Robert Nishihara
Impact in
- Artificial Intelligence top 10%
- Reinforcement Learning in Robotics
- Stochastic Gradient Optimization Techniques
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- Distributed and Parallel Computing Systems
- IoT and Edge/Fog Computing
Papers in
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- Distributed and Parallel Computing Systems 2
- Distributed systems and fault tolerance 1
- Advanced Data Storage Technologies 1
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- Cloud Computing and Resource Management 4
- Co-authors
- Philipp Moritz (7 shared papers)Michael I. Jordan (3 shared papers)Ion Stoica (6 shared papers)Richard Liaw (4 shared papers)Eric Liang (3 shared papers)Roy Fox (2 shared papers)Joseph E. Gonzalez (2 shared papers)Ken Goldberg (2 shared papers)
- Journals
- Journal of Machine Learning Research (1 paper)arXiv (Cornell University) (1 paper)International Conference on Artificial Intelligence and Statistics (1 paper)International Conference on Machine Learning (1 paper)
- Partner nations
- United StatesUnited KingdomSwitzerland
In The Last Decade
Robert Nishihara
8 papers receiving 224 citations
Peers
Comparison fields: 5 of 63
- Artificial Intelligence 111
- Computer Networks and Communications 78
- Hardware and Architecture 21
- Statistics and Probability 19
- Information Systems 49
Countries citing papers authored by Robert Nishihara
This map shows the geographic impact of Robert Nishihara's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Robert Nishihara with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Robert Nishihara more than expected).
Fields of papers citing papers by Robert Nishihara
This network shows the impact of papers produced by Robert Nishihara. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Robert Nishihara. The network helps show where Robert Nishihara may publish in the future.
Co-authors
The 18 scholars most cited alongside Robert Nishihara, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | RLlib: Abstractions for Distributed Reinforcement Learning | 2018 | 50 |
| 2 | Ray RLLib: A Composable and Scalable Reinforcement Learning Library | 2017 | 49 |
| 3 | A Linearly-Convergent Stochastic L-BFGS Algorithm | 2016 | 39 |
| 4 | 2017 | 35 | |
| 5 | 2019 | 25 | |
| 6 | 2014 | 24 | |
| 7 | 2021 | 14 | |
| 8 | 2022 | 2 |
About Robert Nishihara
Robert Nishihara is a scholar working on Computer Networks and Communications, Information Systems, Hardware and Architecture, Artificial Intelligence and Computational Mechanics, having authored 8 papers that have together received 238 indexed citations. Recurring topics across this work include Cloud Computing and Resource Management (4 papers), Parallel Computing and Optimization Techniques (3 papers), Distributed and Parallel Computing Systems (2 papers), Distributed systems and fault tolerance (1 paper), Evolutionary Algorithms and Applications (1 paper), Markov Chains and Monte Carlo Methods (1 paper), VLSI and FPGA Design Techniques (1 paper) and Advanced Data Storage Technologies (1 paper). The work is most often cited by research in Artificial Intelligence (111 citations), Computer Networks and Communications (78 citations), Hardware and Architecture (21 citations), Statistics and Probability (19 citations) and Information Systems (49 citations). Robert Nishihara has collaborated with scholars based in United States, United Kingdom and Switzerland. Frequent co-authors include Philipp Moritz, Michael I. Jordan, Ion Stoica, Richard Liaw, Eric Liang, Roy Fox, Joseph E. Gonzalez, Ken Goldberg, Stephanie Wang and Iain Murray. Their work appears in journals such as Journal of Machine Learning Research, arXiv (Cornell University), International Conference on Artificial Intelligence and Statistics and International Conference on Machine Learning.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.