Travelled to:1 × Germany
1 × Israel
1 × United Kingdom
6 × USA
Collaborated with:I.S.Dhillon M.I.Jordan Y.Guan P.L.Bartlett T.Broderick M.A.Sustik S.Basu R.J.Mooney J.E.Hopcroft O.Khan B.Selman J.V.Davis P.Jain S.Sra
Talks about:kernel (4) cluster (3) learn (3) algorithm (2) graph (2) mean (2) base (2) bay (2) nonparametr (1) multilevel (1)
Person: Brian Kulis
DBLP: Kulis:Brian
Contributed to:
201320122010200720062005
200520042003Wrote 9 papers:
- ICML-c3-2013-BroderickKJ #named
- MAD-Bayes: MAP-based Asymptotic Derivations from Bayes (TB, BK, MIJ), pp. 226–234.
- ICML-2012-KulisJ #algorithm
- Revisiting k-means: New Algorithms via Bayesian Nonparametrics (BK, MIJ), p. 148.
- ICML-2010-KulisB #learning #online
- Implicit Online Learning (BK, PLB), pp. 575–582.
- 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.
- 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.
- KDD-2004-DhillonGK #clustering #kernel #normalisation
- Kernel k-means: spectral clustering and normalized cuts (ISD, YG, BK), pp. 551–556.
- KDD-2003-HopcroftKKS #community #network #scalability
- Natural communities in large linked networks (JEH, OK, BK, BS), pp. 541–546.