Robert Nishihara

According to our database, Robert Nishihara authored at least 20 papers between 2014 and 2022.

Collaborative distances:

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

2022

ESCHER: expressive scheduling with ephemeral resources.

Romil Bhardwaj, Alexey Tumanov, Stephanie Wang, Richard Liaw, Philipp Moritz, Robert Nishihara, Ion Stoica

Proceedings of the 13th Symposium on Cloud Computing, SoCC 2022, 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

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

2019

On Systems and Algorithms for Distributed Machine Learning.

Robert Nishihara

PhD thesis, 2019

Policy Gradient Search: Online Planning and Expert Iteration without Search Trees.

Thomas Anthony, Robert Nishihara, Philipp Moritz, Tim Salimans, John Schulman

CoRR, 2019

Lineage stash: fault tolerance off the critical path.

Stephanie Wang, John Liagouris, Robert Nishihara, Philipp Moritz, Ujval Misra, Alexey Tumanov, Ion Stoica

Proceedings of the 27th ACM Symposium on Operating Systems Principles, 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

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

Real-Time Machine Learning: The Missing Pieces.

Robert Nishihara, Philipp Moritz, Stephanie Wang, Alexey Tumanov, William Paul, Johann Schleier-Smith, Richard Liaw, Michael I. Jordan, Ion Stoica

CoRR, 2017

Real-Time Machine Learning: The Missing Pieces.

Robert Nishihara, Philipp Moritz, Stephanie Wang, Alexey Tumanov, William Paul, Johann Schleier-Smith, Richard Liaw, Mehrdad Niknami, Michael I. Jordan, Ion Stoica

Proceedings of the 16th Workshop on Hot Topics in Operating Systems, 2017

Discovering Causal Signals in Images.

David Lopez-Paz, Robert Nishihara, Soumith Chintala, Bernhard Schölkopf, Léon Bottou

Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

2016

SparkNet: Training Deep Networks in Spark.

Philipp Moritz, Robert Nishihara, Ion Stoica, Michael I. Jordan

Proceedings of the 4th International Conference on Learning Representations, 2016

No Regret Bound for Extreme Bandits.

Robert Nishihara, David Lopez-Paz, Léon Bottou

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

A Linearly-Convergent Stochastic L-BFGS Algorithm.

Philipp Moritz, Robert Nishihara, Michael I. Jordan

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

A General Analysis of the Convergence of ADMM.

Robert Nishihara, Laurent Lessard, Benjamin Recht, Andrew K. Packard, Michael I. Jordan

Proceedings of the 32nd International Conference on Machine Learning, 2015

2014

Parallel MCMC with generalized elliptical slice sampling.

Robert Nishihara, Iain Murray, Ryan P. Adams

J. Mach. Learn. Res., 2014

On the Convergence Rate of Decomposable Submodular Function Minimization.

Robert Nishihara, Stefanie Jegelka, Michael I. Jordan

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014