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Anytime MiniBatch: Exploiting Stragglers in Online Distributed Optimization


Jun 10, 2020
Nuwan Ferdinand, Haider Al-Lawati, Stark C. Draper, Matthew Nokleby

* Proc. of the 7th Int. Conf. on Learning Representations (ICLR), May 2019, New Orleans, LA, USA 
* International Conference on Learning Representations (ICLR), May 2019, New Orleans, LA, USA 

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Scaling-up Distributed Processing of Data Streams for Machine Learning


May 18, 2020
Matthew Nokleby, Haroon Raja, Waheed U. Bajwa

* Preprint of a journal paper; 45 pages, 9 figures 

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Learning Furniture Compatibility with Graph Neural Networks


Apr 15, 2020
Luisa F. Polania, Mauricio Flores, Yiran Li, Matthew Nokleby

* Accepted for publication at CVPR Workshops 

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An Effective Label Noise Model for DNN Text Classification


Mar 18, 2019
Ishan Jindal, Daniel Pressel, Brian Lester, Matthew Nokleby

* Accepted at NAACL-HLT 2019 Main Conference Long paper 

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Optimizing Taxi Carpool Policies via Reinforcement Learning and Spatio-Temporal Mining


Nov 11, 2018
Ishan Jindal, Zhiwei Qin, Xuewen Chen, Matthew Nokleby, Jieping Ye

* Accepted at IEEE International Conference on Big Data 2018. arXiv admin note: text overlap with arXiv:1710.04350 

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Information Bottleneck Methods for Distributed Learning


Oct 26, 2018
Parinaz Farajiparvar, Ahmad Beirami, Matthew Nokleby


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Tensor Matched Kronecker-Structured Subspace Detection for Missing Information


Oct 25, 2018
Ishan Jindal, Matthew Nokleby


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Stochastic Optimization from Distributed, Streaming Data in Rate-limited Networks


Aug 06, 2018
Matthew Nokleby, Waheed U. Bajwa

* 16 pages, 6 figures; Accepted for publication in IEEE Transactions on Signal and Information Processing over Networks 

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Classification and Representation via Separable Subspaces: Performance Limits and Algorithms


Dec 29, 2017
Ishan Jindal, Matthew Nokleby

* IEEE Journal of Selected Topics in Signal Processing ( Volume: 12 , Issue: 5 , Oct. 2018 ) 
* This paper is submitted to IEEE JSTSP Special Issue on Information-Theoretic Methods in Data Acquisition, Analysis, and Processing 2018 

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Rate-Distortion Bounds on Bayes Risk in Supervised Learning


Nov 17, 2017
Matthew Nokleby, Ahmad Beirami, Robert Calderbank

* Revised submission to IEEE Transactions on Information Theory 

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A Unified Neural Network Approach for Estimating Travel Time and Distance for a Taxi Trip


Oct 12, 2017
Ishan Jindal, Tony, Qin, Xuewen Chen, Matthew Nokleby, Jieping Ye


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Learning Deep Networks from Noisy Labels with Dropout Regularization


May 09, 2017
Ishan Jindal, Matthew Nokleby, Xuewen Chen

* Published at 2016 IEEE 16th International Conference on Data Mining 

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