Kai Xiao
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering
- Control and Systems Engineering
- Signal Processing
- Co-authors
- Russ TedrakeAleksander MądryNur Muhammad Mahi ShafiullahSven GowalKrishnamurthy DvijothamPushmeet KohliJonathan UesatoTsui-Wei Weng
- Topics
- Adversarial Robustness in Machine Learning (4 papers)Scheduling and Optimization Algorithms (1 paper)Optimization and Search Problems (1 paper)
- Journals
- Graphs and CombinatoricsarXiv (Cornell University)DSpace@MIT (Massachusetts Institute of Technology)
- Partner nations
- United StatesBelgiumUnited Kingdom
In The Last Decade
Kai Xiao
5 papers receiving 131 citations
Peers
Comparison fields: 5 of 32
- Artificial Intelligence 119
- Computer Vision and Pattern Recognition 34
- Electrical and Electronic Engineering 29
- Control and Systems Engineering 19
- Signal Processing 17
Countries citing papers authored by Kai Xiao
This map shows the geographic impact of Kai Xiao'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 Kai Xiao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kai Xiao more than expected).
Fields of papers citing papers by Kai Xiao
This network shows the impact of papers produced by Kai Xiao. 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 Kai Xiao. The network helps show where Kai Xiao may publish in the future.
Co-authorship network of co-authors of Kai Xiao
This figure shows the co-authorship network connecting the top 25 collaborators of Kai Xiao. A scholar is included among the top collaborators of Kai Xiao 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 Kai Xiao. Kai Xiao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Toward Evaluating Robustness of Deep Reinforcement Learning with Continuous Control | 8 |
| 2 | A FRAMEWORK FOR ROBUSTNESS CERTIFICATION OF SMOOTHED CLASSIFIERS USING F-DIVERGENCES | 4 |
| 3 | 2 | |
| 4 | Training for faster adversarial robustness verification via inducing Relu stability | 13 |
| 5 | 114 |
About Kai Xiao
Kai Xiao is a scholar working on Artificial Intelligence, Industrial and Manufacturing Engineering and Computer Networks and Communications, having authored 5 papers that have together received 141 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (4 papers), Scheduling and Optimization Algorithms (1 paper) and Optimization and Search Problems (1 paper). The work is most often cited by research in Artificial Intelligence (119 citations), Software (10 citations) and Computer Vision and Pattern Recognition (34 citations). Kai Xiao has collaborated with scholars based in United States, Belgium and United Kingdom. Frequent co-authors include Russ Tedrake, Aleksander Mądry, Nur Muhammad Mahi Shafiullah, Sven Gowal, Krishnamurthy Dvijotham, Pushmeet Kohli, Jonathan Uesato, Tsui-Wei Weng, Robert Stanforth and Jayson Lynch. Their work appears in journals such as Graphs and Combinatorics, arXiv (Cornell University) and DSpace@MIT (Massachusetts Institute of Technology).
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.