Andrew Critch
- Management Science and Operations Research
- Artificial Intelligence
- Computational Theory and Mathematics
- Computational Mathematics top 10%
- Atomic and Molecular Physics, and Optics
- Co-authors
- Mario FedrizziMatteo BrunelliStuart RussellEugene VinitskySergey LevineMichael D. DennisAlexandre M. BayenNatasha Jaques
- Topics
- Neural Networks and Applications (2 papers)Logic, Reasoning, and Knowledge (2 papers)Bayesian Modeling and Causal Inference (2 papers)
- Cited by
- Computational MathematicsManagement Science and Operations ResearchGeneral Decision Sciences
- Journals
- SHILAP Revista de lepidopterologíaApplied Mathematics and ComputationJournal of Symbolic Logic
- Partner nations
- United StatesItalyFinland
In The Last Decade
Andrew Critch
10 papers receiving 61 citations
Peers
Comparison fields: 5 of 40
- Management Science and Operations Research 28
- Artificial Intelligence 25
- Computational Theory and Mathematics 16
- Computational Mathematics 14
- Atomic and Molecular Physics, and Optics 5
Countries citing papers authored by Andrew Critch
This map shows the geographic impact of Andrew Critch'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 Andrew Critch with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andrew Critch more than expected).
Fields of papers citing papers by Andrew Critch
This network shows the impact of papers produced by Andrew Critch. 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 Andrew Critch. The network helps show where Andrew Critch may publish in the future.
Co-authorship network of co-authors of Andrew Critch
This figure shows the co-authorship network connecting the top 25 collaborators of Andrew Critch. A scholar is included among the top collaborators of Andrew Critch 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 Andrew Critch. Andrew Critch is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Importance and Coherence: Methods for Evaluating Modularity in Neural Networks | 0 |
| 2 | Neural Networks are Surprisingly Modular | 3 |
| 3 | The MAGICAL Benchmark for Robust Imitation | 1 |
| 4 | 8 | |
| 5 | 2 | |
| 6 | 1 | |
| 7 | Negotiable Reinforcement Learning for Pareto Optimal Sequential Decision-Making | 1 |
| 8 | 2 | |
| 9 | 12 | |
| 10 | 28 | |
| 11 | Algebraic Geometry of Hidden Markov and Related Models | 5 |
About Andrew Critch
Andrew Critch is a scholar working on Biophysics, Discrete Mathematics and Combinatorics and Artificial Intelligence, having authored 11 papers that have together received 63 indexed citations. Recurring topics across this work include Neural Networks and Applications (2 papers), Logic, Reasoning, and Knowledge (2 papers) and Bayesian Modeling and Causal Inference (2 papers). The work is most often cited by research in Computational Mathematics (14 citations), Management Science and Operations Research (28 citations) and General Decision Sciences (2 citations). Andrew Critch has collaborated with scholars based in United States, Italy and Finland. Frequent co-authors include Mario Fedrizzi, Matteo Brunelli, Stuart Russell, Eugene Vinitsky, Sergey Levine, Michael D. Dennis, Alexandre M. Bayen, Natasha Jaques, Sam Toyer and Stephen Casper. Their work appears in journals such as SHILAP Revista de lepidopterología, Applied Mathematics and Computation and Journal of Symbolic Logic.
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.