- CSB - MetaPress - Google - Bing - Yahoo
 | 2012 | |
|---|---|---|
| j8 |      | Prateek Jain, Brian Kulis, Jason V. Davis, Inderjit S. Dhillon: Metric and Kernel Learning Using a Linear Transformation. Journal of Machine Learning Research 13: 519-547 (2012) |
| j7 | | Brian Kulis, Kristen Grauman: Kernelized Locality-Sensitive Hashing. IEEE Trans. Pattern Anal. Mach. Intell. 34(6): 1092-1104 (2012) |
| c18 | | Judy Hoffman, Brian Kulis, Trevor Darrell, Kate Saenko: Discovering Latent Domains for Multisource Domain Adaptation. ECCV (2) 2012: 702-715 |
| c17 | | Brian Kulis, Michael I. Jordan: Revisiting k-means: New Algorithms via Bayesian Nonparametrics. ICML 2012 |
| c16 | | Ke Jiang, Brian Kulis, Michael I. Jordan: Small-Variance Asymptotics for Exponential Family Dirichlet Process Mixture Models. NIPS 2012: 3167-3175 |
| 2011 | ||
| c15 | | Matthew E. Taylor, Brian Kulis, Fei Sha: Metric learning for reinforcement learning agents. AAMAS 2011: 777-784 |
| c14 | | Brian Kulis, Kate Saenko, Trevor Darrell: What you saw is not what you get: Domain adaptation using asymmetric kernel transforms. CVPR 2011: 1785-1792 |
| i1 | | Brian Kulis, Michael I. Jordan: Revisiting k-means: New Algorithms via Bayesian Nonparametrics. CoRR abs/1111.0352 (2011) |
| 2010 | ||
| c13 | | Kate Saenko, Brian Kulis, Mario Fritz, Trevor Darrell: Adapting Visual Category Models to New Domains. ECCV (4) 2010: 213-226 |
| c12 | | |
| c11 | | Prateek Jain, Brian Kulis, Inderjit S. Dhillon: Inductive Regularized Learning of Kernel Functions. NIPS 2010: 946-954 |
| 2009 | ||
| j6 | | Brian Kulis, Suvrit Sra, Inderjit S. Dhillon: Convex Perturbations for Scalable Semidefinite Programming. Journal of Machine Learning Research - Proceedings Track 5: 296-303 (2009) |
| j5 | | Brian Kulis, Mátyás A. Sustik, Inderjit S. Dhillon: Low-Rank Kernel Learning with Bregman Matrix Divergences. Journal of Machine Learning Research 10: 341-376 (2009) |
| j4 | | Brian Kulis, Sugato Basu, Inderjit S. Dhillon, Raymond J. Mooney: Semi-supervised graph clustering: a kernel approach. Machine Learning 74(1): 1-22 (2009) |
| j3 | | Brian Kulis, Prateek Jain, Kristen Grauman: Fast Similarity Search for Learned Metrics. IEEE Trans. Pattern Anal. Mach. Intell. 31(12): 2143-2157 (2009) |
| c10 | | Brian Kulis, Kristen Grauman: Kernelized locality-sensitive hashing for scalable image search. ICCV 2009: 2130-2137 |
| c9 | | Brian Kulis, Trevor Darrell: Learning to Hash with Binary Reconstructive Embeddings. NIPS 2009: 1042-1050 |
| 2008 | ||
| c8 | | |
| c7 | | Prateek Jain, Brian Kulis, Inderjit S. Dhillon, Kristen Grauman: Online Metric Learning and Fast Similarity Search. NIPS 2008: 761-768 |
| 2007 | ||
| j2 | | Brian Kulis, Arun C. Surendran, John C. Platt: Fast Low-Rank Semidefinite Programming for Embedding and Clustering. Journal of Machine Learning Research - Proceedings Track 2: 235-242 (2007) |
| j1 | | Inderjit S. Dhillon, Yuqiang Guan, Brian Kulis: Weighted Graph Cuts without Eigenvectors A Multilevel Approach. IEEE Trans. Pattern Anal. Mach. Intell. 29(11): 1944-1957 (2007) |
| c6 | | Jason V. Davis, Brian Kulis, Prateek Jain, Suvrit Sra, Inderjit S. Dhillon: Information-theoretic metric learning. ICML 2007: 209-216 |
| 2006 | ||
| c5 | | Brian Kulis, Mátyás A. Sustik, Inderjit S. Dhillon: Learning low-rank kernel matrices. ICML 2006: 505-512 |
| 2005 | ||
| c4 | | Brian Kulis, Sugato Basu, Inderjit S. Dhillon, Raymond J. Mooney: Semi-supervised graph clustering: a kernel approach. ICML 2005: 457-464 |
| c3 | | Inderjit S. Dhillon, Yuqiang Guan, Brian Kulis: A fast kernel-based multilevel algorithm for graph clustering. KDD 2005: 629-634 |
| 2004 | ||
| c2 | | Inderjit S. Dhillon, Yuqiang Guan, Brian Kulis: Kernel k-means: spectral clustering and normalized cuts. KDD 2004: 551-556 |
| 2003 | ||
| c1 | | John E. Hopcroft, Omar Khan, Brian Kulis, Bart Selman: Natural communities in large linked networks. KDD 2003: 541-546 |
Colors in the list of coauthors
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