Yulia Rubanova
Impact in
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- Model Reduction and Neural Networks
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- Time Series Analysis and Forecasting
Papers in
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- Machine Learning and Algorithms 1
- Machine Learning in Healthcare 1
- Co-authors
- David Duvenaud (1 shared paper)Ricky T. Q. Chen (1 shared paper)Quaid Morris (1 shared paper)Kevin J. Murphy (1 shared paper)Kevin Swersky (1 shared paper)David Dohan (1 shared paper)Yusuf Aytar (1 shared paper)Drew A. Hudson (1 shared paper)
- Journals
- PubMed (1 paper)Uncertainty in Artificial Intelligence (1 paper)Neural Information Processing Systems (1 paper)
- Partner nations
- CanadaUnited StatesUnited Kingdom
In The Last Decade
Yulia Rubanova
3 papers receiving 132 citations
Peers
Comparison fields: 5 of 68
- Statistical and Nonlinear Physics 38
- Signal Processing 26
- Artificial Intelligence 74
- Statistics, Probability and Uncertainty 6
- Computer Vision and Pattern Recognition 17
Countries citing papers authored by Yulia Rubanova
This map shows the geographic impact of Yulia Rubanova'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 Yulia Rubanova with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yulia Rubanova more than expected).
Fields of papers citing papers by Yulia Rubanova
This network shows the impact of papers produced by Yulia Rubanova. 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 Yulia Rubanova. The network helps show where Yulia Rubanova may publish in the future.
Co-authors
The 14 scholars most cited alongside Yulia Rubanova, 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 | Latent Ordinary Differential Equations for Irregularly-Sampled Time Series | 2019 | 126 |
| 2 | 2019 | 7 | |
| 3 | Amortized Bayesian Optimization over Discrete Spaces | 2020 | 4 |
| 4 | 2024 | 0 |
About Yulia Rubanova
Yulia Rubanova is a scholar working on Artificial Intelligence, Molecular Biology, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics and Signal Processing, having authored 4 papers that have together received 137 indexed citations. Recurring topics across this work include Machine Learning and Algorithms (1 paper), Advanced Multi-Objective Optimization Algorithms (1 paper), Cancer Genomics and Diagnostics (1 paper), Evolution and Genetic Dynamics (1 paper), Advanced Bandit Algorithms Research (1 paper), Model Reduction and Neural Networks (1 paper), Machine Learning in Healthcare (1 paper) and Time Series Analysis and Forecasting (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (38 citations), Signal Processing (26 citations), Artificial Intelligence (74 citations), Statistics, Probability and Uncertainty (6 citations) and Computer Vision and Pattern Recognition (17 citations). Yulia Rubanova has collaborated with scholars based in Canada, United States and United Kingdom. Frequent co-authors include David Duvenaud, Ricky T. Q. Chen, Quaid Morris, Kevin J. Murphy, Kevin Swersky, David Dohan, Yusuf Aytar, Drew A. Hudson, Rishabh Kabra and Igor Gilitschenski. Their work appears in journals such as PubMed, Uncertainty in Artificial Intelligence 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.