Ben Rubinstein

Tracking, tracing, trust: contemplating mitigating the impact ofCOVID-19 through technological interventions

Christopher Culnane, Kobi Leins, Benjamin IP Rubinstein

Journal article  |  2020  |  Medical Journal of Australia

Tracking, tracing, trust: contemplating mitigating the impact ofCOVID-19 through technological interventions

Christopher Culnane, Kobi Leins, Benjamin IP Rubinstein

Journal article  |  2020  |  Medical Journal of Australia

Early identification of an impending rockslide location via a spatially-aided Gaussian mixture model

Shuo Zhou, Howard Bondell, Antoinette Tordesillas, Benjamin IP Rubinstein, James Bailey

Journal article  |  2020  |  Annals of Applied Statistics

Movement of soil and rocks in an unstable slope under gravitational forces is an example of a complex system that is highly dynami..

Sampling Without Compromising Accuracy in Adaptive Data Analysis

Benjamin Fish, Lev Reyzin, Benjamin Rubinstein

Conference Proceedings  |  2020  |  Proceedings of the 31st International Conference on Algorithmic Learning Theory

In this work, we study how to use sampling to speed up mechanisms for answering adaptive queries into datasets without reducing th..

Legion: Best-first concolic testing (competition contribution)

D Liu, G Ernst, T Murray, BIP Rubinstein

Conference Proceedings  |  2020  |  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Legion is a grey-box coverage-based concolic tool that aims to balance the complementary nature of fuzzing and symbolic execution ..

Function Interpolation for Learned Index Structures

NF Setiawan, BIP Rubinstein, R Borovica-Gajic

Conference Proceedings  |  2020  |  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Range indexes such as B-trees are widely recognised as effective data structures for enabling fast retrieval of records by the que..

Assessing Centrality Without Knowing Connections

Leyla Roohi, Benjamin IP Rubinstein, Vanessa Teague

Conference Proceedings  |  2020  |  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

We consider the privacy-preserving computation of node influence in distributed social networks, as measured by egocentric between..

Attacking Data Transforming Learners at Training Time

Scott Alfeld, Ara Vartanian, Lucas Newman-Johnson, Benjamin IP Rubinstein

Conference Proceedings  |  2019  |  Proceedings of the AAAI Conference on Artificial Intelligence

While machine learning systems are known to be vulnerable to data-manipulation attacks at both training and deployment time, littl..

Truth inference at scale: A Bayesian model for adjudicating highly redundant crowd annotations

Y Li, BIP Rubinstein, T Cohn

Conference Proceedings  |  2019  |  WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019)

Crowd-sourcing is a cheap and popular means of creating training and evaluation datasets for machine learning, however it poses th..

Differentially-Private Two-Party Egocentric Betweenness Centrality

L Roohi, BIP Rubinstein, V Teague

Conference Proceedings  |  2019  |  IEEE INFOCOM 2019 - IEEE Conference on Computer Communications

© 2019 IEEE. We describe a novel protocol for computing the egocentric betweenness centrality of a node when relevant edge informa..

Exploiting Worker Correlation for Label Aggregation in Crowdsourcing

Yuan LI, Benjamin Rubinstein, Trevor Cohn

Conference Proceedings  |  2019  |  Proceedings of the 36th International Conference on Machine Learning

Crowdsourcing has emerged as a core component of data science pipelines. From collected noisy worker labels, aggregation models th..

Adversarial Machine Learning

Anthony Joseph, Blaine Nelson, Benjamin Rubinstein, JD Tygar

Book  |  2019

Written by leading researchers, this complete introduction brings together all the theory and tools needed for building robust mac..

Exploiting worker correlation for label aggregation in crowdsourcing

Y Li, BIP Rubinstein, T Cohn

Conference Proceedings  |  2019  |  36th International Conference on Machine Learning, ICML 2019

Histogramming privately ever after: Differentially-private data-dependent error bound optimisation

Benjamin IP Rubinstein, Maryam FANAEEPOUR

Conference Proceedings  |  2018  |  2018 IEEE 34th International Conference on Data Engineering (ICDE)

© 2018 IEEE. Hay et al. (2016) recently observed that existing histogram release mechanisms under differential privacy do not prov..

