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Research

A Collection of Our Publications and Presentations

Background

oPRO.ai is built on years of groundbreaking work on machine learning and distributed computing. 

Publications

Overview

oPRO.ai: A New Platform for Distributed Machine Learning on Big Data

Eric P. Xing, Qirong Ho, Wei Dai, Jin Kyu Kim, Jinliang Wei, Seunghak Lee, Xun Zheng, Pengtao Xie, Abhimanu Kumar, Yaoliang Yu

IEEE Transactions on Big Data, Volume 1, No. 2, Pages 49-67, 2015

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Systems

Strategies and Principles of Distributed Machine Learning on Big Data

Eric P. Xing, Qirong Ho, Pengtao Xie, Wei Dai

Engineering, Volume 2, No. 2, Pages 179-95, 2016

Orpheus: Efficient Distributed Machine Learning via System and Algorithm Co-design

Pengtao Xie, Jin-Kyu Kim, Qirong Ho, Yaoliang Yu, Eric P. Xing

Symposium of Cloud Computing (SoCC 2018)

Poseidon: An Efficient Communication Interface for Distributed Deep Learning on GPU Clusters

Hao Zhang, Zeyu Zheng, Wei Dai, Qirong Ho, Eric P. Xing

USENIX Annual Technical Conference (ATC 2017)

STRADS: A Distributed Framework for Scheduled Model Parallel Machine Learning

Jin Kyu Kim, Qirong Ho, Seunghak Lee, Xun Zheng, Wei Dai, Garth A. Gibson, Eric P. Xing

European Conference on Computer Systems (Eurosys 2016)

Managed Communication and Consistency for Fast Data-Parallel Iterative Analytics

Jinliang Wei, Wei Dai, Aurick Qiao, Henggang Cui, Qirong Ho, Gregory R. Ganger, Phillip B. Gibbons, Garth A. Gibson, Eric P. Xing

ACM Symposium on Cloud Computing (SOCC 2015), Best paper award.

More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server

Qirong Ho, James Cipar, Henggang Cui, Jin Kyu Kim, Seunghak Lee, Phillip B. Gib

Neural Information Processing Systems (NIPS 2013), Oral (top 5%)

On Model Parallelization and Scheduling Strategies for Distributed Machine Learning

Seunghak Lee, Jin Kyu Kim, Xun Zheng, Qirong Ho, Garth A. Gibson, Eric P. Xing

Neural Information Processing Systems (NIPS 2014)

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Theory

On Convergence of Model Parallel Proximal Gradient Algorithm for Stale Synchronous Parallel System

Yi Zhou, Yaoliang Yu, Wei Dai, Yingbin Liang, Eric P. Xing

Artificial Intelligence and Statistics (AISTATS 2016)

Analysis of High-Performance Distributed ML at Scale Through Parameter Server Consistency Models

Wei Dai, Abhimanu Kumar, Jinliang Wei, Qirong Ho, Garth A. Gibson, Eric P. Xing

AAAI Conference on Artificial Intelligence (AAAI 2015)

Lighter-Communication Distributed Machine Learning via Sufficient Factor Broadcasting

Pengtao Xie, Jin Kyu Kim, Yi Zhou, Qirong Ho, Abhimanu Kumar, Yaoliang Yu, Eric P. Xing

Uncertainty in Artificial Intelligence (UAI 2016)

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Other Applications

Harnessing Deep Neural Networks with Logic Rules

Zhiting Hu, Xuezhe Ma, Zhengzhong Liu, Eduard Hovy, Eric P. Xing

Annual Meeting of the Association for Computational Linguistics (ACL 2016) | Outstanding Paper Award.

Learning Answer-Entailing Structures for Machine Comprehension

Mrinmaya Sachan, Avinava Dubey, Matthew Richardson, Eric P. Xing

Annual Meeting of the Association for Computational Linguistics (ACL 2015) | Honorable Mention Recipient.

ZM-Net: Real-time Zero-shot Image Manipulation Network

Hao Wang, Xiaodan Liang, Hao Zhang, Dit-Yan Yeung, Eric P. Xing

Currently under submission.

Diversity-Promoting Bayesian Learning of Latent Variable Models

Pengtao Xie, Jun Zhu, Eric P. Xing

International Conference on Machine Learning (ICML 2016)

Science Question Answering Using Instructional Materials

Mrinmaya Sachan, Eric P. Xing

Annual Meeting of the Association for Computational Linguistics (ACL 2016)

Learning Concept Taxonomies from Multi-Modal Data

Hao Zhang, Zhiting Hu, Yuntian Deng, Mrinmaya Sachan, Zhicheng Yan, Eric P. Xing

Annual Meeting of the Association for Computational Linguistics (ACL 2016)

Grounding Topic Models with Knowledge Bases

Zhiting Hu, Gang Luo, Mrinmaya Sachan, Zaiqing Nie, Eric P. Xing

International Joint Conference on Artificial Intelligence (IJCAI 2016)

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Presentations

A Statistical Machine Learning Perspective of Deep Learning: Algorithm, Theory, Scalable Computing

Eric P. Xing

International Summer School on Deep Learning, Bilbao, Spain

System and Algorithm Co-Design, Theory and Practice, for Distributed Machine Learning

Eric P. Xing

Simons Institute for the Theory of Computing

High Efficiency Systems for Distributed AI and ML at Scale

Qirong Ho

Strata+Hadoop World in Singapore, 2016

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