Jacob R. Gardner

2.0k total citations
19 papers, 517 citations indexed

About

Jacob R. Gardner is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Molecular Biology. According to data from OpenAlex, Jacob R. Gardner has authored 19 papers receiving a total of 517 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 4 papers in Computational Theory and Mathematics and 3 papers in Molecular Biology. Recurrent topics in Jacob R. Gardner's work include Gaussian Processes and Bayesian Inference (6 papers), Advanced Multi-Objective Optimization Algorithms (4 papers) and Neural Networks and Applications (3 papers). Jacob R. Gardner is often cited by papers focused on Gaussian Processes and Bayesian Inference (6 papers), Advanced Multi-Objective Optimization Algorithms (4 papers) and Neural Networks and Applications (3 papers). Jacob R. Gardner collaborates with scholars based in United States, China and Mexico. Jacob R. Gardner's co-authors include Kilian Q. Weinberger, John P. Cunningham, Matt J. Kusner, Zhixiang, Andrew Gordon Wilson, Chuan Guo, David Eriksson, Roman Garnett, Matthias Poloczek and Michael Pearce and has published in prestigious journals such as Nucleic Acids Research, SHILAP Revista de lepidopterología and eLife.

In The Last Decade

Jacob R. Gardner

16 papers receiving 495 citations

Peers

Jacob R. Gardner

CTM

MSOR

CSE

CVPR

Andreas Damianou United Kingdom

Hirotaka Hachiya Japan

Gautam Dasarathy United States

Stefan Roth Germany

Ziyu Wang China

Pierrick Legrand France

Wen Jiang China

Eric Brochu Canada

Oleg Burdakov Sweden

Andreas Damianou United Kingdom

Jacob R. Gardner

297 ×1.2

103 ×0.7

CTM

48 ×0.7

MSOR

120 ×2.0

CSE

92 ×1.6

CVPR

Citations per year, relative to Jacob R. Gardner Jacob R. Gardner (= 1×) peers Andreas Damianou

Countries citing papers authored by Jacob R. Gardner

Since Specialization

Citations

This map shows the geographic impact of Jacob R. Gardner'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 Jacob R. Gardner with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jacob R. Gardner more than expected).

Fields of papers citing papers by Jacob R. Gardner

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jacob R. Gardner. 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 Jacob R. Gardner. The network helps show where Jacob R. Gardner may publish in the future.

Co-authorship network of co-authors of Jacob R. Gardner

This figure shows the co-authorship network connecting the top 25 collaborators of Jacob R. Gardner. A scholar is included among the top collaborators of Jacob R. Gardner 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 Jacob R. Gardner. Jacob R. Gardner is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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19 of 19 papers shown

Jones, Haydn T., et al.. (2025). Learned Offline Query Planning via Bayesian Optimization. Proceedings of the ACM on Management of Data. 3(3). 1–29.

Wu, Di, et al.. (2025). Generative modeling for RNA splicing prediction and design. eLife.

Davey, Pinakin Gunvant, et al.. (2024). Contrast response function estimation with nonparametric Bayesian active learning. Journal of Vision. 24(1). 6–6. 2 indexed citations

Barbour, Dennis L., et al.. (2024). Multitask Machine Learning of Contrast Sensitivity Functions. Journal of Vision. 24(10). 1082–1082.

Giorgi, Salvatore, et al.. (2023). Filling in the white space: Spatial interpolation with Gaussian processes and social media data. SHILAP Revista de lepidopterología. 5. 100159–100159. 2 indexed citations

Wang, Haoyu, et al.. (2023). Extracting or Guessing? Improving Faithfulness of Event Temporal Relation Extraction. 5 indexed citations

Fong, Jerry J., Jacob R. Gardner, Jared Andrews, et al.. (2021). Determining subpopulation methylation profiles from bisulfite sequencing data of heterogeneous samples using DXM. Nucleic Acids Research. 49(16). e93–e93. 10 indexed citations

Guo, Chuan, Jacob R. Gardner, Yurong You, Andrew Gordon Wilson, & Kilian Q. Weinberger. (2019). Simple Black-box Adversarial Attacks. arXiv (Cornell University). 2484–2493. 52 indexed citations

Eriksson, David, Michael Pearce, Jacob R. Gardner, R.D. Turner, & Matthias Poloczek. (2019). Scalable Global Optimization via Local Bayesian Optimization. arXiv (Cornell University). 32. 5496–5507. 64 indexed citations

10.

Gardner, Jacob R., Geoff Pleiss, Kilian Q. Weinberger, David Bindel, & Andrew Gordon Wilson. (2018). GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration. Neural Information Processing Systems. 31. 7576–7586. 57 indexed citations

11.

Gardner, Jacob R., et al.. (2018). Product kernel interpolation for scalable gaussian processes. International Conference on Artificial Intelligence and Statistics. 1407–1416. 5 indexed citations

12.

Gardner, Jacob R., Chuan Guo, Kilian Q. Weinberger, Roman Garnett, & Roger Grosse. (2017). Discovering and Exploiting Additive Structure for Bayesian Optimization. International Conference on Artificial Intelligence and Statistics. 1311–1319. 29 indexed citations

13.

Gardner, Jacob R., et al.. (2015). Psychophysical detection testing with Bayesian active learning. Uncertainty in Artificial Intelligence. 286–297. 8 indexed citations

14.

Gardner, Jacob R., et al.. (2015). Fast, Continuous Audiogram Estimation Using Machine Learning. Ear and Hearing. 36(6). e326–e335. 43 indexed citations

15.

Zhou, Quan, Wenlin Chen, Shiji Song, et al.. (2015). A Reduction of the Elastic Net to Support Vector Machines with an Application to GPU Computing. Proceedings of the AAAI Conference on Artificial Intelligence. 29(1). 30 indexed citations

16.

Kusner, Matt J., Jacob R. Gardner, Roman Garnett, & Kilian Q. Weinberger. (2015). Differentially Private Bayesian Optimization. PolyPublie (École Polytechnique de Montréal). 918–927. 13 indexed citations

17.

Gardner, Jacob R., et al.. (2015). Bayesian active model selection with an application to automated audiometry. 28. 2386–2394. 21 indexed citations

18.

Gardner, Jacob R., Matt J. Kusner, Zhixiang, Kilian Q. Weinberger, & John P. Cunningham. (2014). Bayesian Optimization with Inequality Constraints. PolyPublie (École Polytechnique de Montréal). 937–945. 175 indexed citations

19.

Gardner, Jacob R., et al.. (2014). WOODSTOCC: Extracting Latent Parallelism from a DNA Sequence Aligner on a GPU. Open Scholarship Institutional Repository (Washington University in St. Louis). 57. 197–204. 1 indexed citations

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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