PPFA: Privacy Preserving Fog-Enabled Aggregation in Smart Grid

Lingjuan Lyu, Karthik Nandakumar, Ben Rubinstein, Jiong Jin, Justin Bedo, Marimuthu Palaniswami

Journal article  |  2018  |  IEEE Transactions on Industrial Informatics

Sublinear-time adaptive data analysis

B Fish, L Reyzin, BIP Rubinstein

Conference Proceedings  |  2018  |  International Symposium on Artificial Intelligence and Mathematics, ISAIM 2018

Reinforcement learning for autonomous defence in software-defined networking

Y Han, BIP Rubinstein, T Abraham, T Alpcan, O De Vel, S Erfani, D Hubczenko, C Leckie, P Montague

Conference Proceedings  |  2018  |  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

© 2018, Springer Nature Switzerland AG. Despite the successful application of machine learning (ML) in a wide range of domains, ad..

Adequacy of the Gradient-Descent Method for Classifier Evasion Attacks

Y Han, B Rubinstein

Conference Proceedings  |  2018  |  The Workshops of the Thirty-First AAAI Conference on Artificial Intelligence

Despite the widespread use of machine learning in adversarial settings such as computer security, recent studies have demonstrated..

Differentially private counting of users' spatial regions

Maryam Fanaeepour, Benjamin IP Rubinstein

Journal article  |  2018  |  Knowledge and Information Systems

Fast manifold landmarking using locality-sensitive hashing

ZMM Aye, BIP Rubinstein, K Ramamohanarao

Conference Proceedings  |  2018  |  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

© Springer International Publishing AG, part of Springer Nature 2018. Manifold landmarks can approximately represent the low-dimen..

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

Neil G Marchant, Benjamin IP Rubinstein

Journal article  |  2017  |  Proceedings of the VLDB Endowment

Machine-Learning Algorithms Predict Graft Failure After Liver Transplantation

Lawrence Lau, Yamuna Kankanige, Benjamin Rubinstein, Robert Jones, Christopher Christophi, Vijayaragavan Muralidharan, James Bailey

Journal article  |  2017  |  Transplantation

Pain-free random differential privacy with sensitivity sampling

BIP Rubinstein, F Alda

Conference Proceedings  |  2017  |  34th International Conference on Machine Learning, ICML 2017

E-Storm: Replication-based State Management in Distributed Stream Processing Systems

Xunyun Liu, Aaron Harwood, Shanika Karunasekera, Benjamin Rubinstein, Rajkumar Buyya

Conference Proceedings  |  2017  |  2017 46th International Conference on Parallel Processing (ICPP)

Misconceptions as a trigger for enhancing student learning in higher education

H Verkade, TD Mulhern, J Lodge, K Elliott, S Cropper, B Rubinstein, A Horton, C Elliott, A Espinosa, L Dooley, S Frankland, R Mulder, M Livett

Book  |  2017

This handbook presents practical information for university educators looking to confront and correct misconceptions held by their..

Exploring misconceptions as a trigger for enhancing student learning

H Verkade, JM Lodge, K Elliott, TD Mulhern, AA Espinosa, SJ Cropper, BIP Rubinstein

Conference Proceedings  |  2017  |  Research and Development in Higher Education Series

This article addresses the importance of confronting misconceptions in the teaching of the STEM disciplines. First, we review the ..

Differential Privacy for Bayesian Inference through Posterior Sampling

Christos Dimitrakakis, Blaine Nelson, Zuhe Zhang, Aikaterini Mitrokotsa, Benjamin IP Rubinstein

Journal article  |  2017  |  Journal of Machine Learning Research

The bernstein mechanism: Function release under differential privacy

F Aldà, BIP Rubinstein

Conference Proceedings  |  2017  |  31st AAAI Conference on Artificial Intelligence, AAAI 2017

© Copyright 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. We address the p..

