Benjamin I. P. Rubinstein

Affiliations:
  • University of Melbourne, School of Computing and Information Systems, Australia
  • Microsoft Research (former)
  • University of California Berkeley, CA, USA (PhD 2010)


According to our database1, Benjamin I. P. Rubinstein authored at least 127 papers between 2001 and 2024.

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Bibliography

2024
To Act or Not to Act: An Adversarial Game for Securing Vehicle Platoons.
IEEE Trans. Inf. Forensics Secur., 2024

Elephants Do Not Forget: Differential Privacy with State Continuity for Privacy Budget.
CoRR, 2024

2023
No DBA? No Regret! Multi-Armed Bandits for Index Tuning of Analytical and HTAP Workloads With Provable Guarantees.
IEEE Trans. Knowl. Data Eng., December, 2023

Predicting dynamic spectrum allocation: a review covering simulation, modelling, and prediction.
Artif. Intell. Rev., October, 2023

Cutting to the chase with warm-start contextual bandits.
Knowl. Inf. Syst., September, 2023

It's Simplex! Disaggregating Measures to Improve Certified Robustness.
CoRR, 2023

IMBERT: Making BERT Immune to Insertion-based Backdoor Attacks.
CoRR, 2023

Exploiting Certified Defences to Attack Randomised Smoothing.
CoRR, 2023

Certified Robustness of Learning-based Static Malware Detectors.
CoRR, 2023

Bayesian Graphical Entity Resolution Using Exchangeable Random Partition Priors.
CoRR, 2023

RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized Deletion.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Efficient Index Learning via Model Reuse and Fine-tuning.
Proceedings of the 39th IEEE International Conference on Data Engineering, ICDE 2023, 2023

Beyond the Coverage Plateau: A Comprehensive Study of Fuzz Blockers (Registered Report).
Proceedings of the 2nd International Fuzzing Workshop, 2023

Mitigating Backdoor Poisoning Attacks through the Lens of Spurious Correlation.
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

An Adversarial Strategic Game for Machine Learning as a Service using System Features.
Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

Enhancing the Antidote: Improved Pointwise Certifications against Poisoning Attacks.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
A Graph Symmetrization Bound on Channel Information Leakage Under Blowfish Privacy.
IEEE Trans. Inf. Theory, 2022

HMAB: Self-Driving Hierarchy of Bandits for Integrated Physical Database Design Tuning.
Proc. VLDB Endow., 2022

Testing the Robustness of Learned Index Structures.
CoRR, 2022

Getting a-Round Guarantees: Floating-Point Attacks on Certified Robustness.
CoRR, 2022

State Selection Algorithms and Their Impact on The Performance of Stateful Network Protocol Fuzzing.
Proceedings of the IEEE International Conference on Software Analysis, 2022

Are We There Yet? Timing and Floating-Point Attacks on Differential Privacy Systems.
Proceedings of the 43rd IEEE Symposium on Security and Privacy, 2022

Securing Cyber-Physical Systems: Physics-Enhanced Adversarial Learning for Autonomous Platoons.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022

Unlabelled Sample Compression Schemes for Intersection-Closed Classes and Extremal Classes.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Double Bubble, Toil and Trouble: Enhancing Certified Robustness through Transitivity.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Foiling Training-Time Attacks on Neural Machine Translation Systems.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2022, 2022

Local Intrinsic Dimensionality Signals Adversarial Perturbations.
Proceedings of the 61st IEEE Conference on Decision and Control, 2022

Measuring and Mitigating Name Biases in Neural Machine Translation.
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022

Hard to Forget: Poisoning Attacks on Certified Machine Unlearning.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
d-blink: Distributed End-to-End Bayesian Entity Resolution.
J. Comput. Graph. Stat., 2021

Local Intrinsic Dimensionality Signals Adversarial Perturbations.
CoRR, 2021

TRS: Transferability Reduced Ensemble via Encouraging Gradient Diversity and Model Smoothness.
CoRR, 2021

Machine Learning in Network Anomaly Detection: A Survey.
IEEE Access, 2021

A Targeted Attack on Black-Box Neural Machine Translation with Parallel Data Poisoning.
Proceedings of the WWW '21: The Web Conference 2021, 2021

Strategic Mitigation Against Wireless Attacks on Autonomous Platoons.
Proceedings of the Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track, 2021

TRS: Transferability Reduced Ensemble via Promoting Gradient Diversity and Model Smoothness.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Needle in a Haystack: Label-Efficient Evaluation under Extreme Class Imbalance.
Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

