Matt J. Kusner
- Artificial Intelligence top 1%
- Computer Vision and Pattern Recognition top 5%
- Information Systems top 5%
- Computational Mechanics top 10%
- Computational Theory and Mathematics top 5%
- Co-authors
- Kilian Q. WeinbergerYu SunNicholas KolkinJoshua R. LoftusJacob R. GardnerZhixiangJohn P. CunninghamAdrià Gascón
- Topics
- Machine Learning and Data Classification (5 papers)Machine Learning in Materials Science (4 papers)Ethics and Social Impacts of AI (4 papers)
- Partner nations
- United StatesUnited KingdomGermany
In The Last Decade
Matt J. Kusner
24 papers receiving 1.6k citations
Hit Papers
Peers
Comparison fields: 5 of 124
- Artificial Intelligence 1.1k
- Computer Vision and Pattern Recognition 316
- Information Systems 246
- Computational Mechanics 126
- Computational Theory and Mathematics 126
Countries citing papers authored by Matt J. Kusner
This map shows the geographic impact of Matt J. Kusner'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 Matt J. Kusner with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matt J. Kusner more than expected).
Fields of papers citing papers by Matt J. Kusner
This network shows the impact of papers produced by Matt J. Kusner. 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 Matt J. Kusner. The network helps show where Matt J. Kusner may publish in the future.
Co-authorship network of co-authors of Matt J. Kusner
This figure shows the co-authorship network connecting the top 25 collaborators of Matt J. Kusner. A scholar is included among the top collaborators of Matt J. Kusner 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 Matt J. Kusner. Matt J. Kusner 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 | 0 | |
| 3 | 12 | |
| 4 | MPC-friendly commitments for publicly verifiable covert security | 0 |
| 5 | 20 | |
| 6 | 8 | |
| 7 | Making Decisions that Reduce Discriminatory Impacts | 4 |
| 8 | Generating molecules via chemical reactions | 1 |
| 9 | 26 | |
| 10 | TAPAS : tricks to accelerate (encrypted) prediction as a service | 8 |
| 11 | When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness | 53 |
| 12 | 2 | |
| 13 | 57 | |
| 14 | From Word Embeddings To Document Distancesbreakdown → | 812 |
| 15 | Fast distributed k -center clustering with outliers on massive data | 20 |
| 16 | Bayesian Optimization with Inequality Constraints | 175 |
| 17 | Stochastic Neighbor Compression | 30 |
| 18 | 40 | |
| 19 | 24 | |
| 20 | Anytime Representation Learning | 6 |
About Matt J. Kusner
Matt J. Kusner is a scholar working on Artificial Intelligence, Safety Research and Computational Theory and Mathematics, having authored 26 papers that have together received 1.7k indexed citations. Recurring topics across this work include Machine Learning and Data Classification (5 papers), Machine Learning in Materials Science (4 papers) and Ethics and Social Impacts of AI (4 papers). The work is most often cited by research in Artificial Intelligence (1.1k citations), Health Informatics (22 citations) and Geology (86 citations). Matt J. Kusner has collaborated with scholars based in United States, United Kingdom and Germany. Frequent co-authors include Kilian Q. Weinberger, Yu Sun, Nicholas Kolkin, Joshua R. Loftus, Jacob R. Gardner, Zhixiang, John P. Cunningham, Adrià Gascón, Nitin Agrawal and Ali Shahin Shamsabadi. Their work appears in journals such as Nature, Journal of Machine Learning Research and Nuclear Fusion.
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.