A Game Theoretical Approach to Defend Against Co-Resident Attacks in Cloud Computing: Preventing Co-Residence Using Semi-Supervised Learning

Yi Han, Tansu Alpcan, Jeffrey Chan, Christopher Leckie, Benjamin IP Rubinstein

Journal article  |  2016  |  IEEE Transactions on Information Forensics and Security

MOOCs Meet Measurement Theory: A Topic-Modelling Approach

J He, BIP Rubinstein, J BAILEY, R Zhang, S Milligan, J Chan

Conference Proceedings  |  2016  |  30th AAAI Conference on Artificial Intelligence, AAAI 2016

Validity: A Framework for Cross-disciplinary Collaboration in Mining Indicators of Learning from MOOC Forums

S Milligan, J He, J Bailey, R Zhang, BIP Rubinstein

Conference Proceedings  |  2016  |  Proceedings of the Sixth International Conference on Learning Analytics & Knowledge - LAK '16

Large Scale Metric Learning

Z Aye, R Kotagiri, B RUBINSTEIN

Conference Proceedings  |  2016  |  2016 International Joint Conference on Neural Networks (IJCNN)

Large-scale strategic games and adversarial machine learning

T Alpcan, BIP Rubinstein, C Leckie

Conference Proceedings  |  2016  |  2016 IEEE 55th Conference on Decision and Control (CDC)

Beyond Points and Paths: Counting Private Bodies

M Fanaeepour, B RUBINSTEIN

Conference Proceedings  |  2016  |  Proceedings - IEEE International Conference on Data Mining, ICDM

Fast Trajectory Clustering using Hashing Methods

Ivan Sanchez, Zay Maung Maung Aye, Benjamin IP Rubinstein, Kotagiri Ramamohanarao

Conference Proceedings  |  2016  |  2016 International Joint Conference on Neural Networks (IJCNN)

On the Differential Privacy of Bayesian Inference

Z Zhang, B RUBINSTEIN, C Dimitrakakis

Conference Proceedings  |  2016  |  30th AAAI Conference on Artificial Intelligence, AAAI 2016

The CASE histogram: privacy-aware processing of trajectory data using aggregates

Maryam Fanaeepour, Lars Kulik, Egemen Tanin, Benjamin IP Rubinstein

Journal article  |  2015  |  GeoInformatica

Principled Graph Matching Algorithms for Integrating Multiple Data Sources

Duo Zhang, Benjamin IP Rubinstein, Jim Gemmell

Journal article  |  2015  |  IEEE Transactions on Knowledge and Data Engineering

Sub-Merge: Diving Down to the Attribute-Value Level in Statistical Schema Matching

Z Lim, B RUBINSTEIN

Conference Proceedings  |  2015  |  PROCEEDINGS OF THE TWENTY-NINTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE

Matching and merging data from conflicting sources is the bread and butter of data integration, which drives search verticals, e-c..

Identifying At-Risk Students in Massive Open Online Courses

J He, J Bailey, B RUBINSTEIN, R Zhang

Conference Proceedings  |  2015  |  PROCEEDINGS OF THE TWENTY-NINTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE

Foreword

Knud Haakonssen

Conference Proceedings  |  2015  |  History of European Ideas

Bounding Embeddings of VC Classes into Maximum Classes

JH Rubinstein, B RUBINSTEIN, PL Bartlett

Book Chapter  |  2015

One of the earliest conjectures in computational learning theory-the Sample Compression conjecture-asserts that concept classes (e..

Attribute-Value-Level Matching

Z Lim, B RUBINSTEIN

Dataset  |  2014

We explore normalisation of attribute values across multiple data sources, where attributes could be categorical, numerical, other..

Security evaluation of support vector machines in adversarial environments

B Biggio, I Corona, B Nelson, BIP Rubinstein, D Maiorca, G Fumera, G Giacinto, F Roli

Book Chapter  |  2014

Robust and Private Bayesian Inference

C Dimitrakakis, B Nelson, A Mitrokotsa, B RUBINSTEIN

Conference Proceedings  |  2014  |  ALGORITHMIC LEARNING THEORY (ALT 2014)

We examine the robustness and privacy of Bayesian inference, under assumptions on the prior, and with no modifications to the Baye..

