Ian Covert
- Artificial Intelligence top 5%
- Neural Networks and Applications 3
- Explainable Artificial Intelligence (XAI) 2
- Machine Learning and Data Classification 2
- Bayesian Modeling and Causal Inference 1
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- Face and Expression Recognition 2
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- Neural dynamics and brain function 1
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- Extracellular vesicles in disease 1
- Single-cell and spatial transcriptomics 1
- Co-authors
- Su‐In LeeScott LundbergHugh ChenNicholas J. FotiAli ShojaieEmily B. FoxAli FarhadiRosanne Liu
- Journals
- IEEE Transactions on Pattern Analysis and Machine Intelligence (1 paper)Nature Communications (1 paper)Nature Machine Intelligence (1 paper)
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Ian Covert
7 papers receiving 469 citations
Hit Papers
Peers
Comparison fields: 5 of 123
- Artificial Intelligence 202
- Computer Vision and Pattern Recognition 72
- Signal Processing 32
- Health Informatics 4
- Management Science and Operations Research 35
Countries citing papers authored by Ian Covert
This map shows the geographic impact of Ian Covert'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 Ian Covert with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ian Covert more than expected).
Fields of papers citing papers by Ian Covert
This network shows the impact of papers produced by Ian Covert. 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 Ian Covert. The network helps show where Ian Covert may publish in the future.
Co-authorship network
The 17 scholars most cited alongside Ian Covert, 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 | 2023 | 11 | |
| 2 | 2023 | 0 | |
| 3 | Algorithms to estimate Shapley value feature attributionsbreakdown → | 2023 | 199 |
| 4 | What does a platypus look like? Generating customized prompts for zero-shot image classificationbreakdown → | 2023 | 90 |
| 5 | Improving KernelSHAP: Practical Shapley Value Estimation Using Linear Regression | 2021 | 30 |
| 6 | 2021 | 137 | |
| 7 | Understanding Global Feature Contributions With Additive Importance Measures | 2020 | 6 |
| 8 | Temporal Graph Convolutional Networks for Automatic Seizure Detection | 2019 | 6 |
About Ian Covert
Ian Covert is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Cognitive Neuroscience, having authored 8 papers that have together received 479 indexed citations. Recurring topics across this work include Neural Networks and Applications (3 papers), Explainable Artificial Intelligence (XAI) (2 papers), Face and Expression Recognition (2 papers), Machine Learning and Data Classification (2 papers), Neural dynamics and brain function (1 paper), Extracellular vesicles in disease (1 paper), Single-cell and spatial transcriptomics (1 paper) and Bayesian Modeling and Causal Inference (1 paper). The work is most often cited by research in Artificial Intelligence (202 citations), Computer Vision and Pattern Recognition (72 citations) and Signal Processing (32 citations). Ian Covert has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Su‐In Lee, Scott Lundberg, Hugh Chen, Nicholas J. Foti, Ali Shojaie, Emily B. Fox, Ali Farhadi, Rosanne Liu, Sarah I. Pratt and Tim Wang. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Nature Communications, Nature Machine Intelligence, International Conference on Artificial Intelligence and Statistics and arXiv (Cornell University).
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.