Michal Vaľko
- Artificial Intelligence top 10%
- Signal Processing top 10%
- Computer Vision and Pattern Recognition
- Management Science and Operations Research
- Molecular Biology
- Co-authors
- Miloš HauskrechtRichard PelikanJames Lyons‐WeilerGilles ClermontGregory F. CooperShyam VisweswaranIyad BatalRémi Munos
- Topics
- Advanced Bandit Algorithms Research (6 papers)Machine Learning and Algorithms (4 papers)Anomaly Detection Techniques and Applications (2 papers)
- Journals
- Journal of Machine Learning ResearchJournal of Biomedical InformaticsInternational Journal of Dentistry
- Partner nations
- United StatesFranceUnited Kingdom
In The Last Decade
Michal Vaľko
16 papers receiving 338 citations
Peers
Comparison fields: 5 of 111
- Artificial Intelligence 197
- Signal Processing 58
- Computer Vision and Pattern Recognition 41
- Management Science and Operations Research 38
- Molecular Biology 32
Countries citing papers authored by Michal Vaľko
This map shows the geographic impact of Michal Vaľko'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 Michal Vaľko with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michal Vaľko more than expected).
Fields of papers citing papers by Michal Vaľko
This network shows the impact of papers produced by Michal Vaľko. 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 Michal Vaľko. The network helps show where Michal Vaľko may publish in the future.
Co-authorship network of co-authors of Michal Vaľko
This figure shows the co-authorship network connecting the top 25 collaborators of Michal Vaľko. A scholar is included among the top collaborators of Michal Vaľko 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 Michal Vaľko. Michal Vaľko is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 2 | |
| 3 | 0 | |
| 4 | Learning in two-player zero-sum partially observable Markov games with perfect recall | 3 |
| 5 | DPPy: DPP Sampling with Python | 11 |
| 6 | Rotting bandits are no harder than stochastic ones | 5 |
| 7 | 8 | |
| 8 | Influence Maximization with Semi-Bandit Feedback. | 3 |
| 9 | Cheap Bandits | 2 |
| 10 | Extreme bandits | 10 |
| 11 | 6 | |
| 12 | 21 | |
| 13 | 90 | |
| 14 | 25 | |
| 15 | Conditional Outlier Detection for Clinical Alerting | 10 |
| 16 | Conditional outlier detection for clinical alerting. | 36 |
| 17 | 8 | |
| 18 | 121 |
About Michal Vaľko
Michal Vaľko is a scholar working on Management Science and Operations Research, Health Information Management and Periodontics, having authored 18 papers that have together received 361 indexed citations. Recurring topics across this work include Advanced Bandit Algorithms Research (6 papers), Machine Learning and Algorithms (4 papers) and Anomaly Detection Techniques and Applications (2 papers). The work is most often cited by research in Artificial Intelligence (197 citations), Health Information Management (27 citations) and Signal Processing (58 citations). Michal Vaľko has collaborated with scholars based in United States, France and United Kingdom. Frequent co-authors include Miloš Hauskrecht, Richard Pelikan, James Lyons‐Weiler, Gilles Clermont, Gregory F. Cooper, Shyam Visweswaran, Iyad Batal, Rémi Munos, Pierre‐Marie Preux and Alexandra Carpentier. Their work appears in journals such as Journal of Machine Learning Research, Journal of Biomedical Informatics and International Journal of Dentistry.
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.