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Benjamin I. P. Rubinstein

Targeted Poisoning Attacks on Black-Box Neural Machine Translation


Nov 02, 2020
Chang Xu, Jun Wang, Yuqing Tang, Francisco Guzman, Benjamin I. P. Rubinstein, Trevor Cohn


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DBA bandits: Self-driving index tuning under ad-hoc, analytical workloads with safety guarantees


Oct 20, 2020
R. Malinga Perera, Bastian Oetomo, Benjamin I. P. Rubinstein, Renata Borovica-Gajic

* 12 pages, 8 figures 

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A Graph Symmetrisation Bound on Channel Information Leakage under Blowfish Privacy


Jul 12, 2020
Tobias Edwards, Benjamin I. P. Rubinstein, Zuhe Zhang, Sanming Zhou

* 11 pages, 3 figures 

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Improving Interpretability of CNN Models Using Non-Negative Concept Activation Vectors


Jul 07, 2020
Ruihan Zhang, Prashan Madumal, Tim Miller, Krista A. Ehinger, Benjamin I. P. Rubinstein


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Discrete Few-Shot Learning for Pan Privacy


Jun 23, 2020
Roei Gelbhart, Benjamin I. P. Rubinstein


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A general framework for label-efficient online evaluation with asymptotic guarantees


Jun 12, 2020
Neil G. Marchant, Benjamin I. P. Rubinstein

* 27 pages, 6 figures 

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Assessing Centrality Without Knowing Connections


May 28, 2020
Leyla Roohi, Benjamin I. P. Rubinstein, Vanessa Teague

* In: Advances in Knowledge Discovery and Data Mining. PAKDD 2020. Lecture Notes in Computer Science, vol 12085. Springer, Cham, pages 152-163 (2020) 
* Full report of paper appearing in PAKDD2020 

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Legion: Best-First Concolic Testing


Feb 15, 2020
Dongge Liu, Gidon Ernst, Toby Murray, Benjamin I. P. Rubinstein

* 4 pages, 2 program snippets, 1 figure, TestComp2020 

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d-blink: Distributed End-to-End Bayesian Entity Resolution


Sep 13, 2019
Neil G. Marchant, Rebecca C. Steorts, Andee Kaplan, Benjamin I. P. Rubinstein, Daniel N. Elazar

* 28 pages, 6 figures, 4 tables. Includes 21 pages of supplementary material 

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Adversarial Reinforcement Learning under Partial Observability in Software-Defined Networking


Feb 25, 2019
Yi Han, David Hubczenko, Paul Montague, Olivier De Vel, Tamas Abraham, Benjamin I. P. Rubinstein, Christopher Leckie, Tansu Alpcan, Sarah Erfani

* 8 pages, 4 figures 

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Truth Inference at Scale: A Bayesian Model for Adjudicating Highly Redundant Crowd Annotations


Feb 24, 2019
Yuan Li, Benjamin I. P. Rubinstein, Trevor Cohn

* Accepted at the Web Conference/WWW 2019 (camera ready) 

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A Note on Bounding Regret of the C$^2$UCB Contextual Combinatorial Bandit


Feb 20, 2019
Bastian Oetomo, Malinga Perera, Renata Borovica-Gajic, Benjamin I. P. Rubinstein

* 3 pages 

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Differentially-Private Two-Party Egocentric Betweenness Centrality


Jan 16, 2019
Leyla Roohi, Benjamin I. P. Rubinstein, Vanessa Teague

* 10 pages; full report with proofs of paper accepted into INFOCOM'2019 

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Reinforcement Learning for Autonomous Defence in Software-Defined Networking


Aug 17, 2018
Yi Han, Benjamin I. P. Rubinstein, Tamas Abraham, Tansu Alpcan, Olivier De Vel, Sarah Erfani, David Hubczenko, Christopher Leckie, Paul Montague

* 20 pages, 8 figures 

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Sublinear-Time Adaptive Data Analysis


Sep 28, 2017
Benjamin Fish, Lev Reyzin, Benjamin I. P. Rubinstein


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In Search of an Entity Resolution OASIS: Optimal Asymptotic Sequential Importance Sampling


Jun 26, 2017
Neil G. Marchant, Benjamin I. P. Rubinstein

* 13 pages, 5 figures 

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Pain-Free Random Differential Privacy with Sensitivity Sampling


Jun 08, 2017
Benjamin I. P. Rubinstein, Francesco Aldà

* 12 pages, 9 figures, 1 table; full report of paper accepted into ICML'2017 

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Adequacy of the Gradient-Descent Method for Classifier Evasion Attacks


May 25, 2017
Yi Han, Benjamin I. P. Rubinstein

* 10 pages, 7 figures, 10 tables 

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


Mar 20, 2017
Jiazhen He, Benjamin I. P. Rubinstein, James Bailey, Rui Zhang, Sandra Milligan

* In preparation for journal submission; Revisions to improve clarity with additional examples 

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Large-Scale Strategic Games and Adversarial Machine Learning


Sep 21, 2016
Tansu Alpcan, Benjamin I. P. Rubinstein, Christopher Leckie

* 7 pages, 1 figure; CDC'16 to appear 

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MOOCs Meet Measurement Theory: A Topic-Modelling Approach


Nov 25, 2015
Jiazhen He, Benjamin I. P. Rubinstein, James Bailey, Rui Zhang, Sandra Milligan, Jeffrey Chan

* 12 pages, 9 figures; accepted into AAAI'2016 

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Principled Graph Matching Algorithms for Integrating Multiple Data Sources


Feb 03, 2014
Duo Zhang, Benjamin I. P. Rubinstein, Jim Gemmell

* 14 pages, 11 figures 

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Security Evaluation of Support Vector Machines in Adversarial Environments


Jan 30, 2014
Battista Biggio, Igino Corona, Blaine Nelson, Benjamin I. P. Rubinstein, Davide Maiorca, Giorgio Fumera, Giorgio Giacinto, and Fabio Roli

* 47 pages, 9 figures; chapter accepted into book 'Support Vector Machine Applications' 

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Bounding Embeddings of VC Classes into Maximum Classes


Jan 29, 2014
J. Hyam Rubinstein, Benjamin I. P. Rubinstein, Peter L. Bartlett

* 22 pages, 2 figures 

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Scaling Multiple-Source Entity Resolution using Statistically Efficient Transfer Learning


Aug 09, 2012
Sahand Negahban, Benjamin I. P. Rubinstein, Jim Gemmell

* Short version to appear in CIKM'2012; 10 pages, 7 figures 

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A Bayesian Approach to Discovering Truth from Conflicting Sources for Data Integration


Mar 01, 2012
Bo Zhao, Benjamin I. P. Rubinstein, Jim Gemmell, Jiawei Han

* Proceedings of the VLDB Endowment (PVLDB), Vol. 5, No. 6, pp. 550-561 (2012) 
* VLDB2012 

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How Open Should Open Source Be?


Sep 02, 2011
Adam Barth, Saung Li, Benjamin I. P. Rubinstein, Dawn Song

* 19 pages, 27 figures 

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Link Prediction by De-anonymization: How We Won the Kaggle Social Network Challenge


Feb 22, 2011
Arvind Narayanan, Elaine Shi, Benjamin I. P. Rubinstein

* 11 pages, 13 figures; submitted to IJCNN'2011 

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Query Strategies for Evading Convex-Inducing Classifiers


Jul 03, 2010
Blaine Nelson, Benjamin I. P. Rubinstein, Ling Huang, Anthony D. Joseph, Steven J. Lee, Satish Rao, J. D. Tygar


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