Brian Kulis

Publication Activity (10 Years)

Years Active: 2003-2024
Publications (10 Years): 49

Top Topics

Metric Learning

Keyword Spotting

Convex Functions

Top Venues

Publications

Eric Yang Yu, Christopher Liao, Sathvik Ravi, Theodoros Tsiligkaridis, Brian Kulis
Image-Caption Encoding for Improving Zero-Shot Generalization. CoRR (2024)
Alice Baird, Rachel Manzelli, Panagiotis Tzirakis, Chris Gagne, Haoqi Li, Sadie Allen, Sander Dieleman, Brian Kulis, Shrikanth S. Narayanan, Alan Cowen
The NeurIPS 2023 Machine Learning for Audio Workshop: Affective Audio Benchmarks and Novel Data. CoRR (2024)
Zuzhao Ye, Gregory Ciccarelli, Brian Kulis
Maximum-Entropy Adversarial Audio Augmentation for Keyword Spotting. ICASSP (2024)
Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis
A Data Centric Approach for Unsupervised Domain Generalization via Retrieval from Web Scale Multimodal Data. CoRR (2024)
Burak Aksar, Efe Sencan, Benjamin Schwaller, Omar Aaziz, Vitus J. Leung, Jim M. Brandt, Brian Kulis, Manuel Egele, Ayse K. Coskun
Runtime Performance Anomaly Diagnosis in Production HPC Systems Using Active Learning. IEEE Trans. Parallel Distributed Syst. 35 (4) (2024)
Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis
Supervised Metric Learning to Rank for Retrieval via Contextual Similarity Optimization. ICML (2023)
Burak Aksar, Efe Sencan, Benjamin Schwaller, Omar Aaziz, Vitus J. Leung, Jim M. Brandt, Brian Kulis, Manuel Egele, Ayse K. Coskun
Prodigy: Towards Unsupervised Anomaly Detection in Production HPC Systems. SC (2023)
Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis
Descriptor and Word Soups: Overcoming the Parameter Efficiency Accuracy Tradeoff for Out-of-Distribution Few-shot Learning. CoRR (2023)
Christin Jose, Joe Wang, Grant P. Strimel, Mohammad Omar Khursheed, Yuriy Mishchenko, Brian Kulis
Latency Control for Keyword Spotting. INTERSPEECH (2022)
Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis
Supervised Metric Learning for Retrieval via Contextual Similarity Optimization. CoRR (2022)
Christin Jose, Joseph Wang, Grant P. Strimel, Mohammad Omar Khursheed, Yuriy Mishchenko, Brian Kulis
Latency Control for Keyword Spotting. CoRR (2022)
Burak Aksar, Efe Sencan, Benjamin Schwaller, Omar Aaziz, Vitus J. Leung, Jim M. Brandt, Brian Kulis, Ayse K. Coskun
ALBADross: Active Learning Based Anomaly Diagnosis for Production HPC Systems. CLUSTER (2022)
Xiao Wang, Ding Ding, Wei Jiang, Wei Wang, Xiaozhong Xu, Shan Liu, Brian Kulis, Peter Chin
Substitutional Neural Image Compression. PCS (2022)
Ali Siahkamari, Durmus Alp Emre Acar, Christopher Liao, Kelly L. Geyer, Venkatesh Saligrama, Brian Kulis
Faster Algorithms for Learning Convex Functions. ICML (2022)
Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis
Pick up the PACE: Fast and Simple Domain Adaptation via Ensemble Pseudo-Labeling. CoRR (2022)
Xide Xia, Tianfan Xue, Wei-Sheng Lai, Zheng Sun, Abby Chang, Brian Kulis, Jiawen Chen
Real-time Localized Photorealistic Video Style Transfer. WACV (2021)
Minxu Peng, Mertcan Cokbas, Unay Dorken Gallastegi, Prakash Ishwar, Janusz Konrad, Brian Kulis, Vivek K. Goyal
Convolutional Neural Network Denoising of Focused Ion Beam Micrographs. MLSP (2021)
Sivaramakrishnan Sankarapandian, Brian Kulis
$β$-Annealed Variational Autoencoder for glitches. CoRR (2021)
Mohammad Omar Khursheed, Christin Jose, Rajath Kumar, Gengshen Fu, Brian Kulis, Santosh Kumar Cheekatmalla
Tiny-CRNN: Streaming Wakeword Detection In A Low Footprint Setting. CoRR (2021)
Mohammad Omar Khursheed, Christin Jose, Rajath Kumar, Gengshen Fu, Brian Kulis, Santosh Kumar Cheekatmalla
Tiny-CRNN: Streaming Wakeword Detection in a Low Footprint Setting. ASRU (2021)
Xiao Wang, Wei Jiang, Wei Wang, Shan Liu, Brian Kulis, Peter Chin
Substitutional Neural Image Compression. CoRR (2021)
Ali Siahkamari, Durmus Alp Emre Acar, Christopher Liao, Kelly Geyer, Venkatesh Saligrama, Brian Kulis
Faster Convex Lipschitz Regression via 2-block ADMM. CoRR (2021)
Ali Siahkamari, Aditya Gangrade, Brian Kulis, Venkatesh Saligrama
Piecewise Linear Regression via a Difference of Convex Functions. CoRR (2020)
Joe Wang, Rajath Kumar, Mike Rodehorst, Brian Kulis, Shiv Naga Prasad Vitaladevuni
An Audio-Based Wakeword-Independent Verification System. INTERSPEECH (2020)
Rajath Kumar, Mike Rodehorst, Joe Wang, Jiacheng Gu, Brian Kulis
Building a Robust Word-Level Wakeword Verification Network. INTERSPEECH (2020)
Hongyi Liu, Apurva Abhyankar, Yuriy Mishchenko, Thibaud Sénéchal, Gengshen Fu, Brian Kulis, Noah D. Stein, Anish Shah, Shiv Naga Prasad Vitaladevuni
Metadata-Aware End-to-End Keyword Spotting. INTERSPEECH (2020)
Kubra Cilingir, Rachel Manzelli, Brian Kulis
Deep Divergence Learning. CoRR (2020)
Ali Siahkamari, Aditya Gangrade, Brian Kulis, Venkatesh Saligrama
Piecewise Linear Regression via a Difference of Convex Functions. ICML (2020)
Xide Xia, Meng Zhang, Tianfan Xue, Zheng Sun, Hui Fang, Brian Kulis, Jiawen Chen
Joint Bilateral Learning for Real-Time Universal Photorealistic Style Transfer. ECCV (8) (2020)
Ali Siahkamari, Xide Xia, Venkatesh Saligrama, David A. Castañón, Brian Kulis
Learning to Approximate a Bregman Divergence. NeurIPS (2020)
Hatice Kubra Cilingir, Rachel Manzelli, Brian Kulis
Deep Divergence Learning. ICML (2020)
Xide Xia, Meng Zhang, Tianfan Xue, Zheng Sun, Hui Fang, Brian Kulis, Jiawen Chen
Joint Bilateral Learning for Real-time Universal Photorealistic Style Transfer. CoRR (2020)
Xide Xia, Tianfan Xue, Wei-Sheng Lai, Zheng Sun, Abby Chang, Brian Kulis, Jiawen Chen
Real-time Localized Photorealistic Video Style Transfer. CoRR (2020)
Fatih Çakir, Kun He, Xide Xia, Brian Kulis, Stan Sclaroff
Deep Metric Learning to Rank. CVPR (2019)
Xiao Wang, Siyue Wang, Pin-Yu Chen, Yanzhi Wang, Brian Kulis, Xue Lin, Sang Peter Chin
Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses. CoRR (2019)
Ali Siahkamari, Venkatesh Saligrama, David Castanon, Brian Kulis
Learning Bregman Divergences. CoRR (2019)
Xiao Wang, Siyue Wang, Pin-Yu Chen, Yanzhi Wang, Brian Kulis, Xue Lin, Sang Peter Chin
Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses. IJCAI (2019)
Trevor Campbell, Brian Kulis, Jonathan P. How
Dynamic Clustering Algorithms via Small-Variance Analysis of Markov Chain Mixture Models. IEEE Trans. Pattern Anal. Mach. Intell. 41 (6) (2019)
Rachel Manzelli, Vijay Thakkar, Ali Siahkamari, Brian Kulis
Conditioning Deep Generative Raw Audio Models for Structured Automatic Music. ISMIR (2018)
Andrew Cutler, Brian Kulis
Inferring Human Traits from Facebook Statuses. SocInfo (1) (2018)
Andrew Cutler, Brian Kulis
Inferring Human Traits From Facebook Statuses. CoRR (2018)
Rachel Manzelli, Vijay Thakkar, Ali Siahkamari, Brian Kulis
Conditioning Deep Generative Raw Audio Models for Structured Automatic Music. CoRR (2018)
Ben Usman, Kate Saenko, Brian Kulis
Stable Distribution Alignment Using the Dual of the Adversarial Distance. ICLR (Workshop) (2018)
Ke Jiang, Suvrit Sra, Brian Kulis
Combinatorial Topic Models using Small-Variance Asymptotics. AISTATS (2017)
Ben Usman, Kate Saenko, Brian Kulis
Stable Distribution Alignment Using the Dual of the Adversarial Distance. CoRR (2017)
Xide Xia, Brian Kulis
W-Net: A Deep Model for Fully Unsupervised Image Segmentation. CoRR (2017)
Ke Jiang, Suvrit Sra, Brian Kulis
Combinatorial Topic Models using Small-Variance Asymptotics. CoRR (2016)
Robert Finn, Brian Kulis
A Sufficient Statistics Construction of Bayesian Nonparametric Exponential Family Conjugate Models. CoRR (2016)
Anirban Roychowdhury, Brian Kulis, Srinivasan Parthasarathy
Robust Monte Carlo Sampling using Riemannian Nosé-Poincaré Hamiltonian Dynamics. ICML (2016)
Ke Jiang, Qichao Que, Brian Kulis
Revisiting kernelized locality-sensitive hashing for improved large-scale image retrieval. CVPR (2015)
Robert Finn, Brian Kulis
A Sufficient Statistics Construction of Exponential Family Levy Measure Densities for Nonparametric Conjugate Models. AISTATS (2015)
Xiangyang Zhou, Jiaxin Zhang, Brian Kulis
Power-Law Graph Cuts. AISTATS (2015)
Anirban Roychowdhury, Brian Kulis
Gamma Processes, Stick-Breaking, and Variational Inference. AISTATS (2015)
Anirban Roychowdhury, Brian Kulis
Gamma Processes, Stick-Breaking, and Variational Inference. CoRR (2014)
Judy Hoffman, Erik Rodner, Jeff Donahue, Brian Kulis, Kate Saenko
Asymmetric and Category Invariant Feature Transformations for Domain Adaptation. Int. J. Comput. Vis. 109 (1-2) (2014)
Xiangyang Zhou, Jiaxin Zhang, Brian Kulis
Power-Law Graph Cuts. CoRR (2014)
Ke Jiang, Qichao Que, Brian Kulis
Revisiting Kernelized Locality-Sensitive Hashing for Improved Large-Scale Image Retrieval. CoRR (2014)
Trevor Campbell, Miao Liu, Brian Kulis, Jonathan P. How, Lawrence Carin
Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture. NIPS (2013)
Anirban Roychowdhury, Ke Jiang, Brian Kulis
Small-Variance Asymptotics for Hidden Markov Models. NIPS (2013)
Trevor Campbell, Miao Liu, Brian Kulis, Jonathan P. How
Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture CoRR (2013)
Brian Kulis
Metric Learning: A Survey. Found. Trends Mach. Learn. 5 (4) (2013)
Tamara Broderick, Brian Kulis, Michael I. Jordan
MAD-Bayes: MAP-based Asymptotic Derivations from Bayes. ICML (3) (2013)
Judy Hoffman, Brian Kulis, Trevor Darrell, Kate Saenko
Discovering Latent Domains for Multisource Domain Adaptation. ECCV (2) (2012)
Brian Kulis, Kristen Grauman
Kernelized Locality-Sensitive Hashing. IEEE Trans. Pattern Anal. Mach. Intell. 34 (6) (2012)
