Publications By Type

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Journal Papers


  • Asymmetric and Category Invariant Feature Transformations for Domain Adaptation
    Judy Hoffman, Erik Rodner, Jeff Donahue, Brian Kulis, & Kate Saenko
    International Journal of Computer Vision (IJCV), Vol. 109, Numbers 1-2, pp. 28-41, 2014.
    [pdf]

  • Metric Learning: A Survey
    Brian Kulis
    Foundations and Trends in Machine Learning, Vol. 5, Number 4, pp. 287--364, 2012.
    [pdf]

  • Kernelized Locality-Sensitive Hashing
    Brian Kulis & Kristen Grauman
    IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 6, pp. 1092--1104, 2012.
    [pdf]

  • Metric and Kernel Learning using a Linear Transformation
    Prateek Jain, Brian Kulis, Jason Davis, & Inderjit Dhillon
    Journal of Machine Learning Research, 13 (Mar): 519--547, 2012.
    [pdf]

  • Fast Similarity Search for Learned Metrics
    Brian Kulis, Prateek Jain, & Kristen Grauman
    IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 12, pp. 2143--2157, 2009.
    [pdf]

  • Low-Rank Kernel Learning with Bregman Matrix Divergences
    Brian Kulis, Matyas Sustik, & Inderjit Dhillon
    Journal of Machine Learning Research, 10 (Feb): 341--376, 2009.
    [pdf]

  • Semi-Supervised Graph Clustering: A Kernel Approach
    Brian Kulis, Sugato Basu, Inderjit Dhillon, & Raymond Mooney
    Machine Learning, vol. 74, no. 1, pp. 1--22, 2009.
    [pdf]

  • Weighted Graph Cuts without Eigenvectors: A Multilevel Approach
    Inderjit Dhillon, Yuqiang Guan, & Brian Kulis
    IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 11, pp. 1944--1957, 2007.
    [pdf] [code]

  • Tracking Evolving Communities in Large Linked Networks
    John Hopcroft, Omar Khan, Brian Kulis, & Bart Selman
    Proc. of the National Academy of Sciences, vol. 101, pp. 5249--5253, April 2004.
    [pdf]

Conference Papers


  • Combinatorial Topic Models using Small-Variance Asymptotics
    Ke Jiang, Suvrit Sra, & Brian Kulis
    In Proc. 20th Intl. AISTATS Conference, 2017.
    [pdf] [Supplementary Material]

  • Robust Monte Carlo Sampling using Riemannian Nose-Poincare Hamiltonian Dynamics
    Anirban Roychowdhury, Brian Kulis, & Srini Parthasarathy
    In Proc. 33rd International Conference on Machine Learning (ICML), 2016.
    [pdf]

  • Revisiting Kernelized Locality-Sensitive Hashing for Improved Large-Scale Image Retrieval
    Ke Jiang, Qicaho Que & Brian Kulis
    In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
    [pdf]
    See arXiv version for full proofs: [arxiv link]

  • A Sufficient Statistics Construction of Exponential Family Levy Measure Densities for Nonparametric Conjugate Models
    Robert Finn & Brian Kulis
    In Proc. 18th Intl. AISTATS Conference, 2015.
    [pdf] [Supplementary Material]

  • Power-Law Graph Cuts
    Xiangyang Zhou, Jiaxin Zhang & Brian Kulis
    In Proc. 18th Intl. AISTATS Conference, 2015.
    [pdf]
    Earlier version presented at the NIPS 2014 Workshop on Networks: From Graphs to Rich Data.

  • Gamma Processes, Stick-Breaking, and Variational Inference
    Anirban Roychowdhury & Brian Kulis
    In Proc. 18th Intl. AISTATS Conference, 2015.
    [pdf] [Supplementary Material]
    Earlier version presented at the NIPS 2014 Workshop on Advances in Variational Inference.

