BibSLEIGH — Inderjit_S

Travelled to:

1 × Australia
1 × Finland
1 × France
1 × Germany
1 × Israel
1 × Spain
1 × United Kingdom
11 × USA
3 × Canada
3 × China

Collaborated with:

C.Hsieh B.Kulis J.V.Davis N.Natarajan P.Jain S.Si K.Chiang Z.Lu J.Ghosh S.Sra H.Yu A.Banerjee D.Shin Y.Guan S.Mallela J.J.Whang D.S.Modha ∅ S.Merugu D.Inouye P.D.Ravikumar D.Kim D.Agarwal M.A.Sustik R.Kumar Y.Hou D.F.Gleich P.Kar A.Tewari V.Vasuki R.Meka C.Caramanis S.Basu R.J.Mooney D.Park J.Neeman J.Zhang S.Sanghavi I.E.Yen X.Lin K.Zhong P.K.Ravikumar H.Yun S.V.N.Vishwanathan M.Deodhar G.Gupta H.Cho

Talks about:

cluster (11) learn (7) kernel (6) rank (5) approach (4) network (4) matrix (4) model (4) use (4) algorithm (3)

Person: Inderjit S. Dhillon

DBLP: Dhillon:Inderjit_S=

Facilitated 1 volumes:

Contributed to:

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

Wrote 35 papers:

ICML-2015-HsiehND #learning #matrix: PU Learning for Matrix Completion (CJH, NN, ISD), pp. 2445–2453.
ICML-2015-HsiehYD #named #parallel #probability: PASSCoDe: Parallel ASynchronous Stochastic dual Co-ordinate Descent (CJH, HFY, ISD), pp. 2370–2379.
ICML-2015-ParkNZSD #collaboration #ranking #scalability: Preference Completion: Large-scale Collaborative Ranking from Pairwise Comparisons (DP, JN, JZ, SS, ISD), pp. 1907–1916.
ICML-2015-YenLZRD #approach #modelling #process: A Convex Exemplar-based Approach to MAD-Bayes Dirichlet Process Mixture Models (IEHY, XL, KZ, PKR, ISD), pp. 2418–2426.
KDD-2015-HouWGD #clustering #programming #rank: Non-exhaustive, Overlapping Clustering via Low-Rank Semidefinite Programming (YH, JJW, DFG, ISD), pp. 427–436.
ICML-c1-2014-HsiehSD #divide and conquer #kernel: A Divide-and-Conquer Solver for Kernel Support Vector Machines (CJH, SS, ISD), pp. 566–574.
ICML-c1-2014-InouyeRD #dependence #topic #word: Admixture of Poisson MRFs: A Topic Model with Word Dependencies (DI, PDR, ISD), pp. 683–691.
ICML-c1-2014-SiHD #approximate #kernel #memory management #performance: Memory Efficient Kernel Approximation (SS, CJH, ISD), pp. 701–709.
ICML-c1-2014-Yu0KD #learning #multi #scalability: Large-scale Multi-label Learning with Missing Labels (HFY, PJ, PK, ISD), pp. 593–601.
VLDB-2014-YunYHVD #algorithm #distributed #matrix #multi #named #probability: NOMAD: Nonlocking, stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix completion (HY, HFY, CJH, SVNV, ISD), pp. 975–986.
RecSys-2013-NatarajanSD #collaboration: Which app will you use next?: collaborative filtering with interactional context (NN, DS, ISD), pp. 201–208.
CIKM-2012-ChiangWD #clustering #network #normalisation #scalability #using: Scalable clustering of signed networks using balance normalized cut (KYC, JJW, ISD), pp. 615–624.
CIKM-2012-ShinSD #multi #predict: Multi-scale link prediction (DS, SS, ISD), pp. 215–224.
KDD-2012-HsiehCD #modelling #network #rank: Low rank modeling of signed networks (CJH, KYC, ISD), pp. 507–515.
CIKM-2011-ChiangNTD #network #predict: Exploiting longer cycles for link prediction in signed networks (KYC, NN, AT, ISD), pp. 1157–1162.
KDD-2011-HsiehD #coordination #matrix #performance: Fast coordinate descent methods with variable selection for non-negative matrix factorization (CJH, ISD), pp. 1064–1072.
ICML-2010-KimSD #algorithm #scalability: A scalable trust-region algorithm with application to mixed-norm regression (DK, SS, ISD), pp. 519–526.
RecSys-2010-VasukiNLD #network #recommendation #using: Affiliation recommendation using auxiliary networks (VV, NN, ZL, ISD), pp. 103–110.
ICML-2009-DeodharGGCD #clustering #framework #scalability #semistructured data: A scalable framework for discovering coherent co-clusters in noisy data (MD, GG, JG, HC, ISD), pp. 241–248.
ICML-2009-LuJD #geometry #learning #metric: Geometry-aware metric learning (ZL, PJ, ISD), pp. 673–680.
RecSys-2009-LuAD #approach #collaboration: A spatio-temporal approach to collaborative filtering (ZL, DA, ISD), pp. 13–20.
ICML-2008-MekaJCD #learning #online #rank: Rank minimization via online learning (RM, PJ, CC, ISD), pp. 656–663.
KDD-2008-DavisD #learning #metric #problem: Structured metric learning for high dimensional problems (JVD, ISD), pp. 195–203.
ICML-2007-DavisKJSD #learning #metric: Information-theoretic metric learning (JVD, BK, PJ, SS, ISD), pp. 209–216.
ICML-2006-KulisSD #kernel #learning #matrix #rank: Learning low-rank kernel matrices (BK, MAS, ISD), pp. 505–512.
KDD-2006-DavisD #community #rank #web: Estimating the global pagerank of web communities (JVD, ISD), pp. 116–125.
ICML-2005-KulisBDM #approach #clustering #graph #kernel: Semi-supervised graph clustering: a kernel approach (BK, SB, ISD, RJM), pp. 457–464.
KDD-2005-DhillonGK #algorithm #clustering #graph #kernel #multi #performance: A fast kernel-based multilevel algorithm for graph clustering (ISD, YG, BK), pp. 629–634.
ICML-2004-BanerjeeDGM #analysis #estimation #exponential #product line: An information theoretic analysis of maximum likelihood mixture estimation for exponential families (AB, ISD, JG, SM).
KDD-2004-BanerjeeDGMM #approach #approximate #clustering #matrix: A generalized maximum entropy approach to bregman co-clustering and matrix approximation (AB, ISD, JG, SM, DSM), pp. 509–514.
KDD-2004-DhillonGK #clustering #kernel #normalisation: Kernel k-means: spectral clustering and normalized cuts (ISD, YG, BK), pp. 551–556.
KDD-2003-BanerjeeDGS #clustering #generative #modelling: Generative model-based clustering of directional data (AB, ISD, JG, SS), pp. 19–28.
KDD-2003-DhillonMM #clustering: Information-theoretic co-clustering (ISD, SM, DSM), pp. 89–98.
KDD-2002-DhillonMK #classification #clustering #word: Enhanced word clustering for hierarchical text classification (ISD, SM, RK), pp. 191–200.
KDD-2001-Dhillon #clustering #documentation #graph #using #word: Co-clustering documents and words using bipartite spectral graph partitioning (ISD), pp. 269–274.