Brian Kulis, Michael I. Jordan
Revisiting k-means: New Algorithms via Bayesian Nonparametrics. ICML (2012)
Ke Jiang, Brian Kulis, Michael I. Jordan
Small-Variance Asymptotics for Exponential Family Dirichlet Process Mixture Models. NIPS (2012)
Prateek Jain, Brian Kulis, Jason V. Davis, Inderjit S. Dhillon
Metric and Kernel Learning Using a Linear Transformation. J. Mach. Learn. Res. 13 (2012)
Brian Kulis, Michael I. Jordan
Revisiting k-means: New Algorithms via Bayesian Nonparametrics CoRR (2011)
Matthew E. Taylor, Brian Kulis, Fei Sha
Metric learning for reinforcement learning agents. AAMAS (2011)
Brian Kulis, Kate Saenko, Trevor Darrell
What you saw is not what you get: Domain adaptation using asymmetric kernel transforms. CVPR (2011)
Prateek Jain, Brian Kulis, Inderjit S. Dhillon
Inductive Regularized Learning of Kernel Functions. NIPS (2010)
Kate Saenko, Brian Kulis, Mario Fritz, Trevor Darrell
Adapting Visual Category Models to New Domains. ECCV (4) (2010)
Brian Kulis, Peter L. Bartlett
Implicit Online Learning. ICML (2010)
Brian Kulis, Trevor Darrell
Learning to Hash with Binary Reconstructive Embeddings. NIPS (2009)
Brian Kulis, Suvrit Sra, Inderjit S. Dhillon
Convex Perturbations for Scalable Semidefinite Programming. AISTATS (2009)
Brian Kulis, Kristen Grauman
Kernelized locality-sensitive hashing for scalable image search. ICCV (2009)
Brian Kulis, Sugato Basu, Inderjit S. Dhillon, Raymond J. Mooney
Semi-supervised graph clustering: a kernel approach. Mach. Learn. 74 (1) (2009)
Prateek Jain, Brian Kulis, Jason V. Davis, Inderjit S. Dhillon
Metric and Kernel Learning using a Linear Transformation. CoRR (2009)
Brian Kulis, Prateek Jain, Kristen Grauman
Fast Similarity Search for Learned Metrics. IEEE Trans. Pattern Anal. Mach. Intell. 31 (12) (2009)
Brian Kulis, Mátyás A. Sustik, Inderjit S. Dhillon
Low-Rank Kernel Learning with Bregman Matrix Divergences. J. Mach. Learn. Res. 10 (2009)
Prateek Jain, Brian Kulis, Kristen Grauman
Fast image search for learned metrics. CVPR (2008)
Prateek Jain, Brian Kulis, Inderjit S. Dhillon, Kristen Grauman
Online Metric Learning and Fast Similarity Search. NIPS (2008)
Brian Kulis, Arun C. Surendran, John C. Platt
Fast Low-Rank Semidefinite Programming for Embedding and Clustering. AISTATS (2007)
Jason V. Davis, Brian Kulis, Prateek Jain, Suvrit Sra, Inderjit S. Dhillon
Information-theoretic metric learning. ICML (2007)
Inderjit S. Dhillon, Yuqiang Guan, Brian Kulis
Weighted Graph Cuts without Eigenvectors A Multilevel Approach. IEEE Trans. Pattern Anal. Mach. Intell. 29 (11) (2007)
Brian Kulis, Mátyás A. Sustik, Inderjit S. Dhillon
Learning low-rank kernel matrices. ICML (2006)
Inderjit S. Dhillon, Yuqiang Guan, Brian Kulis
A fast kernel-based multilevel algorithm for graph clustering. KDD (2005)
Brian Kulis, Sugato Basu, Inderjit S. Dhillon, Raymond J. Mooney
Semi-supervised graph clustering: a kernel approach. ICML (2005)
Inderjit S. Dhillon, Yuqiang Guan, Brian Kulis
Kernel k-means: spectral clustering and normalized cuts. KDD (2004)
John E. Hopcroft, Omar Khan, Brian Kulis, Bart Selman
Natural communities in large linked networks. KDD (2003)