Eric Liang

According to our database, Eric Liang authored at least 28 papers between 2004 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Exoshuffle-CloudSort.

Frank Sifei Luan, Stephanie Wang, Samyukta Yagati, Sean Kim, Kenneth Lien, Isaac Ong, Tony Hong, SangBin Cho, Eric Liang, Ion Stoica

CoRR, 2023

Exoshuffle: An Extensible Shuffle Architecture.

Frank Sifei Luan, Stephanie Wang, Samyukta Yagati, Sean Kim, Kenneth Lien, Isaac Ong, Tony Hong, SangBin Cho, Eric Liang, Ion Stoica

Proceedings of the ACM SIGCOMM 2023 Conference, 2023

ExoFlow: A Universal Workflow System for Exactly-Once DAGs.

Siyuan Zhuang, Stephanie Wang, Eric Liang, Yi Cheng, Ion Stoica

Proceedings of the 17th USENIX Symposium on Operating Systems Design and Implementation, 2023

2022

Predicting Pedestrian Crosswalk Behavior Using Convolutional Neural Networks.

Eric Liang, Mark Stamp

CoRR, 2022

Exoshuffle: Large-Scale Shuffle at the Application Level.

Frank Sifei Luan, Stephanie Wang, Samyukta Yagati, Sean Kim, Kenneth Lien, SangBin Cho, Eric Liang, Ion Stoica

CoRR, 2022

2021

Hoplite: efficient and fault-tolerant collective communication for task-based distributed systems.

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

Proceedings of the ACM SIGCOMM 2021 Conference, Virtual Event, USA, August 23-27, 2021., 2021

Ownership: A Distributed Futures System for Fine-Grained Tasks.

Stephanie Wang, Eric Liang, Edward Oakes, Benjamin Hindman, Frank Sifei Luan, Audrey Cheng, Ion Stoica

Proceedings of the 18th USENIX Symposium on Networked Systems Design and Implementation, 2021

RLlib Flow: Distributed Reinforcement Learning is a Dataflow Problem.

Eric Liang, Zhanghao Wu, Michael Luo, Sven Mika, Joseph E. Gonzalez, Ion Stoica

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

NeuroCard: One Cardinality Estimator for All Tables.

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

Proc. VLDB Endow., 2020

Distributed Reinforcement Learning is a Dataflow Problem.

Eric Liang, Zhanghao Wu, Michael Luo, Sven Mika, Ion Stoica

CoRR, 2020

Hoplite: Efficient Collective Communication for Task-Based Distributed Systems.

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

CoRR, 2020

Variable Skipping for Autoregressive Range Density Estimation.

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

Proceedings of the 37th International Conference on Machine Learning, 2020

IMPACT: Importance Weighted Asynchronous Architectures with Clipped Target Networks.

Michael Luo, Jiahao Yao, Richard Liaw, Eric Liang, Ion Stoica

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Deep Unsupervised Cardinality Estimation.

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

Proc. VLDB Endow., 2019

Selectivity Estimation with Deep Likelihood Models.

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

CoRR, 2019

Neural packet classification.

Eric Liang, Hang Zhu, Xin Jin, Ion Stoica

Proceedings of the ACM Special Interest Group on Data Communication, 2019

Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules.

Daniel Ho, Eric Liang, Xi Chen, Ion Stoica, Pieter Abbeel

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Tune: A Research Platform for Distributed Model Selection and Training.

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

CoRR, 2018

Ray: A Distributed Framework for Emerging AI Applications.

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

Proceedings of the 13th USENIX Symposium on Operating Systems Design and Implementation, 2018

RLlib: Abstractions for Distributed Reinforcement Learning.

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

Proceedings of the 35th International Conference on Machine Learning, 2018

Benchmarks for reinforcement learning in mixed-autonomy traffic.

Eugene Vinitsky, Aboudy Kreidieh, Luc Le Flem, Nishant Kheterpal, Kathy Jang, Cathy Wu, Fangyu Wu, Richard Liaw, Eric Liang, Alexandre M. Bayen

Proceedings of the 2nd Annual Conference on Robot Learning, 2018

2017

Ray RLLib: A Composable and Scalable Reinforcement Learning Library.

Eric Liang, Richard Liaw, Robert Nishihara, Philipp Moritz, Roy Fox, Joseph Gonzalez, Ken Goldberg, Ion Stoica

CoRR, 2017

Ray: A Distributed Framework for Emerging AI Applications.

Philipp Moritz, Robert Nishihara, Stephanie Wang, Alexey Tumanov, Richard Liaw, Eric Liang, William Paul, Michael I. Jordan, Ion Stoica

CoRR, 2017

2016

SparkR: Scaling R Programs with Spark.

Shivaram Venkataraman, Zongheng Yang, Davies Liu, Eric Liang, Hossein Falaki, Xiangrui Meng, Reynold Xin, Ali Ghodsi, Michael J. Franklin, Ion Stoica, Matei Zaharia

Proceedings of the 2016 International Conference on Management of Data, 2016

2013

Generalized scale independence through incremental precomputation.

Michael Armbrust, Eric Liang, Tim Kraska, Armando Fox, Michael J. Franklin, David A. Patterson

Proceedings of the ACM SIGMOD International Conference on Management of Data, 2013

Structuring a sharded image retrieval database.

Eric Liang, Avideh Zakhor

Proceedings of the Multimedia Content and Mobile Devices 2013, 2013

2011

Location-based image retrieval for urban environments.

Jerry Zhang, Aaron Hallquist, Eric Liang, Avideh Zakhor

Proceedings of the 18th IEEE International Conference on Image Processing, 2011

2004

Autonomous Inverted Helicopter Flight via Reinforcement Learning.

Andrew Y. Ng, Adam Coates, Mark Diel, Varun Ganapathi, Jamie Schulte, Ben Tse, Eric Berger, Eric Liang

Proceedings of the Experimental Robotics IX, 2004