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Variable Skipping for Autoregressive Range Density Estimation

Jul 10, 2020
Eric Liang, Zongheng Yang, Ion Stoica, Pieter Abbeel, Yan Duan, Xi Chen

* ICML 2020. Code released at: https://var-skip.github.io/ 

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NeuroCard: One Cardinality Estimator for All Tables

Jun 15, 2020
Zongheng Yang, Amog Kamsetty, Sifei Luan, Eric Liang, Yan Duan, Xi Chen, Ion Stoica


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Hoplite: Efficient Collective Communication for Task-Based Distributed Systems

Feb 13, 2020
Siyuan Zhuang, Zhuohan Li, Danyang Zhuo, Stephanie Wang, Eric Liang, Robert Nishihara, Philipp Moritz, Ion Stoica


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IMPACT: Importance Weighted Asynchronous Architectures with Clipped Target Networks

Jan 23, 2020
Michael Luo, Jiahao Yao, Richard Liaw, Eric Liang, Ion Stoica

* ICLR 2020 Publication; 14 pages, 10 figures 

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Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules

May 14, 2019
Daniel Ho, Eric Liang, Ion Stoica, Pieter Abbeel, Xi Chen

* ICML 2019 

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Selectivity Estimation with Deep Likelihood Models

May 10, 2019
Zongheng Yang, Eric Liang, Amog Kamsetty, Chenggang Wu, Yan Duan, Xi Chen, Pieter Abbeel, Joseph M. Hellerstein, Sanjay Krishnan, Ion Stoica


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Neural Packet Classification

Feb 27, 2019
Eric Liang, Hang Zhu, Xin Jin, Ion Stoica


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Ray: A Distributed Framework for Emerging AI Applications

Sep 30, 2018
Philipp Moritz, Robert Nishihara, Stephanie Wang, Alexey Tumanov, Richard Liaw, Eric Liang, Melih Elibol, Zongheng Yang, William Paul, Michael I. Jordan, Ion Stoica

* 17 pages, 14 figures, 13th USENIX Symposium on Operating Systems Design and Implementation, 2018 

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Tune: A Research Platform for Distributed Model Selection and Training

Jul 13, 2018
Richard Liaw, Eric Liang, Robert Nishihara, Philipp Moritz, Joseph E. Gonzalez, Ion Stoica

* 8 Pages, Presented at the 2018 ICML AutoML workshop 

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RLlib: Abstractions for Distributed Reinforcement Learning

Jun 29, 2018
Eric Liang, Richard Liaw, Philipp Moritz, Robert Nishihara, Roy Fox, Ken Goldberg, Joseph E. Gonzalez, Michael I. Jordan, Ion Stoica

* Published in the International Conference on Machine Learning (ICML 2018), 10 pages 

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