Richard Liaw
- Artificial Intelligence top 10%
- Control and Systems Engineering top 10%
- Computer Networks and Communications top 10%
- Information Systems top 10%
- Automotive Engineering
- Co-authors
- Ion StoicaKen GoldbergJoseph E. GonzalezEric LiangRobert NishiharaPhilipp MoritzRoy FoxAlexey Tumanov
- Topics
- Parallel Computing and Optimization Techniques (3 papers)Cloud Computing and Resource Management (3 papers)Machine Learning and Data Classification (3 papers)
- Journals
- The International Journal of Robotics ResearcharXiv (Cornell University)International Conference on Machine Learning
- Partner nations
- United StatesSweden
In The Last Decade
Richard Liaw
10 papers receiving 256 citations
Peers
Comparison fields: 5 of 55
- Artificial Intelligence 113
- Control and Systems Engineering 89
- Computer Networks and Communications 67
- Information Systems 49
- Automotive Engineering 45
Countries citing papers authored by Richard Liaw
This map shows the geographic impact of Richard Liaw'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 Richard Liaw with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Richard Liaw more than expected).
Fields of papers citing papers by Richard Liaw
This network shows the impact of papers produced by Richard Liaw. 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 Richard Liaw. The network helps show where Richard Liaw may publish in the future.
Co-authorship network of co-authors of Richard Liaw
This figure shows the co-authorship network connecting the top 25 collaborators of Richard Liaw. A scholar is included among the top collaborators of Richard Liaw based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Richard Liaw. Richard Liaw is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 2 | |
| 3 | 12 | |
| 4 | 29 | |
| 5 | RLlib: Abstractions for Distributed Reinforcement Learning | 50 |
| 6 | Benchmarks for reinforcement learning in mixed-autonomy traffic | 38 |
| 7 | 42 | |
| 8 | Iterative Noise Injection for Scalable Imitation Learning. | 5 |
| 9 | Ray RLLib: A Composable and Scalable Reinforcement Learning Library | 49 |
| 10 | 35 |
About Richard Liaw
Richard Liaw is a scholar working on Hardware and Architecture, Artificial Intelligence and Information Systems, having authored 10 papers that have together received 264 indexed citations. Recurring topics across this work include Parallel Computing and Optimization Techniques (3 papers), Cloud Computing and Resource Management (3 papers) and Machine Learning and Data Classification (3 papers). The work is most often cited by research in Control and Systems Engineering (89 citations), Automotive Engineering (45 citations) and Hardware and Architecture (25 citations). Richard Liaw has collaborated with scholars based in United States and Sweden. Frequent co-authors include Ion Stoica, Ken Goldberg, Joseph E. Gonzalez, Eric Liang, Robert Nishihara, Philipp Moritz, Roy Fox, Alexey Tumanov, Michael I. Jordan and Animesh Garg. Their work appears in journals such as The International Journal of Robotics Research, arXiv (Cornell University) 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.