Yulia Rubanova

17.2k citations
3 papers · 131 · h-index 3

Impact in

Papers in

Journals
Neural Information Processing Systems (1 paper)Uncertainty in Artificial Intelligence (1 paper)PubMed (1 paper)

In The Last Decade

Yulia Rubanova

3 papers receiving 126 citations

Peers

Yulia Rubanova
Comparison fields: 5 of 67
  • Statistical and Nonlinear Physics 36
  • Signal Processing 26
  • Artificial Intelligence 71
  • Health Information Management 5
  • Statistics, Probability and Uncertainty 6
Replace Liyao Gao with:
Liyao Gao United States
Vincent Fortuin Switzerland
Tianyuan Yu China
Madan Mohan Sati India
Sanjay Kumar Sonbhadra India
Jiaqi Lin China
L. K. Li China
Marco Ancona Switzerland
Mohammad Emtiyaz Khan Switzerland
Yulia Rubanova relative to Liyao Gao United States Liyao Gao's profile →
Citations per field
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Liyao Gao · 1×
Citations per year

Countries citing papers authored by Yulia Rubanova

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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 6 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.

Border = papers with Yulia Rubanova Line = papers co-authored together Yulia Rubanova links everyone, so they are left out of the graph.

All Works

3 of 3 papers shown
#Work
1
Latent Ordinary Differential Equations for Irregularly-Sampled Time Series
2019120
2 20197
3
Amortized Bayesian Optimization over Discrete Spaces
20204

About Yulia Rubanova

Yulia Rubanova is a scholar working on Artificial Intelligence, Molecular Biology, Statistical and Nonlinear Physics, Signal Processing and Computational Theory and Mathematics, having authored 3 papers that have together received 131 indexed citations. Recurring topics across this work include Single-cell and spatial transcriptomics (1 paper), Cancer Genomics and Diagnostics (1 paper), Time Series Analysis and Forecasting (1 paper), Machine Learning and Algorithms (1 paper), Model Reduction and Neural Networks (1 paper), Advanced Bandit Algorithms Research (1 paper), Machine Learning in Healthcare (1 paper) and Advanced Multi-Objective Optimization Algorithms (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (36 citations), Signal Processing (26 citations), Artificial Intelligence (71 citations), Health Information Management (5 citations) and Statistics, Probability and Uncertainty (6 citations). Yulia Rubanova has collaborated with scholars based in Canada, United Kingdom and United States. Frequent co-authors include David Duvenaud, Ricky T. Q. Chen, Quaid Morris, Kevin J. Murphy, Kevin Swersky and David Dohan. Their work appears in journals such as Neural Information Processing Systems, Uncertainty in Artificial Intelligence and PubMed.

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.

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