Robert Stanforth

2.0k total citations
12 papers, 333 citations indexed

About

Robert Stanforth is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering and Hardware and Architecture. According to data from OpenAlex, Robert Stanforth has authored 12 papers receiving a total of 333 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 3 papers in Electrical and Electronic Engineering and 2 papers in Hardware and Architecture. Recurrent topics in Robert Stanforth's work include Adversarial Robustness in Machine Learning (8 papers), Integrated Circuits and Semiconductor Failure Analysis (3 papers) and Topic Modeling (3 papers). Robert Stanforth is often cited by papers focused on Adversarial Robustness in Machine Learning (8 papers), Integrated Circuits and Semiconductor Failure Analysis (3 papers) and Topic Modeling (3 papers). Robert Stanforth collaborates with scholars based in United Kingdom and United States. Robert Stanforth's co-authors include Pushmeet Kohli, Sven Gowal, Krishnamurthy Dvijotham, Po-Sen Huang, Johannes Welbl, Dani Yogatama, Jonathan Uesato, Chongli Qin, Boris Mirkin and Jack W. Rae and has published in prestigious journals such as SAR and QSAR in environmental research, QSAR & Combinatorial Science and arXiv (Cornell University).

In The Last Decade

Robert Stanforth

12 papers receiving 318 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Robert Stanforth United Kingdom 9 255 52 43 31 27 12 333
Daniel Zügner Germany 7 278 1.1× 33 0.6× 31 0.7× 45 1.5× 32 1.2× 11 373
Siqi Chen China 9 90 0.4× 31 0.6× 50 1.2× 42 1.4× 15 0.6× 29 240
Song Bian China 4 119 0.5× 75 1.4× 19 0.4× 29 0.9× 17 0.6× 4 209
Anna Pagh Denmark 6 117 0.5× 27 0.5× 25 0.6× 13 0.4× 21 0.8× 6 185
Volker Heun Germany 7 109 0.4× 16 0.3× 45 1.0× 78 2.5× 17 0.6× 16 185
Peide Qian China 8 156 0.6× 15 0.3× 29 0.7× 33 1.1× 7 0.3× 39 258
Abdou Youssef United States 10 179 0.7× 39 0.8× 156 3.6× 12 0.4× 12 0.4× 49 356
Zheng Gao United States 8 96 0.4× 27 0.5× 29 0.7× 45 1.5× 15 0.6× 36 220
Amir Akbarnejad Canada 3 210 0.8× 21 0.4× 7 0.2× 10 0.3× 21 0.8× 6 233
Giannis Nikolentzos France 9 215 0.8× 71 1.4× 16 0.4× 52 1.7× 11 0.4× 17 281

Countries citing papers authored by Robert Stanforth

Since Specialization
Citations

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

Fields of papers citing papers by Robert Stanforth

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Robert Stanforth. 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 Robert Stanforth. The network helps show where Robert Stanforth may publish in the future.

Co-authorship network of co-authors of Robert Stanforth

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

All Works

12 of 12 papers shown
1.
Dathathri, Sumanth, Krishnamurthy Dvijotham, Robert Stanforth, et al.. (2021). Make Sure You're Unsure: A Framework for Verifying Probabilistic Specifications. arXiv (Cornell University). 34. 2 indexed citations
2.
Welbl, Johannes, Po-Sen Huang, Robert Stanforth, et al.. (2020). Towards Verified Robustness under Text Deletion Interventions. International Conference on Learning Representations. 3 indexed citations
3.
Weng, Tsui-Wei, Krishnamurthy Dvijotham, Jonathan Uesato, et al.. (2020). Toward Evaluating Robustness of Deep Reinforcement Learning with Continuous Control. International Conference on Learning Representations. 8 indexed citations
4.
Huang, Po-Sen, Huan Zhang, Robert Stanforth, et al.. (2020). Reducing Sentiment Bias in Language Models via Counterfactual Evaluation. 65–83. 74 indexed citations
5.
Huang, Po-Sen, Robert Stanforth, Johannes Welbl, et al.. (2019). Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation. 4081–4091. 64 indexed citations
6.
Alayrac, Jean-Baptiste, Jonathan Uesato, Po-Sen Huang, et al.. (2019). Are Labels Required for Improving Adversarial Robustness. arXiv (Cornell University). 32. 12192–12202. 24 indexed citations
7.
Dvijotham, Krishnamurthy, Robert Stanforth, Sven Gowal, et al.. (2019). Efficient Neural Network Verification with Exactness Characterization. Uncertainty in Artificial Intelligence. 497–507. 8 indexed citations
8.
Qin, Chongli, James Martens, Sven Gowal, et al.. (2019). Adversarial Robustness through Local Linearization. Neural Information Processing Systems. 32. 13824–13833. 27 indexed citations
9.
Gowal, Sven, Krishnamurthy Dvijotham, Robert Stanforth, et al.. (2019). Scalable Verified Training for Provably Robust Image Classification. 4841–4850. 64 indexed citations
10.
Gowal, Sven, Krishnamurthy Dvijotham, Robert Stanforth, Timothy Mann, & Pushmeet Kohli. (2019). A Dual Approach to Verify and Train Deep Networks. 6156–6160. 3 indexed citations
11.
Stanforth, Robert, et al.. (2007). The quality of QSAR models: problems and solutions. SAR and QSAR in environmental research. 18(1-2). 89–100. 28 indexed citations
12.
Stanforth, Robert, et al.. (2007). A Measure of Domain of Applicability for QSAR Modelling Based on Intelligent K‐Means Clustering. QSAR & Combinatorial Science. 26(7). 837–844. 28 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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