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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 2010Show all

3 papers · 1 filter

cs.LG2010★ 3 cited

Query Strategies for Evading Convex-Inducing Classifiers

Blaine Nelson, Benjamin I. P. Rubinstein, Ling Huang +4

Classifiers are often used to detect miscreant activities. We study how an adversary can systematically query a classifier to elicit information that allows the adversary to evade…

cs.LG2010★ 13 cited

Near-Optimal Evasion of Convex-Inducing Classifiers

Blaine Nelson, Benjamin I. P. Rubinstein, Ling Huang +6

Classifiers are often used to detect miscreant activities. We study how an adversary can efficiently query a classifier to elicit information that allows the adversary to evade det…

cs.LG2010

On the Stability of Empirical Risk Minimization in the Presence of Multiple Risk Minimizers

Benjamin I. P. Rubinstein, Aleksandr Simma

Recently Kutin and Niyogi investigated several notions of algorithmic stability--a property of a learning map conceptually similar to continuity--showing that training-stability is…

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