Kulis, Brian

41 publications

WACV 2025 Image-Caption Encoding for Improving Zero-Shot Generalization Eric Yu, Christopher Liao, Sathvik Ravi, Theodoros Tsiligkaridis, Brian Kulis

ICLR 2025 Multimodal Unsupervised Domain Generalization by Retrieving Across the Modality Gap Christopher Liao, Christian So, Theodoros Tsiligkaridis, Brian Kulis

CVPR 2024 Descriptor and Word Soups: Overcoming the Parameter Efficiency Accuracy Tradeoff for Out-of-Distribution Few-Shot Learning Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis

ICML 2023 Supervised Metric Learning to Rank for Retrieval via Contextual Similarity Optimization Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis

ICML 2022 Faster Algorithms for Learning Convex Functions Ali Siahkamari, Durmus Alp Emre Acar, Christopher Liao, Kelly L Geyer, Venkatesh Saligrama, Brian Kulis

WACV 2021 Real-Time Localized Photorealistic Video Style Transfer Xide Xia, Tianfan Xue, Wei-Sheng Lai, Zheng Sun, Abby Chang, Brian Kulis, Jiawen Chen

ICML 2020 Deep Divergence Learning Hatice Kubra Cilingir, Rachel Manzelli, Brian Kulis

ECCV 2020 Joint Bilateral Learning for Real-Time Universal Photorealistic Style Transfer Xide Xia, Meng Zhang, Tianfan Xue, Zheng Sun, Hui Fang, Brian Kulis, Jiawen Chen

NeurIPS 2020 Learning to Approximate a Bregman Divergence Ali Siahkamari, Xide Xia, Venkatesh Saligrama, David Castañón, Brian Kulis

ICML 2020 Piecewise Linear Regression via a Difference of Convex Functions Ali Siahkamari, Aditya Gangrade, Brian Kulis, Venkatesh Saligrama

IJCAI 2019 Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-Off for Stochastic Defenses Xiao Wang, Siyue Wang, Pin-Yu Chen, Yanzhi Wang, Brian Kulis, Xue Lin, Sang Chin

ICMLW 2019 Prototypical Bregman Networks Kubra Cilingir, Brian Kulis

AISTATS 2017 Combinatorial Topic Models Using Small-Variance Asymptotics Ke Jiang, Suvrit Sra, Brian Kulis

ICML 2016 Robust Monte Carlo Sampling Using Riemannian Nosé-Poincaré Hamiltonian Dynamics Anirban Roychowdhury, Brian Kulis, Srinivasan Parthasarathy

AISTATS 2015 A Sufficient Statistics Construction of Exponential Family Levy Measure Densities for Nonparametric Conjugate Models Robert Finn, Brian Kulis

AISTATS 2015 Gamma Processes, Stick-Breaking, and Variational Inference Anirban Roychowdhury, Brian Kulis

AISTATS 2015 Power-Law Graph Cuts Xiangyang Zhou, Jiaxin Zhang, Brian Kulis

CVPR 2015 Revisiting Kernelized Locality-Sensitive Hashing for Improved Large-Scale Image Retrieval Ke Jiang, Qichao Que, Brian Kulis

NeurIPS 2013 Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture Trevor Campbell, Miao Liu, Brian Kulis, Jonathan P How, Lawrence Carin

ICML 2013 MAD-Bayes: MAP-Based Asymptotic Derivations from Bayes Tamara Broderick, Brian Kulis, Michael Jordan

FnTML 2013 Metric Learning: A Survey Brian Kulis

NeurIPS 2013 Small-Variance Asymptotics for Hidden Markov Models Anirban Roychowdhury, Ke Jiang, Brian Kulis

ECCV 2012 Discovering Latent Domains for Multisource Domain Adaptation Judy Hoffman, Brian Kulis, Trevor Darrell, Kate Saenko

JMLR 2012 Metric and Kernel Learning Using a Linear Transformation Prateek Jain, Brian Kulis, Jason V. Davis, Inderjit S. Dhillon

ICML 2012 Revisiting K-Means: New Algorithms via Bayesian Nonparametrics Brian Kulis, Michael I. Jordan

NeurIPS 2012 Small-Variance Asymptotics for Exponential Family Dirichlet Process Mixture Models Ke Jiang, Brian Kulis, Michael I. Jordan

CVPR 2011 What You Saw Is Not What You Get: Domain Adaptation Using Asymmetric Kernel Transforms Brian Kulis, Kate Saenko, Trevor Darrell

ECCV 2010 Adapting Visual Category Models to New Domains Kate Saenko, Brian Kulis, Mario Fritz, Trevor Darrell

ICML 2010 Implicit Online Learning Brian Kulis, Peter L. Bartlett

NeurIPS 2010 Inductive Regularized Learning of Kernel Functions Prateek Jain, Brian Kulis, Inderjit S. Dhillon

AISTATS 2009 Convex Perturbations for Scalable Semidefinite Programming Brian Kulis, Suvrit Sra, Inderjit Dhillon

ICCV 2009 Kernelized Locality-Sensitive Hashing for Scalable Image Search Brian Kulis, Kristen Grauman

NeurIPS 2009 Learning to Hash with Binary Reconstructive Embeddings Brian Kulis, Trevor Darrell

JMLR 2009 Low-Rank Kernel Learning with Bregman Matrix Divergences Brian Kulis, Mátyás A. Sustik, Inderjit S. Dhillon

MLJ 2009 Semi-Supervised Graph Clustering: A Kernel Approach Brian Kulis, Sugato Basu, Inderjit S. Dhillon, Raymond J. Mooney

CVPR 2008 Fast Image Search for Learned Metrics Prateek Jain, Brian Kulis, Kristen Grauman

NeurIPS 2008 Online Metric Learning and Fast Similarity Search Prateek Jain, Brian Kulis, Inderjit S. Dhillon, Kristen Grauman

AISTATS 2007 Fast Low-Rank Semidefinite Programming for Embedding and Clustering Brian Kulis, Arun C. Surendran, John C. Platt

ICML 2007 Information-Theoretic Metric Learning Jason V. Davis, Brian Kulis, Prateek Jain, Suvrit Sra, Inderjit S. Dhillon

ICML 2006 Learning Low-Rank Kernel Matrices Brian Kulis, Mátyás A. Sustik, Inderjit S. Dhillon

ICML 2005 Semi-Supervised Graph Clustering: A Kernel Approach Brian Kulis, Sugato Basu, Inderjit S. Dhillon, Raymond J. Mooney