Max Ryabinin
Impact in
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- Topic Modeling
- Natural Language Processing Techniques
- Adversarial Robustness in Machine Learning
- Explainable Artificial Intelligence (XAI)
- Text Readability and Simplification
- Semantic Web and Ontologies
Papers in
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- Topic Modeling 3
- Natural Language Processing Techniques 3
- Stochastic Gradient Optimization Techniques 1
- Adversarial Robustness in Machine Learning 1
- Data Stream Mining Techniques 1
- Machine Learning and Algorithms 1
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- Online Learning and Analytics 1
- Co-authors
- Ekaterina Artemova (1 shared paper)Vladislav Mikhailov (1 shared paper)Peter Richtárik (1 shared paper)Michael G. Diskin (1 shared paper)Quentin G. Anthony (1 shared paper)Ben Athiwaratkun (1 shared paper)Chris Ré (1 shared paper)Irina Rish (1 shared paper)
- Journals
- arXiv (Cornell University) (3 papers)Neural Information Processing Systems (1 paper)
- Partner nations
- RussiaUnited KingdomCanada
In The Last Decade
Max Ryabinin
11 papers receiving 29 citations
Peers
Comparison fields: 5 of 18
- Health Informatics 1
- Artificial Intelligence 18
- Human-Computer Interaction 1
- Information Systems 3
- Computer Networks and Communications 3
Countries citing papers authored by Max Ryabinin
This map shows the geographic impact of Max Ryabinin'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 Max Ryabinin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Max Ryabinin more than expected).
Fields of papers citing papers by Max Ryabinin
This network shows the impact of papers produced by Max Ryabinin. 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 Max Ryabinin. The network helps show where Max Ryabinin may publish in the future.
Co-authors
The 25 scholars most cited alongside Max Ryabinin, 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 | 2022 | 11 | |
| 2 | 2024 | 5 | |
| 3 | Towards Crowdsourced Training of Large Neural Networks using Decentralized Mixture-of-Experts | 2020 | 3 |
| 4 | 2022 | 2 | |
| 5 | 2024 | 2 | |
| 6 | 2023 | 1 | |
| 7 | 2024 | 1 | |
| 8 | 2024 | 1 | |
| 9 | 2020 | 1 | |
| 10 | 2021 | 1 | |
| 11 | 2025 | 1 |
About Max Ryabinin
Max Ryabinin is a scholar working on Artificial Intelligence, Computer Science Applications, Management Science and Operations Research, Computer Vision and Pattern Recognition and Computer Networks and Communications, having authored 11 papers that have together received 29 indexed citations. Recurring topics across this work include Topic Modeling (3 papers), Natural Language Processing Techniques (3 papers), Stochastic Gradient Optimization Techniques (1 paper), Online Learning and Analytics (1 paper), Advanced Bandit Algorithms Research (1 paper), Adversarial Robustness in Machine Learning (1 paper), Data Stream Mining Techniques (1 paper) and Machine Learning and Algorithms (1 paper). The work is most often cited by research in Health Informatics (1 citation), Artificial Intelligence (18 citations), Human-Computer Interaction (1 citation), Information Systems (3 citations) and Computer Networks and Communications (3 citations). Max Ryabinin has collaborated with scholars based in Russia, United Kingdom and Canada. Frequent co-authors include Ekaterina Artemova, Vladislav Mikhailov, Peter Richtárik, Michael G. Diskin, Quentin G. Anthony, Ben Athiwaratkun, Chris Ré, Irina Rish, Yihong Chen and Сергей Борисович Попов. Their work appears in journals such as arXiv (Cornell University) and Neural Information Processing Systems.
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.