  • Small-Variance Asymptotics for Hidden Markov Models
    Anirban Roychowdhury, Ke Jiang, & Brian Kulis
    In Neural Information Processing Systems (NIPS), 2013.
    [pdf]

  • Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process
    Trevor Campbell, Miao Liu, Brian Kulis, Jonathan How, & Lawrence Carin
    In Neural Information Processing Systems (NIPS), 2013.
    [pdf]

  • MAD-Bayes: MAP-based Asymptotic Derivations from Bayes
    Tamara Broderick, Brian Kulis, & Michael I. Jordan
    In Proc. 30th International Conference on Machine Learning (ICML), 2013.
    [pdf] [Supplementary Material]

  • Small-Variance Asymptotics for Exponential Family Dirichlet Process Mixture Models
    Ke Jiang, Brian Kulis, & Michael I. Jordan
    In Neural Information Processing Systems (NIPS), 2012.
    [pdf]

  • Discovering Latent Domains for Multisource Domain Adaptation
    Judy Hoffman, Brian Kulis, Kate Saenko, & Trevor Darrell
    In Proc. 12th European Conference on Computer Vision (ECCV), 2012.
    [pdf] [Supplementary Material]

  • Revisiting k-means: New Algorithms via Bayesian Nonparametrics
    Brian Kulis & Michael I. Jordan
    In Proc. 29th International Conference on Machine Learning (ICML), 2012.
    [pdf]

  • What You Saw is Not What You Get: Domain Adaptation Using Asymmetric Kernel Transforms
    Brian Kulis, Kate Saenko & Trevor Darrell
    In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011.
    Oral Presentation: 3.5% Acceptance Rate
    [pdf]

  • Metric Learning for Reinforcement Learning Agents
    Matthew E. Taylor, Brian Kulis, & Fei Sha
    In 10th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2011.
    [pdf]

  • Inductive Regularized Learning of Kernel Functions
    Prateek Jain, Brian Kulis, & Inderjit Dhillon
    In Neural Information Processing Systems (NIPS), 2010.
    [pdf]

  • Adapting Visual Category Models to New Domains
    Kate Saenko, Brian Kulis, Mario Fritz & Trevor Darrell
    In Proc. 11th European Conference on Computer Vision (ECCV), 2010.
    [pdf]

  • Implicit Online Learning
    Brian Kulis & Peter Bartlett
    In Proc. 27th International Conference on Machine Learning (ICML), 2010.
    [pdf]

  • Learning to Hash with Binary Reconstructive Embeddings
    Brian Kulis & Trevor Darrell
    In Neural Information Processing Systems (NIPS), 2009.
    [pdf] [code]

  • Kernelized Locality-Sensitive Hashing for Scalable Image Search
    Brian Kulis & Kristen Grauman
    In Proc. 12th International Conference on Computer Vision (ICCV), 2009.
    [pdf] [webpage and code]

  • Convex Perturbations for Scalable Semidefinite Programming
    Brian Kulis, Suvrit Sra, & Inderjit Dhillon
    In Proc. 12th Intl. AISTATS Conference, 2009.
    [pdf]

  • Online Metric Learning and Fast Similarity Search
    Prateek Jain, Brian Kulis, Inderjit Dhillon, & Kristen Grauman
    In Neural Information Processing Systems (NIPS), 2008.
    Oral Presentation: 2.7% Acceptance Rate
    [pdf], Longer version: [pdf]

  • Fast Image Search for Learned Metrics
    Prateek Jain, Brian Kulis, & Kristen Grauman
    In. Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008.
    CVPR 2008 Best Student Paper Award
    [pdf]

  • Information-Theoretic Metric Learning
    Jason Davis, Brian Kulis, Prateek Jain, Suvrit Sra, & Inderjit Dhillon
    In Proc. 24th International Conference on Machine Learning (ICML), 2007.
    ICML 2007 Best Student Paper Award
    [pdf] [code]

  • Fast Low-Rank Semidefinite Programming for Embedding and Clustering
    Brian Kulis, Arun Surendran, & John Platt
    In Proc. 11th Intl. AISTATS Conference, 2007.
    [pdf]

  • Learning Low-Rank Kernel Matrices
    Brian Kulis, Matyas Sustik, & Inderjit Dhillon
    In Proc. 23rd International Conference on Machine Learning (ICML), 2006.
    [pdf]

