A convex formulation for learning shared structures from multiple tasks (JC, LT, JL, JY), pp. 137–144.
ICML-2009-JiY: An accelerated gradient method for trace norm minimization (SJ, JY), pp. 457–464.
ICML-2009-LiuY #linear #performance: Efficient Euclidean projections in linear time (JL, JY), pp. 657–664.
ICML-2009-SunJY #machine learning #problem: A least squares formulation for a class of generalized eigenvalue problems in machine learning (LS, SJ, JY), pp. 977–984.
ICML-2009-XuJYLK #feature model: Non-monotonic feature selection (ZX, RJ, JY, MRL, IK), pp. 1145–1152.
ICML-2009-YangJY #learning #online: Online learning by ellipsoid method (LY, RJ, JY), pp. 1153–1160.
KDD-2009-JiYLZKY #interactive #using: Drosophila gene expression pattern annotation using sparse features and term-term interactions (SJ, LY, YXL, ZHZ, SK, JY), pp. 407–416.
KDD-2009-LiuCY #scalability: Large-scale sparse logistic regression (JL, JC, JY), pp. 547–556.
KDD-2009-ShenJY #matrix #mining: Mining discrete patterns via binary matrix factorization (BHS, SJ, JY), pp. 757–766.
KDD-2009-SunPLCWLRY #estimation #mining: Mining brain region connectivity for alzheimer’s disease study via sparse inverse covariance estimation (LS, RP, JL, KC, TW, JL, ER, JY), pp. 1335–1344.
ICML-2008-ChenY #kernel: Training SVM with indefinite kernels (JC, JY), pp. 136–143.
ICML-2008-SunJY #analysis #canonical #correlation: A least squares formulation for canonical correlation analysis (LS, SJ, JY), pp. 1024–1031.
KDD-2008-ChenJCLWY #classification #kernel #learning: Learning subspace kernels for classification (JC, SJ, BC, QL, MW, JY), pp. 106–114.
KDD-2008-JiTYY #classification #multi: Extracting shared subspace for multi-label classification (SJ, LT, SY, JY), pp. 381–389.
KDD-2008-SunJY #classification #learning #multi: Hypergraph spectral learning for multi-label classification (LS, SJ, JY), pp. 668–676.
KDD-2008-YeCWLZPBJLAR #data fusion #semistructured data: Heterogeneous data fusion for alzheimer’s disease study (JY, KC, TW, JL, ZZ, RP, MB, RJ, HL, GEA, ER), pp. 1025–1033.
KDD-2008-ZhaoWLYC #data flow #identification #multi #semistructured data: Identifying biologically relevant genes via multiple heterogeneous data sources (ZZ, JW, HL, JY, YC), pp. 839–847.
ICML-2007-Ye #analysis #linear: Least squares linear discriminant analysis (JY), pp. 1087–1093.
ICML-2007-YeCJ #kernel #learning #parametricity #programming: Discriminant kernel and regularization parameter learning via semidefinite programming (JY, JC, SJ), pp. 1095–1102.
KDD-2007-ChenZYL #adaptation #clustering #distance #learning #metric: Nonlinear adaptive distance metric learning for clustering (JC, ZZ, JY, HL), pp. 123–132.
KDD-2007-YeJC #analysis #kernel #learning #matrix #polynomial #programming: Learning the kernel matrix in discriminant analysis via quadratically constrained quadratic programming (JY, SJ, JC), pp. 854–863.
CIKM-2006-YeXLJBCK #analysis #linear #performance: Efficient model selection for regularized linear discriminant analysis (JY, TX, QL, RJ, JB, VC, CK), pp. 532–539.
ICML-2006-YeX #analysis #linear #null #orthogonal: Null space versus orthogonal linear discriminant analysis (JY, TX), pp. 1073–1080.
KDD-2006-YeW #analysis: Regularized discriminant analysis for high dimensional, low sample size data (JY, TW), pp. 454–463.
ICML-2004-Ye #approximate #matrix #rank: Generalized low rank approximations of matrices (JY).
ICML-2004-YeJLP #analysis #feature model #linear: Feature extraction via generalized uncorrelated linear discriminant analysis (JY, RJ, QL, HP).
KDD-2004-YeJL #image #named #performance #reduction #retrieval: GPCA: an efficient dimension reduction scheme for image compression and retrieval (JY, RJ, QL), pp. 354–363.
KDD-2004-YePLJXK #algorithm #composition #incremental #named #reduction: IDR/QR: an incremental dimension reduction algorithm via QR decomposition (JY, QL, HX, HP, RJ, VK), pp. 364–373.