Towards Systematic and Dynamic Task Allocation for Collaborative Parallel Fuzzing.
Proceedings of the 36th IEEE/ACM International Conference on Automated Software Engineering, 2021

Closing the BIG-LID: An Effective Local Intrinsic Dimensionality Defense for Nonlinear Regression Poisoning.
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

DBA bandits: Self-driving index tuning under ad-hoc, analytical workloads with safety guarantees.
Proceedings of the 37th IEEE International Conference on Data Engineering, 2021

Mitigating Data Poisoning in Text Classification with Differential Privacy.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2021, 2021

A Communication Security Game on Switched Systems for Autonomous Vehicle Platoons.
Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021

Putting words into the system's mouth: A targeted attack on neural machine translation using monolingual data poisoning.
Proceedings of the Findings of the Association for Computational Linguistics: ACL/IJCNLP 2021, 2021

As Easy as 1, 2, 3: Behavioural Testing of NMT Systems for Numerical Translation.
Proceedings of the Findings of the Association for Computational Linguistics: ACL/IJCNLP 2021, 2021

Invertible Concept-based Explanations for CNN Models with Non-negative Concept Activation Vectors.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
Not fit for Purpose: A critical analysis of the 'Five Safes'.
CoRR, 2020

Targeted Poisoning Attacks on Black-Box Neural Machine Translation.
CoRR, 2020

A Graph Symmetrisation Bound on Channel Information Leakage under Blowfish Privacy.
CoRR, 2020

Improving Interpretability of CNN Models Using Non-Negative Concept Activation Vectors.
CoRR, 2020

Discrete Few-Shot Learning for Pan Privacy.
CoRR, 2020

A general framework for label-efficient online evaluation with asymptotic guarantees.
CoRR, 2020

Assessing Centrality Without Knowing Connections.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2020

LEGION: Best-First Concolic Testing.
Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering, 2020

Adversarial Reinforcement Learning under Partial Observability in Autonomous Computer Network Defence.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

Legion: Best-First Concolic Testing (Competition Contribution).
Proceedings of the Fundamental Approaches to Software Engineering, 2020

Sampling Without Compromising Accuracy in Adaptive Data Analysis.
Proceedings of the Algorithmic Learning Theory, 2020

Function Interpolation for Learned Index Structures.
Proceedings of the Databases Theory and Applications, 2020

2019
Stop the Open Data Bus, We Want to Get Off.
CoRR, 2019

Adversarial Reinforcement Learning under Partial Observability in Software-Defined Networking.
CoRR, 2019

A Note on Bounding Regret of the C$^2$UCB Contextual Combinatorial Bandit.
CoRR, 2019

Truth Inference at Scale: A Bayesian Model for Adjudicating Highly Redundant Crowd Annotations.
Proceedings of the World Wide Web Conference, 2019

Differentially-Private Two-Party Egocentric Betweenness Centrality.
Proceedings of the 2019 IEEE Conference on Computer Communications, 2019

Exploiting Worker Correlation for Label Aggregation in Crowdsourcing.
Proceedings of the 36th International Conference on Machine Learning, 2019

Attacking Data Transforming Learners at Training Time.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018
PPFA: Privacy Preserving Fog-Enabled Aggregation in Smart Grid.
IEEE Trans. Ind. Informatics, 2018

Differentially private counting of users' spatial regions.
Knowl. Inf. Syst., 2018

Options for encoding names for data linking at the Australian Bureau of Statistics.
CoRR, 2018

Fast Manifold Landmarking Using Locality-Sensitive Hashing.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2018

Sublinear-Time Adaptive Data Analysis.
Proceedings of the International Symposium on Artificial Intelligence and Mathematics, 2018

Histogramming Privately Ever After: Differentially-Private Data-Dependent Error Bound Optimisation.
Proceedings of the 34th IEEE International Conference on Data Engineering, 2018

Reinforcement Learning for Autonomous Defence in Software-Defined Networking.
Proceedings of the Decision and Game Theory for Security - 9th International Conference, 2018

Adequacy of the Gradient-Descent Method for Classifier Evasion Attacks.
Proceedings of the Workshops of the The Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017
In Search of an Entity Resolution OASIS: Optimal Asymptotic Sequential Importance Sampling.
Proc. VLDB Endow., 2017

Differential Privacy for Bayesian Inference through Posterior Sampling.
J. Mach. Learn. Res., 2017

Health Data in an Open World.
CoRR, 2017

Vulnerabilities in the use of similarity tables in combination with pseudonymisation to preserve data privacy in the UK Office for National Statistics' Privacy-Preserving Record Linkage.
CoRR, 2017

