Benjamin I. P. Rubinstein is a scholar working on Artificial Intelligence, Computer Networks and Communications and Signal Processing.
According to data from OpenAlex, Benjamin I. P. Rubinstein has authored 89 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 64 papers in Artificial Intelligence, 17 papers in Computer Networks and Communications and 14 papers in Signal Processing. Recurrent topics in Benjamin I. P. Rubinstein's work include Adversarial Robustness in Machine Learning (23 papers), Privacy-Preserving Technologies in Data (19 papers) and Network Security and Intrusion Detection (13 papers). Benjamin I. P. Rubinstein is often cited by papers focused on Adversarial Robustness in Machine Learning (23 papers), Privacy-Preserving Technologies in Data (19 papers) and Network Security and Intrusion Detection (13 papers). Benjamin I. P. Rubinstein collaborates with scholars based in Australia, United States and Germany. Benjamin I. P. Rubinstein's co-authors include Ling Huang, Anthony D. Joseph, J. D. Tygar, Blaine Nelson, Nina Taft, Jim Gemmell, Bo Zhao, Jiawei Han, Blaine Nelson and A. C. Leopold and has published in prestigious journals such as Science, SHILAP Revista de lepidopterología and PLANT PHYSIOLOGY.
In The Last Decade
Benjamin I. P. Rubinstein
83 papers
receiving
2.7k citations
Hit Papers
What are hit papers?
Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Adversarial machine learning
2011648 citationsLing Huang, Anthony D. Joseph et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
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Countries citing papers authored by Benjamin I. P. Rubinstein
Since
Specialization
Citations
This map shows the geographic impact of Benjamin I. P. Rubinstein'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 Benjamin I. P. Rubinstein with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Benjamin I. P. Rubinstein more than expected).
Fields of papers citing papers by Benjamin I. P. Rubinstein
This network shows the impact of papers produced by Benjamin I. P. Rubinstein. 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 Benjamin I. P. Rubinstein. The network helps show where Benjamin I. P. Rubinstein may publish in the future.
Co-authorship network of co-authors of Benjamin I. P. Rubinstein
This figure shows the co-authorship network connecting the top 25 collaborators of Benjamin I. P. Rubinstein.
A scholar is included among the top collaborators of Benjamin I. P. Rubinstein 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 Benjamin I. P. Rubinstein. Benjamin I. P. Rubinstein is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Yang, Zhuolin, Qian Chen, Pan Zhou, et al.. (2021). TRS: Transferability Reduced Ensemble via Promoting Gradient Diversity and Model Smoothness. Neural Information Processing Systems. 34.8 indexed citations
10.
Li, Yuan, Benjamin I. P. Rubinstein, & Trevor Cohn. (2019). Exploiting Worker Correlation for Label Aggregation in Crowdsourcing. Minerva Access (University of Melbourne). 3886–3895.19 indexed citations
11.
Han, Yi, Paul Montague, Tamas Abraham, et al.. (2019). Adversarial Reinforcement Learning under Partial Observability in Software-Defined Networking.. arXiv (Cornell University).1 indexed citations
12.
Han, Yi & Benjamin I. P. Rubinstein. (2018). Adequacy of the Gradient-Descent Method for Classifier Evasion Attacks. Minerva Access (University of Melbourne). 237–244.1 indexed citations
Rubinstein, Benjamin I. P., Blaine Nelson, Ling Huang, et al.. (2008). Evading Anomaly Detection through Variance Injection Attacks on PCA (Extended Abstract). 5230.2 indexed citations
18.
Rubinstein, J & Benjamin I. P. Rubinstein. (2008). Geometric & topological representations of maximum classes with applications to sample compression. Conference on Learning Theory. 299–310.3 indexed citations
19.
Nelson, Blaine, Marco Barreno, Anthony D. Joseph, et al.. (2008). Exploiting machine learning to subvert your spam filter. Edinburgh Research Explorer. 7.173 indexed citations
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