Brian Kulis

According to our database, Brian Kulis authored at least 73 papers between 2003 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Runtime Performance Anomaly Diagnosis in Production HPC Systems Using Active Learning.

Burak Aksar, Efe Sencan, Benjamin Schwaller, Omar Aaziz, Vitus J. Leung, Jim M. Brandt, Brian Kulis, Manuel Egele, Ayse K. Coskun

IEEE Trans. Parallel Distributed Syst., April, 2024

The NeurIPS 2023 Machine Learning for Audio Workshop: Affective Audio Benchmarks and Novel Data.

Alice Baird, Rachel Manzelli, Panagiotis Tzirakis, Chris Gagne, Haoqi Li, Sadie Allen, Sander Dieleman, Brian Kulis, Shrikanth S. Narayanan, Alan Cowen

CoRR, 2024

A Data Centric Approach for Unsupervised Domain Generalization via Retrieval from Web Scale Multimodal Data.

Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis

CoRR, 2024

Image-Caption Encoding for Improving Zero-Shot Generalization.

Eric Yang Yu, Christopher Liao, Sathvik Ravi, Theodoros Tsiligkaridis, Brian Kulis

CoRR, 2024

2023

Descriptor and Word Soups: Overcoming the Parameter Efficiency Accuracy Tradeoff for Out-of-Distribution Few-shot Learning.

Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis

CoRR, 2023

Prodigy: Towards Unsupervised Anomaly Detection in Production HPC Systems.

Burak Aksar, Efe Sencan, Benjamin Schwaller, Omar Aaziz, Vitus J. Leung, Jim M. Brandt, Brian Kulis, Manuel Egele, Ayse K. Coskun

Proceedings of the International Conference for High Performance Computing, 2023

Supervised Metric Learning to Rank for Retrieval via Contextual Similarity Optimization.

Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis

Proceedings of the International Conference on Machine Learning, 2023

2022

Supervised Metric Learning for Retrieval via Contextual Similarity Optimization.

Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis

CoRR, 2022

Latency Control for Keyword Spotting.

Christin Jose, Joseph Wang, Grant P. Strimel, Mohammad Omar Khursheed, Yuriy Mishchenko, Brian Kulis

CoRR, 2022

Pick up the PACE: Fast and Simple Domain Adaptation via Ensemble Pseudo-Labeling.

Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis

CoRR, 2022

Substitutional Neural Image Compression.

Xiao Wang, Ding Ding, Wei Jiang, Wei Wang, Xiaozhong Xu, Shan Liu, Brian Kulis, Peter Chin

Proceedings of the Picture Coding Symposium, 2022

Latency Control for Keyword Spotting.

Christin Jose, Joe Wang, Grant P. Strimel, Mohammad Omar Khursheed, Yuriy Mishchenko, Brian Kulis

Proceedings of the Interspeech 2022, 2022

Faster Algorithms for Learning Convex Functions.

Ali Siahkamari, Durmus Alp Emre Acar, Christopher Liao, Kelly L. Geyer, Venkatesh Saligrama, Brian Kulis

Proceedings of the International Conference on Machine Learning, 2022

ALBADross: Active Learning Based Anomaly Diagnosis for Production HPC Systems.

Burak Aksar, Efe Sencan, Benjamin Schwaller, Omar Aaziz, Vitus J. Leung, Jim M. Brandt, Brian Kulis, Ayse K. Coskun

Proceedings of the IEEE International Conference on Cluster Computing, 2022

2021

Faster Convex Lipschitz Regression via 2-block ADMM.

Ali Siahkamari, Durmus Alp Emre Acar, Christopher Liao, Kelly Geyer, Venkatesh Saligrama, Brian Kulis

CoRR, 2021

$β$-Annealed Variational Autoencoder for glitches.

Sivaramakrishnan Sankarapandian, Brian Kulis

CoRR, 2021

Substitutional Neural Image Compression.

Xiao Wang, Wei Jiang, Wei Wang, Shan Liu, Brian Kulis, Peter Chin

CoRR, 2021

Real-time Localized Photorealistic Video Style Transfer.

Xide Xia, Tianfan Xue, Wei-Sheng Lai, Zheng Sun, Abby Chang, Brian Kulis, Jiawen Chen

Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2021

Convolutional Neural Network Denoising of Focused Ion Beam Micrographs.

Minxu Peng, Mertcan Cokbas, Unay Dorken Gallastegi, Prakash Ishwar, Janusz Konrad, Brian Kulis, Vivek K. Goyal

Proceedings of the 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP), 2021

Tiny-CRNN: Streaming Wakeword Detection in a Low Footprint Setting.

Mohammad Omar Khursheed, Christin Jose, Rajath Kumar, Gengshen Fu, Brian Kulis, Santosh Kumar Cheekatmalla

Proceedings of the IEEE Automatic Speech Recognition and Understanding Workshop, 2021

2020

Learning to Approximate a Bregman Divergence.

Ali Siahkamari, Xide Xia, Venkatesh Saligrama, David A. Castañón, Brian Kulis

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

An Audio-Based Wakeword-Independent Verification System.

Joe Wang, Rajath Kumar, Mike Rodehorst, Brian Kulis, Shiv Naga Prasad Vitaladevuni

Proceedings of the Interspeech 2020, 2020

Building a Robust Word-Level Wakeword Verification Network.

Rajath Kumar, Mike Rodehorst, Joe Wang, Jiacheng Gu, Brian Kulis

Proceedings of the Interspeech 2020, 2020

Metadata-Aware End-to-End Keyword Spotting.

Hongyi Liu, Apurva Abhyankar, Yuriy Mishchenko, Thibaud Sénéchal, Gengshen Fu, Brian Kulis, Noah D. Stein, Anish Shah, Shiv Naga Prasad Vitaladevuni

Proceedings of the Interspeech 2020, 2020

Piecewise Linear Regression via a Difference of Convex Functions.

Ali Siahkamari, Aditya Gangrade, Brian Kulis, Venkatesh Saligrama

Proceedings of the 37th International Conference on Machine Learning, 2020

Deep Divergence Learning.

Hatice Kubra Cilingir, Rachel Manzelli, Brian Kulis

Proceedings of the 37th International Conference on Machine Learning, 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

Proceedings of the Computer Vision - ECCV 2020, 2020

2019

Dynamic Clustering Algorithms via Small-Variance Analysis of Markov Chain Mixture Models.

Trevor Campbell, Brian Kulis, Jonathan P. How

IEEE Trans. Pattern Anal. Mach. Intell., 2019

Learning Bregman Divergences.

Ali Siahkamari, Venkatesh Saligrama, David Castanon, Brian Kulis

CoRR, 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

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Deep Metric Learning to Rank.

Fatih Çakir, Kun He, Xide Xia, Brian Kulis, Stan Sclaroff

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018

Inferring Human Traits from Facebook Statuses.

Andrew Cutler, Brian Kulis

Proceedings of the Social Informatics, 2018

Conditioning Deep Generative Raw Audio Models for Structured Automatic Music.

Rachel Manzelli, Vijay Thakkar, Ali Siahkamari, Brian Kulis

Proceedings of the 19th International Society for Music Information Retrieval Conference, 2018

Stable Distribution Alignment Using the Dual of the Adversarial Distance.

Ben Usman, Kate Saenko, Brian Kulis

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

W-Net: A Deep Model for Fully Unsupervised Image Segmentation.

Xide Xia, Brian Kulis

CoRR, 2017

Combinatorial Topic Models using Small-Variance Asymptotics.

Ke Jiang, Suvrit Sra, Brian Kulis

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

A Sufficient Statistics Construction of Bayesian Nonparametric Exponential Family Conjugate Models.

Robert Finn, Brian Kulis

CoRR, 2016

Robust Monte Carlo Sampling using Riemannian Nosé-Poincaré Hamiltonian Dynamics.

Anirban Roychowdhury, Brian Kulis, Srinivasan Parthasarathy

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Revisiting kernelized locality-sensitive hashing for improved large-scale image retrieval.

Ke Jiang, Qichao Que, Brian Kulis

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015

Power-Law Graph Cuts.

Xiangyang Zhou, Jiaxin Zhang, Brian Kulis

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

Gamma Processes, Stick-Breaking, and Variational Inference.

Anirban Roychowdhury, Brian Kulis

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

A Sufficient Statistics Construction of Exponential Family Levy Measure Densities for Nonparametric Conjugate Models.

Robert Finn, Brian Kulis

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014

Asymmetric and Category Invariant Feature Transformations for Domain Adaptation.

Judy Hoffman, Erik Rodner, Jeff Donahue, Brian Kulis, Kate Saenko

Int. J. Comput. Vis., 2014

2013

Metric Learning: A Survey.

Brian Kulis

Found. Trends Mach. Learn., 2013

Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture

Trevor Campbell, Miao Liu, Brian Kulis, Jonathan P. How

CoRR, 2013

Small-Variance Asymptotics for Hidden Markov Models.