  • A Fast Kernel-Based Multilevel Algorithm for Graph Clustering
    Inderjit Dhillon, Yuqiang Guan, & Brian Kulis
    In Proc. 11th ACM SIGKDD Intl. Conference on Knowledge Discovery and Data Mining, 2005.
    [pdf] [code]

  • Semi-Supervised Graph Clustering: A Kernel Approach
    Brian Kulis, Sugato Basu, Inderjit Dhillon, & Raymond Mooney
    In Proc. 22nd International Conference on Machine Learning (ICML), 2005.
    ICML 2005 Best Student Paper Award
    [pdf]

  • Kernel k-means, Spectral Clustering, and Normalized Cuts
    Inderjit Dhillon, Yuqiang Guan, & Brian Kulis
    In Proc. 10th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, 2004.
    [pdf]

  • Natural Communities in Large Linked Networks
    John Hopcroft, Omar Khan, Brian Kulis, & Bart Selman
    In Proc. 9th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, 2003.
    [pdf]

Preprints, Workshop Papers, and Technical Reports


  • W-Net: A Deep Model for Fully Unsupervised Image Segmentation
    Xide Xia and Brian Kulis
    Arxiv: 1711.08506, 2017.
    [arxiv link]

  • Stable Distribution Alignment using the Dual of the Adversarial Distance
    Ben Usman, Kate Saenko, and Brian Kulis
    Arxiv: 1707.04046, 2017.
    [arxiv link]

  • Dynamic Clustering Algorithms via Small-Variance Asymptotics of Markov Chain Mixture Models
    Trevor Campbell, Brian Kulis, and Jonathan How
    Arxiv: 1707.08493, 2017.
    [arxiv link]

  • Combinatorial Topic Models using Small-Variance Asymptotics
    Ke Jiang, Suvrit Sra, & Brian Kulis
    Arxiv: 1604:02027, 2016.
    [arxiv link]

  • A Sufficient Statistics Construction of Bayesian Nonparametric Exponential Family Conjugate Models
    Robert Finn & Brian Kulis
    Arxiv: 1601.02257, 2016. (Follow-up to the AISTATS paper, with full proofs and further results on conjugacy.)
    [arxiv link]

  • MAD-Bayes: MAP-based Asymptotic Derivations from Bayes
    Tamara Broderick, Brian Kulis, & Michael I. Jordan
    Arxiv: 1212.2126, 2012.
    [arxiv link]

  • Revisiting k-means: New Algorithms via Bayesian Nonparametrics
    Brian Kulis & Michael I. Jordan
    Arxiv:1111.0352, 2011.
    [arxiv link]

  • Learning to Hash with Binary Reconstructive Embeddings
    Brian Kulis & Trevor Darrell
    UC Berkeley EECS Techical Report #UCB/EECS-2009-101, July, 2009.
    [pdf]

  • Fast Similarity Search for Learned Metrics
    Prateek Jain, Brian Kulis, & Kristen Grauman
    UTCS Techical Report #TR-07-48, September 2007.
    [pdf]

  • Scalable Semidefinite Programming using Convex Perturbations
    Brian Kulis, Suvrit Sra, Stefanie Jegelka, & Inderjit Dhillon
    UTCS Technical Report #TR-07-47, September 2007.
    [pdf]

  • Online Linear Regression using Burg Entropy
    Prateek Jain, Brian Kulis, & Inderjit Dhillon
    UTCS Technical Report #TR-07-08, February 2007.
    [pdf]

  • Information-Theoretic Metric Learning
    Jason Davis, Brian Kulis, Suvrit Sra, & Inderjit Dhillon
    In NIPS 2006 Workshop on Learning to Compare Examples, 2006.
    [pdf]

  • A Unified View of Kernel k-means, Spectral Clustering and Graph Cuts
    Inderjit Dhillon, Yuqiang Guan, & Brian Kulis
    UTCS Technical Report #TR-04-25, July 2004.
    [pdf]