Citations per year, relative to Richard Liaw Richard Liaw (= 1×)
peers
Roy Fox
Countries citing papers authored by Richard Liaw
Since
Specialization
Citations
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).
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
10 of 10 papers shown
1.
Tumanov, Alexey, Stephanie Wang, Richard Liaw, et al.. (2022). ESCHER. 47–62.2 indexed citations
Liaw, Richard, et al.. (2021). RubberBand. 327–342.12 indexed citations
4.
Liaw, Richard, et al.. (2019). HyperSched. 61–73.29 indexed citations
5.
Liang, Eric, Richard Liaw, Robert Nishihara, et al.. (2018). RLlib: Abstractions for Distributed Reinforcement Learning. International Conference on Machine Learning. 3053–3062.50 indexed citations
6.
Vinitsky, Eugene, Aboudy Kreidieh, Cathy Wu, et al.. (2018). Benchmarks for reinforcement learning in mixed-autonomy traffic. 399–409.38 indexed citations
Laskey, Michael, Jonathan Lee, Richard Liaw, et al.. (2017). Iterative Noise Injection for Scalable Imitation Learning.. arXiv (Cornell University).5 indexed citations
9.
Liang, Eric, Richard Liaw, Robert Nishihara, et al.. (2017). Ray RLLib: A Composable and Scalable Reinforcement Learning Library. arXiv (Cornell University).49 indexed citations
10.
Nishihara, Robert, Philipp Moritz, Stephanie Wang, et al.. (2017). Real-Time Machine Learning. 106–110.35 indexed citations
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.