- Natural Communities in Large Linked Networks John Hopcroft, Omar Khan, Brian Kulis, and Bart Selman
- Fast Low-Rank Semidefinite Programming for Embedding and Clustering
- Fast Image Search for Learned Metrics Prateek Jain Brian Kulis Kristen Grauman
- Semi-supervised Graph Clustering: A Kernel Approach Brian Kulis kulis@cs.utexas.edu
- Learning to Hash with Binary Reconstructive Brian Kulis
- Fast Similarity Search for Learned Metrics Prateek Jain Brian Kulis Kristen Grauman
- Implicit Online Learning Brian Kulis KULIS@EECS.BERKELEY.EDU
- Journal of Machine Learning Research 10 (2009) 341-376 Submitted 8/07; Revised 10/08; Published 2/09 Low-Rank Kernel Learning with Bregman Matrix Divergences
- Information-Theoretic Metric Learning Jason V. Davis JDAVIS@CS.UTEXAS.EDU
- Convex Perturbations for Scalable Semidefinite Programming Brian Kulis
- Weighted Graph Cuts without Eigenvectors: A Multilevel Approach
- Fast Similarity Search for Learned Metrics Brian Kulis, Member, IEEE, Prateek Jain, Student Member, IEEE, and
- Inductive Regularized Learning of Kernel Functions Prateek Jain
- Adapting Visual Category Models to New Kate Saenko, Brian Kulis, Mario Fritz, and Trevor Darrell
- Learning Low-Rank Kernel Matrices Brian Kulis kulis@cs.utexas.edu
- Online Linear Regression using Burg Entropy Prateek Jain, Brian Kulis, and Inderjit Dhillon
- Information-Theoretic Metric Learning Jason Davis, Brian Kulis, Suvrit Sra and Inderjit Dhillon
- A Unified View of Kernel k-means, Spectral Clustering and Graph Inderjit Dhillon, Yuqiang Guan and Brian Kulis
- Scalable Semidefinite Programming using Convex Perturbations
- DOI 10.1007/s10994-008-5084-4 Semi-supervised graph clustering: a kernel approach
- What You Saw is Not What You Get: Domain Adaptation Using Asymmetric Kernel Transforms
- Learning to Hash with Binary Reconstructive Brian Kulis and Trevor Darrell
- Metric Learning for Reinforcement Learning Agents Matthew E. Taylor, Brian Kulis, and Fei Sha
- Tracking evolving communities in large linked networks