Foreword

C Dimitrakakis, A Mitrokotsa, B Rubinstein, GJ Ahn

Conference Proceedings  |  2014  |  Proceedings of the ACM Conference on Computer and Communications Security

Differential privacy and private Bayesian inference

C Dimitrakakis, B Nelson, A Mitrokotsa, BIP Rubinstein

Conference Proceedings  |  2014  |  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

On the challenges of balancing privacy and utility of open health data

C Guttmann, X Sun, C Rao, C Queiroz, BIP Rubinstein

Conference Proceedings  |  2013  |  Joint Proceedings of the Workshop on AI Problems and Approaches for Intelligent Environments and Workshop on Semantic Cities - AIIP '13

Scaling multiple-source entity resolution using statistically efficient transfer learning

SN Negahban, BIP Rubinstein, JG Gemmell

Conference Proceedings  |  2012  |  Proceedings of the 21st ACM international conference on Information and Knowledge Management

AISec 2012 foreword

AA Cárdenas, B Nelson, BIP Rubinstein

Conference Proceedings  |  2012  |  Proceedings of the ACM Conference on Computer and Communications Security

Fifth ACM Workshop on Artificial Intelligence and Security (AISec 2012)

AA Cárdenas, B Nelson, BIP Rubinstein

Conference Proceedings  |  2012  |  Proceedings of the 2012 ACM conference on Computer and communications security - CCS '12

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

Benjamin IP Rubinstein, Aleksandr Simma

Journal article  |  2012  |  IEEE Transactions on Information Theory

A Learning-Based Approach to Reactive Security

Adam Barth, Benjamin IP Rubinstein, Mukund Sundararajan, John C Mitchell, Dawn Song, Peter L Bartlett

Journal article  |  2012  |  IEEE Transactions on Dependable and Secure Computing

Query Strategies for Evading Convex-Inducing Classifiers

Blaine Nelson, Benjamin IP Rubinstein, Ling Huang, Anthony D Joseph, Steven J Lee, Satish Rao, JD Tygar

Journal article  |  2012  |  Journal of Machine Learning Research

A Geometric Approach to Sample Compression

Benjamin IP Rubinstein, J Hyam Rubinstein

Journal article  |  2012  |  Journal of Machine Learning Research

A bayesian approach to discovering truth from conflicting sources for data integration

B Zhao, BIP Rubinstein, J Gemmell, J Han

Journal article  |  2012  |  Proceedings of the VLDB Endowment

Adversarial machine learning

L Huang, AD Joseph, B Nelson, BIP Rubinstein, JD Tygar

Conference Proceedings  |  2011  |  Proceedings of the 4th ACM Workshop on Security and Artificial Intelligence

Link prediction by de-anonymization: How We Won the Kaggle Social Network Challenge

A Narayanan, E Shi, BIP Rubinstein

Conference Proceedings  |  2011  |  Proceedings of the 2011 International Joint Conference on Neural Networks

Classifier evasion: Models and open problems

B Nelson, BIP Rubinstein, L Huang, AD Joseph, JD Tygar

Book Chapter  |  2011  |  Lecture Notes in Artificial Intelligence

Near-optimal evasion of convex-inducing classifiers

B Nelson, BIP Rubinstein, L Huang, AD Joseph, SH Lau, SJ Lee, S Rao, A Tran, JD Tygar

Conference Proceedings  |  2010  |  Proceedings of the 13th International Conference on Artificial Intelligence and Statistics

Shifting: One-inclusion mistake bounds and sample compression (vol 75, pg 37, 2009)

Benjamin IP Rubinstein, Peter L Bartlett, J Hyam Rubinstein

Journal article  |  2010  |  Journal of Computer and System Sciences

A Learning-Based Approach to Reactive Security

Adam Barth, Benjamin IP Rubinstein, Mukund Sundararajan, John C Mitchell, Dawn Song, Peter L Bartlett

Conference Proceedings  |  2010  |  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Adaptive bidding for display advertising

