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Piecewise Linear Regression via a Difference of Convex Functions

Jul 05, 2020
Ali Siahkamari, Aditya Gangrade, Brian Kulis, Venkatesh Saligrama

* Appearing in ICML2020 Proceedings 

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Deep Divergence Learning

May 06, 2020
Kubra Cilingir, Rachel Manzelli, Brian Kulis

* Under review 

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Joint Bilateral Learning for Real-time Universal Photorealistic Style Transfer

Apr 27, 2020
Xide Xia, Meng Zhang, Tianfan Xue, Zheng Sun, Hui Fang, Brian Kulis, Jiawen Chen

* 16 pages, 10 figures 

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Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses

Aug 20, 2019
Xiao Wang, Siyue Wang, Pin-Yu Chen, Yanzhi Wang, Brian Kulis, Xue Lin, Peter Chin

* Published as Conference Paper @ IJCAI 2019 

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Learning Bregman Divergences

Jun 06, 2019
Ali Siahkamari, Venkatesh Saligrama, David Castanon, Brian Kulis

* 18 pages, 3 figures 

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Conditioning Deep Generative Raw Audio Models for Structured Automatic Music

Jun 26, 2018
Rachel Manzelli, Vijay Thakkar, Ali Siahkamari, Brian Kulis

* Presented at the ISMIR 2018 Conference 

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Stable Distribution Alignment Using the Dual of the Adversarial Distance

Jan 30, 2018
Ben Usman, Kate Saenko, Brian Kulis

* ICLR 2018 Conference Invite to Workshop 

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

Nov 22, 2017
Xide Xia, Brian Kulis


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

Jul 26, 2017
Trevor Campbell, Brian Kulis, Jonathan How

* 27 pages 

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Combinatorial Topic Models using Small-Variance Asymptotics

May 27, 2016
Ke Jiang, Suvrit Sra, Brian Kulis

* 19 pages 

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A Sufficient Statistics Construction of Bayesian Nonparametric Exponential Family Conjugate Models

Jan 10, 2016
Robert Finn, Brian Kulis


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Power-Law Graph Cuts

Nov 25, 2014
Xiangyang Zhou, Jiaxin Zhang, Brian Kulis


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Revisiting Kernelized Locality-Sensitive Hashing for Improved Large-Scale Image Retrieval

Nov 16, 2014
Ke Jiang, Qichao Que, Brian Kulis

* 15 pages 

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Gamma Processes, Stick-Breaking, and Variational Inference

Oct 04, 2014
Anirban Roychowdhury, Brian Kulis


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Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture

Nov 01, 2013
Trevor Campbell, Miao Liu, Brian Kulis, Jonathan P. How, Lawrence Carin

* This paper is from NIPS 2013. Please use the following BibTeX citation: @inproceedings{Campbell13_NIPS, Author = {Trevor Campbell and Miao Liu and Brian Kulis and Jonathan P. How and Lawrence Carin}, Title = {Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process}, Booktitle = {Advances in Neural Information Processing Systems (NIPS)}, Year = {2013}} 

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MAD-Bayes: MAP-based Asymptotic Derivations from Bayes

Feb 15, 2013
Tamara Broderick, Brian Kulis, Michael I. Jordan

* 13 pages, 3 figures 

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Revisiting k-means: New Algorithms via Bayesian Nonparametrics

Jun 14, 2012
Brian Kulis, Michael I. Jordan

* 14 pages. Updated based on the corresponding ICML paper 

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Metric and Kernel Learning using a Linear Transformation

Oct 30, 2009
Prateek Jain, Brian Kulis, Jason V. Davis, Inderjit S. Dhillon


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