Anirban Roychowdhury, Ke Jiang, Brian Kulis

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture.

Trevor Campbell, Miao Liu, Brian Kulis, Jonathan P. How, Lawrence Carin

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

MAD-Bayes: MAP-based Asymptotic Derivations from Bayes.

Tamara Broderick, Brian Kulis, Michael I. Jordan

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

Kernelized Locality-Sensitive Hashing.

Brian Kulis, Kristen Grauman

IEEE Trans. Pattern Anal. Mach. Intell., 2012

Metric and Kernel Learning Using a Linear Transformation.

Prateek Jain, Brian Kulis, Jason V. Davis, Inderjit S. Dhillon

J. Mach. Learn. Res., 2012

Small-Variance Asymptotics for Exponential Family Dirichlet Process Mixture Models.

Ke Jiang, Brian Kulis, Michael I. Jordan

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Revisiting k-means: New Algorithms via Bayesian Nonparametrics.

Brian Kulis, Michael I. Jordan

Proceedings of the 29th International Conference on Machine Learning, 2012

Discovering Latent Domains for Multisource Domain Adaptation.

Judy Hoffman, Brian Kulis, Trevor Darrell, Kate Saenko

Proceedings of the Computer Vision - ECCV 2012, 2012

2011

What you saw is not what you get: Domain adaptation using asymmetric kernel transforms.

Brian Kulis, Kate Saenko, Trevor Darrell

Proceedings of the 24th IEEE Conference on Computer Vision and Pattern Recognition, 2011

Metric learning for reinforcement learning agents.

Matthew E. Taylor, Brian Kulis, Fei Sha

Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011), 2011

2010

Inductive Regularized Learning of Kernel Functions.

Prateek Jain, Brian Kulis, Inderjit S. Dhillon

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Implicit Online Learning.

Brian Kulis, Peter L. Bartlett

Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

Adapting Visual Category Models to New Domains.

Kate Saenko, Brian Kulis, Mario Fritz, Trevor Darrell

Proceedings of the Computer Vision, 2010

2009

Fast Similarity Search for Learned Metrics.

Brian Kulis, Prateek Jain, Kristen Grauman

IEEE Trans. Pattern Anal. Mach. Intell., 2009

Semi-supervised graph clustering: a kernel approach.

Brian Kulis, Sugato Basu, Inderjit S. Dhillon, Raymond J. Mooney

Mach. Learn., 2009

Convex Perturbations for Scalable Semidefinite Programming.

Brian Kulis, Suvrit Sra, Inderjit S. Dhillon

Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

Low-Rank Kernel Learning with Bregman Matrix Divergences.

Brian Kulis, Mátyás A. Sustik, Inderjit S. Dhillon

J. Mach. Learn. Res., 2009

Learning to Hash with Binary Reconstructive Embeddings.

Brian Kulis, Trevor Darrell

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

Kernelized locality-sensitive hashing for scalable image search.

Brian Kulis, Kristen Grauman

Proceedings of the IEEE 12th International Conference on Computer Vision, ICCV 2009, Kyoto, Japan, September 27, 2009

2008

Online Metric Learning and Fast Similarity Search.

Prateek Jain, Brian Kulis, Inderjit S. Dhillon, Kristen Grauman

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Fast image search for learned metrics.

Prateek Jain, Brian Kulis, Kristen Grauman

Proceedings of the 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008), 2008

2007

Weighted Graph Cuts without Eigenvectors A Multilevel Approach.

Inderjit S. Dhillon, Yuqiang Guan, Brian Kulis

IEEE Trans. Pattern Anal. Mach. Intell., 2007

Fast Low-Rank Semidefinite Programming for Embedding and Clustering.

Brian Kulis, Arun C. Surendran, John C. Platt

Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007

Information-theoretic metric learning.

Jason V. Davis, Brian Kulis, Prateek Jain, Suvrit Sra, Inderjit S. Dhillon

Proceedings of the Machine Learning, 2007

2006

Learning low-rank kernel matrices.

Brian Kulis, Mátyás A. Sustik, Inderjit S. Dhillon

Proceedings of the Machine Learning, 2006

2005

A fast kernel-based multilevel algorithm for graph clustering.

Inderjit S. Dhillon, Yuqiang Guan, Brian Kulis

Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2005

2004

Kernel k-means: spectral clustering and normalized cuts.

Inderjit S. Dhillon, Yuqiang Guan, Brian Kulis

Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2004

2003

Natural communities in large linked networks.

John E. Hopcroft, Omar Khan, Brian Kulis, Bart Selman

Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 24, 2003