default search action
Brian Kulis
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
- [j10]Burak Aksar
, Efe Sencan
Runtime Performance Anomaly Diagnosis in Production HPC Systems Using Active Learning. IEEE Trans. Parallel Distributed Syst. 35(4): 693-706 (2024) - [c52]Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis:
Descriptor and Word Soups Q: Overcoming the Parameter Efficiency Accuracy Tradeoff for Out-of-Distribution Few-shot Learning. CVPR 2024: 27005-27015 - [c51]Zuzhao Ye, Gregory Ciccarelli, Brian Kulis:
Maximum-Entropy Adversarial Audio Augmentation for Keyword Spotting. ICASSP 2024: 10826-10830 - [i29]Eric Yang Yu, Christopher Liao, Sathvik Ravi, Theodoros Tsiligkaridis, Brian Kulis:
Image-Caption Encoding for Improving Zero-Shot Generalization. CoRR abs/2402.02662 (2024) - [i28]Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis:
A Data Centric Approach for Unsupervised Domain Generalization via Retrieval from Web Scale Multimodal Data. CoRR abs/2402.04416 (2024) - [i27]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 abs/2403.14048 (2024) - 2023
- [c50]Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis:
Supervised Metric Learning to Rank for Retrieval via Contextual Similarity Optimization. ICML 2023: 20906-20938 - [c49]Burak Aksar
Prodigy: Towards Unsupervised Anomaly Detection in Production HPC Systems. SC 2023: 26:1-26:14 - [i26]Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis:
Descriptor and Word Soups: Overcoming the Parameter Efficiency Accuracy Tradeoff for Out-of-Distribution Few-shot Learning. CoRR abs/2311.13612 (2023) - 2022
- [c48]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: 369-380 - [c47]Ali Siahkamari, Durmus Alp Emre Acar, Christopher Liao, Kelly L. Geyer, Venkatesh Saligrama, Brian Kulis:
Faster Algorithms for Learning Convex Functions. ICML 2022: 20176-20194 - [c46]Christin Jose, Joe Wang, Grant P. Strimel, Mohammad Omar Khursheed, Yuriy Mishchenko, Brian Kulis:
Latency Control for Keyword Spotting. INTERSPEECH 2022: 1891-1895 - [c45]Xiao Wang, Ding Ding, Wei Jiang, Wei Wang, Xiaozhong Xu, Shan Liu, Brian Kulis, Peter Chin:
Substitutional Neural Image Compression. PCS 2022: 97-101 - [i25]Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis:
Pick up the PACE: Fast and Simple Domain Adaptation via Ensemble Pseudo-Labeling. CoRR abs/2205.13508 (2022) - [i24]Christin Jose
Latency Control for Keyword Spotting. CoRR abs/2206.07261 (2022) - [i23]Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis:
Supervised Metric Learning for Retrieval via Contextual Similarity Optimization. CoRR abs/2210.01908 (2022) - 2021
- [c44]Mohammad Omar Khursheed, Christin Jose
Tiny-CRNN: Streaming Wakeword Detection in a Low Footprint Setting. ASRU 2021: 541-547 - [c43]Minxu Peng, Mertcan Cokbas, Unay Dorken Gallastegi
Convolutional Neural Network Denoising of Focused Ion Beam Micrographs. MLSP 2021: 1-6 - [c42]Xide Xia, Tianfan Xue, Wei-Sheng Lai, Zheng Sun, Abby Chang, Brian Kulis, Jiawen Chen:
Real-time Localized Photorealistic Video Style Transfer. WACV 2021: 1088-1097 - [i22]Xiao Wang, Wei Jiang, Wei Wang, Shan Liu, Brian Kulis, Peter Chin:
Substitutional Neural Image Compression. CoRR abs/2105.07512 (2021) - [i21]Sivaramakrishnan Sankarapandian, Brian Kulis:
$β$-Annealed Variational Autoencoder for glitches. CoRR abs/2107.10667 (2021) - [i20]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 abs/2109.14725 (2021) - [i19]Ali Siahkamari, Durmus Alp Emre Acar, Christopher Liao, Kelly Geyer, Venkatesh Saligrama, Brian Kulis:
Faster Convex Lipschitz Regression via 2-block ADMM. CoRR abs/2111.01348 (2021) - 2020
- [c41]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: 327-342 - [c40]Hatice Kubra Cilingir, Rachel Manzelli, Brian Kulis:
Deep Divergence Learning. ICML 2020: 2027-2037 - [c39]Ali Siahkamari, Aditya Gangrade, Brian Kulis, Venkatesh Saligrama:
Piecewise Linear Regression via a Difference of Convex Functions. ICML 2020: 8895-8904 - [c38]Joe Wang, Rajath Kumar, Mike Rodehorst, Brian Kulis, Shiv Naga Prasad Vitaladevuni:
An Audio-Based Wakeword-Independent Verification System. INTERSPEECH 2020: 1952-1956 - [c37]Rajath Kumar, Mike Rodehorst, Joe Wang, Jiacheng Gu, Brian Kulis:
Building a Robust Word-Level Wakeword Verification Network. INTERSPEECH 2020: 1972-1976 - [c36]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: 2282-2286 - [c35]Ali Siahkamari, Xide Xia, Venkatesh Saligrama, David A. Castañón, Brian Kulis:
Learning to Approximate a Bregman Divergence. NeurIPS 2020 - [i18]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 abs/2004.10955 (2020) - [i17]Kubra Cilingir, Rachel Manzelli, Brian Kulis:
Deep Divergence Learning. CoRR abs/2005.02612 (2020) - [i16]Ali Siahkamari, Aditya Gangrade, Brian Kulis, Venkatesh Saligrama:
Piecewise Linear Regression via a Difference of Convex Functions. CoRR abs/2007.02422 (2020) - [i15]Xide Xia, Tianfan Xue, Wei-Sheng Lai, Zheng Sun, Abby Chang, Brian Kulis, Jiawen Chen:
Real-time Localized Photorealistic Video Style Transfer. CoRR abs/2010.10056 (2020)
2010 – 2019
- 2019
- [j9]Trevor Campbell
Dynamic Clustering Algorithms via Small-Variance Analysis of Markov Chain Mixture Models. IEEE Trans. Pattern Anal. Mach. Intell. 41(6): 1338-1352 (2019) - [c34]Fatih Çakir, Kun He, Xide Xia, Brian Kulis, Stan Sclaroff:
Deep Metric Learning to Rank. CVPR 2019: 1861-1870 - [c33]Xiao Wang, Siyue Wang, Pin-Yu Chen, Yanzhi Wang, Brian Kulis, Xue Lin, Sang Chin:
Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses. IJCAI 2019: 6013-6019 - [i14]Ali Siahkamari, Venkatesh Saligrama, David Castanon, Brian Kulis:
Learning Bregman Divergences. CoRR abs/1905.11545 (2019) - [i13]Xiao Wang, Siyue Wang, Pin-Yu Chen, Yanzhi Wang, Brian Kulis, Xue Lin, Peter Chin:
Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses. CoRR abs/1908.07116 (2019) - 2018
- [c32]Ben Usman, Kate Saenko, Brian Kulis:
Stable Distribution Alignment Using the Dual of the Adversarial Distance. ICLR (Workshop) 2018 - [c31]Rachel Manzelli, Vijay Thakkar, Ali Siahkamari, Brian Kulis:
Conditioning Deep Generative Raw Audio Models for Structured Automatic Music. ISMIR 2018: 182-189 - [c30]Andrew Cutler
Inferring Human Traits from Facebook Statuses. SocInfo (1) 2018: 167-195 - [i12]Andrew Cutler, Brian Kulis:
Inferring Human Traits From Facebook Statuses. CoRR abs/1805.08718 (2018) - [i11]Rachel Manzelli, Vijay Thakkar, Ali Siahkamari, Brian Kulis:
Conditioning Deep Generative Raw Audio Models for Structured Automatic Music. CoRR abs/1806.09905 (2018) - 2017
- [c29]Ke Jiang, Suvrit Sra, Brian Kulis:
Combinatorial Topic Models using Small-Variance Asymptotics. AISTATS 2017: 421-429 - [i10]Ben Usman, Kate Saenko, Brian Kulis:
Stable Distribution Alignment Using the Dual of the Adversarial Distance. CoRR abs/1707.04046 (2017) - [i9]Xide Xia, Brian Kulis:
W-Net: A Deep Model for Fully Unsupervised Image Segmentation. CoRR abs/1711.08506 (2017) - 2016
- [c28]Anirban Roychowdhury, Brian Kulis, Srinivasan Parthasarathy:
Robust Monte Carlo Sampling using Riemannian Nosé-Poincaré Hamiltonian Dynamics. ICML 2016: 2673-2681 - [i8]Robert Finn, Brian Kulis:
A Sufficient Statistics Construction of Bayesian Nonparametric Exponential Family Conjugate Models. CoRR abs/1601.02257 (2016) - [i7]Ke Jiang, Suvrit Sra, Brian Kulis:
Combinatorial Topic Models using Small-Variance Asymptotics. CoRR abs/1604.02027 (2016) - 2015
- [c27]Robert Finn, Brian Kulis:
A Sufficient Statistics Construction of Exponential Family Levy Measure Densities for Nonparametric Conjugate Models. AISTATS 2015 - [c26]Anirban Roychowdhury, Brian Kulis:
Gamma Processes, Stick-Breaking, and Variational Inference. AISTATS 2015 - [c25]Xiangyang Zhou, Jiaxin Zhang, Brian Kulis:
Power-Law Graph Cuts. AISTATS 2015 - [c24]Ke Jiang, Qichao Que, Brian Kulis:
Revisiting kernelized locality-sensitive hashing for improved large-scale image retrieval. CVPR 2015: 4933-4941 - 2014
- [j8]Judy Hoffman
Asymmetric and Category Invariant Feature Transformations for Domain Adaptation. Int. J. Comput. Vis. 109(1-2): 28-41 (2014) - [i6]Anirban Roychowdhury, Brian Kulis:
Gamma Processes, Stick-Breaking, and Variational Inference. CoRR abs/1410.1068 (2014) - [i5]Xiangyang Zhou, Jiaxin Zhang, Brian Kulis:
Power-Law Graph Cuts. CoRR abs/1411.1971 (2014) - [i4]Ke Jiang, Qichao Que, Brian Kulis:
Revisiting Kernelized Locality-Sensitive Hashing for Improved Large-Scale Image Retrieval. CoRR abs/1411.4199 (2014) - 2013
- [j7]Brian Kulis:
Metric Learning: A Survey. Found. Trends Mach. Learn. 5(4): 287-364 (2013) - [c23]Tamara Broderick, Brian Kulis, Michael I. Jordan:
MAD-Bayes: MAP-based Asymptotic Derivations from Bayes. ICML (3) 2013: 226-234 - [c22]Trevor Campbell, Miao Liu, Brian Kulis, Jonathan P. How, Lawrence Carin:
Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture. NIPS 2013: 449-457 - [c21]Anirban Roychowdhury, Ke Jiang, Brian Kulis:
Small-Variance Asymptotics for Hidden Markov Models. NIPS 2013: 2103-2111 - [i3]Trevor Campbell, Miao Liu, Brian Kulis, Jonathan P. How:
Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture. CoRR abs/1305.6659 (2013) - 2012
- [j6]Prateek Jain, Brian Kulis, Jason V. Davis, Inderjit S. Dhillon:
Metric and Kernel Learning Using a Linear Transformation. J. Mach. Learn. Res. 13: 519-547 (2012) - [j5]Brian Kulis, Kristen Grauman:
Kernelized Locality-Sensitive Hashing. IEEE Trans. Pattern Anal. Mach. Intell. 34(6): 1092-1104 (2012) - [c20]Judy Hoffman, Brian Kulis, Trevor Darrell, Kate Saenko:
Discovering Latent Domains for Multisource Domain Adaptation. ECCV (2) 2012: 702-715 - [c19]Brian Kulis, Michael I. Jordan:
Revisiting k-means: New Algorithms via Bayesian Nonparametrics. ICML 2012 - [c18]Ke Jiang, Brian Kulis, Michael I. Jordan:
Small-Variance Asymptotics for Exponential Family Dirichlet Process Mixture Models. NIPS 2012: 3167-3175 - 2011
- [c17]Matthew E. Taylor, Brian Kulis, Fei Sha:
Metric learning for reinforcement learning agents. AAMAS 2011: 777-784 - [c16]Brian Kulis, Kate Saenko, Trevor Darrell:
What you saw is not what you get: Domain adaptation using asymmetric kernel transforms. CVPR 2011: 1785-1792 - [i2]Brian Kulis, Michael I. Jordan:
Revisiting k-means: New Algorithms via Bayesian Nonparametrics. CoRR abs/1111.0352 (2011) - 2010
- [c15]Kate Saenko, Brian Kulis, Mario Fritz, Trevor Darrell:
Adapting Visual Category Models to New Domains. ECCV (4) 2010: 213-226 - [c14]Brian Kulis, Peter L. Bartlett:
Implicit Online Learning. ICML 2010: 575-582 - [c13]Prateek Jain, Brian Kulis, Inderjit S. Dhillon:
Inductive Regularized Learning of Kernel Functions. NIPS 2010: 946-954
2000 – 2009
- 2009
- [j4]Brian Kulis, Mátyás A. Sustik, Inderjit S. Dhillon:
Low-Rank Kernel Learning with Bregman Matrix Divergences. J. Mach. Learn. Res. 10: 341-376 (2009) - [j3]Brian Kulis, Sugato Basu, Inderjit S. Dhillon, Raymond J. Mooney:
Semi-supervised graph clustering: a kernel approach. Mach. Learn. 74(1): 1-22 (2009) - [j2]Brian Kulis, Prateek Jain, Kristen Grauman:
Fast Similarity Search for Learned Metrics. IEEE Trans. Pattern Anal. Mach. Intell. 31(12): 2143-2157 (2009) - [c12]Brian Kulis, Kristen Grauman:
Kernelized locality-sensitive hashing for scalable image search. ICCV 2009: 2130-2137 - [c11]Brian Kulis, Trevor Darrell:
Learning to Hash with Binary Reconstructive Embeddings. NIPS 2009: 1042-1050 - [c10]Brian Kulis, Suvrit Sra, Inderjit S. Dhillon:
Convex Perturbations for Scalable Semidefinite Programming. AISTATS 2009: 296-303 - [i1]Prateek Jain, Brian Kulis, Jason V. Davis, Inderjit S. Dhillon:
Metric and Kernel Learning using a Linear Transformation. CoRR abs/0910.5932 (2009) - 2008
- [c9]Prateek Jain, Brian Kulis, Kristen Grauman:
Fast image search for learned metrics. CVPR 2008 - [c8]Prateek Jain, Brian Kulis, Inderjit S. Dhillon, Kristen Grauman:
Online Metric Learning and Fast Similarity Search. NIPS 2008: 761-768 - 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) - [c7]Jason V. Davis, Brian Kulis, Prateek Jain, Suvrit Sra
Information-theoretic metric learning. ICML 2007: 209-216 - [c6]Brian Kulis, Arun C. Surendran, John C. Platt:
Fast Low-Rank Semidefinite Programming for Embedding and Clustering. AISTATS 2007: 235-242 - 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
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from ,
, and
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and
to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from .
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-10-08 21:32 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by: