Bobak Shahriari
- Computational Theory and Mathematics top 0.5%
- Advanced Multi-Objective Optimization Algorithms 2
- Adaptive Dynamic Programming Control 1
- Artificial Intelligence top 1%
- Gaussian Processes and Bayesian Inference 1
- Adversarial Robustness in Machine Learning 1
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- Advanced Bandit Algorithms Research 2
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- Probabilistic and Robust Engineering Design 1
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- Topology Optimization in Engineering 1
- Soil Mechanics and Vehicle Dynamics 1
- Co-authors
- Nando de FreitasZiyu WangKevin SwerskyRyan P. AdamsMatthew D. HoffmanNilima NigamKonrad ŻołnaScott Reed
- Cited by
- Computational Theory and MathematicsArtificial IntelligenceManagement Science and Operations Research
- Journals
- Proceedings of the IEEE (1 paper)Computers & Mathematics with Applications (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- United StatesCanada
In The Last Decade
Bobak Shahriari
6 papers receiving 3.3k citations
Hit Papers
Peers
Comparison fields: 5 of 181
- Computational Theory and Mathematics 827
- Artificial Intelligence 1.1k
- Management Science and Operations Research 384
- Statistics, Probability and Uncertainty 186
- Control and Systems Engineering 351
Countries citing papers authored by Bobak Shahriari
This map shows the geographic impact of Bobak Shahriari'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 Bobak Shahriari with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bobak Shahriari more than expected).
Fields of papers citing papers by Bobak Shahriari
This network shows the impact of papers produced by Bobak Shahriari. 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 Bobak Shahriari. The network helps show where Bobak Shahriari may publish in the future.
Co-authorship network
The 15 scholars most cited alongside Bobak Shahriari, 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 | Critic Regularized Regression | 2020 | 1 |
| 2 | 2017 | 3 | |
| 3 | 2016 | 2 | |
| 4 | Taking the Human Out of the Loop: A Review of Bayesian Optimizationbreakdown → | 2015 | 3359 |
| 5 | On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning | 2014 | 49 |
| 6 | Pareto Optimization of Vehicle Suspension Vibration for a Nonlinear Half- car Model Using a Multi-objective Genetic Algorithm | 2012 | 5 |
About Bobak Shahriari
Bobak Shahriari is a scholar working on Computational Theory and Mathematics, Management Science and Operations Research and Statistics, Probability and Uncertainty, having authored 6 papers that have together received 3.4k indexed citations. Recurring topics across this work include Advanced Multi-Objective Optimization Algorithms (2 papers), Advanced Bandit Algorithms Research (2 papers), Gaussian Processes and Bayesian Inference (1 paper), Topology Optimization in Engineering (1 paper), Probabilistic and Robust Engineering Design (1 paper), Soil Mechanics and Vehicle Dynamics (1 paper), Adaptive Dynamic Programming Control (1 paper) and Adversarial Robustness in Machine Learning (1 paper). The work is most often cited by research in Computational Theory and Mathematics (827 citations), Artificial Intelligence (1.1k citations) and Management Science and Operations Research (384 citations). Bobak Shahriari has collaborated with scholars based in United States and Canada. Frequent co-authors include Nando de Freitas, Ziyu Wang, Kevin Swersky, Ryan P. Adams, Matthew D. Hoffman, Nilima Nigam, Konrad Żołna, Scott Reed, Alexander Novikov and Jost Tobias Springenberg. Their work appears in journals such as Proceedings of the IEEE, Computers & Mathematics with Applications, arXiv (Cornell University), International Conference on Artificial Intelligence and Statistics and Open Collections.
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.