A Ghosh, BIP Rubinstein, S Vassilvitskii, M Zinkevich

Conference Proceedings  |  2009  |  Proceedings of the 18th International Conference on World Wide Web

Misleading learners: Co-opting your spam filter

B Nelson, M Barreno, F Jack Chi, AD Joseph, BIP Rubinstein, U Saini, C Sutton, JD Tygar, K Xia

Book Chapter  |  2009

Stealthy poisoning attacks on PCA-based anomaly detectors

BIP Rubinstein, B Nelson, L Huang, AD Joseph, SH Lau, S Rao, N Taft, JD Tygar

Conference Proceedings  |  2009  |  ACM SIGMETRICS Performance Evaluation Review

Shifting: One-inclusion mistake bounds and sample compression

Benjamin IP Rubinstein, Peter L Bartlett, J Hyam Rubinstein

Journal article  |  2009  |  Journal of Computer and System Sciences

ANTIDOTE: Understanding and Defending against Poisoning of Anomaly Detectors

Benjamin IP Rubinstein, Blaine Nelson, Ling Huang, Anthony D Joseph, Shing-hon Lau, Satish Rao, Nina Taft, JD Tygar

Conference Proceedings  |  2009  |  Proceedings of the 9th ACM SIGCOMM Internet Measurement Conference

Open problems in the security of learning

M Barreno, PL Bartlett, FJ Chi, AD Joseph, B Nelson, BIP Rubinstein, U Saini, JD Tygar

Conference Proceedings  |  2008  |  Proceedings of the 1st ACM workshop on Workshop on AISec - AISec '08

Geometric & topological representations of maximum classes with applications to sample compression

BIP Rubinstein, JH Rubinstein

Conference Proceedings  |  2008  |  21st Annual Conference on Learning Theory, COLT 2008

Evading anomaly detection through variance injection attacks on PCA

BIP Rubinstein, B Nelson, L Huang, AD Joseph, SH Lau, N Taft, JD Tygar

Conference Proceedings  |  2008  |  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Exploiting machine learning to subvert your spam filter

B Nelson, M Barreno, FJ Chi, AD Joseph, BIP Rubinstein, U Saini, C Sutton, JD Tygar, K Xia

Conference Proceedings  |  2008  |  LEET 2008 - 1st USENIX Workshop on Large-Scale Exploits and Emergent Threats: Botnets, Spyware, Worms, and More

Evading Anomaly Detection through Variance Injection Attacks on PCA (Extended Abstract)

Benjamin IP Rubinstein, Blaine Nelson, Ling Huang, Anthony D Joseph, Shing-hon Lau, Nina Taft, JD Tygar

Conference Proceedings  |  2008  |  RECENT ADVANCES IN INTRUSION DETECTION, RAID 2008

Exploiting machine learning to subvert your spam filter

B Nelson, M Barreno, FJ Chi, AD Joseph, BIP Rubinstein, U Saini, C Sutton, JD Tygar, K Xia

Conference Proceedings  |  2008  |  LEET 2008 - 1st USENIX Workshop on Large-Scale Exploits and Emergent Threats: Botnets, Spyware, Worms, and More

Shifting, one-inclusion mistake bounds and tight multiclass expected risk bounds

BIP Rubinstein, PL Bartlett, JH Rubinstein

Conference Proceedings  |  2007  |  Advances in Neural Information Processing Systems

Machine learning in low-level microarray analysis

B RUBINSTEIN, J McAuliffe, S Cawley, M PALANISWAMI, R KOTAGIRI, TP Speed

Journal article  |  2003  |  SIGKDD Explorations

Evolving quantum circuits using genetic programming

BIP Rubinstein

Conference Proceedings  |  2001  |  Proceedings of the IEEE Conference on Evolutionary Computation, ICEC

Learning in a Large Function Space: Privacy-Preserving Mechanisms for SVM Learning

Benjamin IP Rubinstein, Peter L Bartlett, Ling Huang, Nina Taft

Journal article  |  Journal of Privacy and Confidentiality

The ubiquitous need for analyzing privacy-sensitive information—including health records, personal communications, product ratings..