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Benjamin I. P. Rubinstein

School of Computing and Information Systems, University of Melbourne

66 papers hereh-index 355k citations134 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author4
  • middle author39
  • last author22

Across the 65 of 66 papers where every author was matched, so the position is known.

fields
  • cs.LG24
  • cs.CR15
  • cs.DB10
  • cs.CL5
  • cs.CV2
  • cs.SE2
affiliations
  • School of Computing and Information Systems, University of Melbourne
Homepage
same name
  • Benjamin I. P. Rubinstein — 6 papers, h 2
  • Benjamin I. P. Rubinstein — 5 papers, h 2
  • Benjamin I. P. Rubinstein — 5 papers, h 1
  • Benjamin I. P. Rubinstein — 4 papers, h 3
  • Benjamin I. P. Rubinstein — 2 papers, h 4
  • Benjamin I. P. Rubinstein — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20092024
most citedLearning in a Large Function Space: Privacy-Preserving Mechanisms for SVM Learning

51 citations · 348 across the 61 of their papers we have counts for

collaborators
Showing 2016Show all

3 papers · 1 filter

cs.GT2016

Large-Scale Strategic Games and Adversarial Machine Learning

Tansu Alpcan, Benjamin I. P. Rubinstein, Christopher Leckie

Decision making in modern large-scale and complex systems such as communication networks, smart electricity grids, and cyber-physical systems motivate novel game-theoretic approach…

cs.DB2016★ 4 cited

Differentially-Private Counting of Users' Spatial Regions

Maryam Fanaeepour, Benjamin I. P. Rubinstein

Mining of spatial data is an enabling technology for mobile services, Internet-connected cars, and the Internet of Things. But the very distinctiveness of spatial data that drives…

cs.LG2016

TopicResponse: A Marriage of Topic Modelling and Rasch Modelling for Automatic Measurement in MOOCs

Jiazhen He, Benjamin I. P. Rubinstein, James Bailey +2

This paper explores the suitability of using automatically discovered topics from MOOC discussion forums for modelling students' academic abilities. The Rasch model from psychometr…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.