End-to-End Differentially-Private Parameter Tuning in Spatial Histograms.
CoRR, 2017

Privacy Assessment of De-identified Opal Data: A report for Transport for NSW.
CoRR, 2017

E-Storm: Replication-Based State Management in Distributed Stream Processing Systems.
Proceedings of the 46th International Conference on Parallel Processing, 2017

Pain-Free Random Differential Privacy with Sensitivity Sampling.
Proceedings of the 34th International Conference on Machine Learning, 2017

The Bernstein Mechanism: Function Release under Differential Privacy.
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016
A Game Theoretical Approach to Defend Against Co-Resident Attacks in Cloud Computing: Preventing Co-Residence Using Semi-Supervised Learning.
IEEE Trans. Inf. Forensics Secur., 2016

TopicResponse: A Marriage of Topic Modelling and Rasch Modelling for Automatic Measurement in MOOCs.
CoRR, 2016

Validity: a framework for cross-disciplinary collaboration in mining indicators of learning from MOOC forums.
Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, 2016

Fast trajectory clustering using Hashing methods.
Proceedings of the 2016 International Joint Conference on Neural Networks, 2016

Large Scale Metric learning.
Proceedings of the 2016 International Joint Conference on Neural Networks, 2016

Beyond Points and Paths: Counting Private Bodies.
Proceedings of the IEEE 16th International Conference on Data Mining, 2016

Large-scale strategic games and adversarial machine learning.
Proceedings of the 55th IEEE Conference on Decision and Control, 2016

On the Differential Privacy of Bayesian Inference.
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

MOOCs Meet Measurement Theory: A Topic-Modelling Approach.
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015
Principled Graph Matching Algorithms for Integrating Multiple Data Sources.
IEEE Trans. Knowl. Data Eng., 2015

The CASE histogram: privacy-aware processing of trajectory data using aggregates.
GeoInformatica, 2015

Sub-Merge: Diving Down to the Attribute-Value Level in Statistical Schema Matching.
Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

Identifying At-Risk Students in Massive Open Online Courses.
Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014
Bounding Embeddings of VC Classes into Maximum Classes.
CoRR, 2014

Security Evaluation of Support Vector Machines in Adversarial Environments.
CoRR, 2014

Workshop Summary of AISec'14: 2014 Workshop on Artificial Intelligent and Security.
Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, 2014

Robust and Private Bayesian Inference.
Proceedings of the Algorithmic Learning Theory - 25th International Conference, 2014

2013
Robust, Secure and Private Bayesian Inference.
CoRR, 2013

On the challenges of balancing privacy and utility of open health data.
Proceedings of the Joint Proceedings of the Workshop on AI Problems and Approaches for Intelligent Environments and Workshop on Semantic Cities, 2013

2012
On the Stability of Empirical Risk Minimization in the Presence of Multiple Risk Minimizers.
IEEE Trans. Inf. Theory, 2012

A Learning-Based Approach to Reactive Security.
IEEE Trans. Dependable Secur. Comput., 2012

A Bayesian Approach to Discovering Truth from Conflicting Sources for Data Integration.
Proc. VLDB Endow., 2012

Learning in a Large Function Space: Privacy-Preserving Mechanisms for SVM Learning.
J. Priv. Confidentiality, 2012

A Geometric Approach to Sample Compression.
J. Mach. Learn. Res., 2012

Query Strategies for Evading Convex-Inducing Classifiers.
J. Mach. Learn. Res., 2012

Scaling multiple-source entity resolution using statistically efficient transfer learning.
Proceedings of the 21st ACM International Conference on Information and Knowledge Management, 2012

Fifth ACM workshop on artificial intelligence and security (AISec 2012).
Proceedings of the ACM Conference on Computer and Communications Security, 2012

2011
How Open Should Open Source Be?
CoRR, 2011

Improving Entity Resolution with Global Constraints
CoRR, 2011

Link prediction by de-anonymization: How We Won the Kaggle Social Network Challenge.
Proceedings of the 2011 International Joint Conference on Neural Networks, 2011

Adversarial machine learning.
Proceedings of the 4th ACM Workshop on Security and Artificial Intelligence, 2011

2010
Secure Learning and Learning for Security: Research in the Intersection.
PhD thesis, 2010

Near-Optimal Evasion of Convex-Inducing Classifiers.
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Corrigendum to "Shifting: One-inclusion mistake bounds and sample compression" [J. Comput. System Sci 75 (1) (2009) 37-59].
J. Comput. Syst. Sci., 2010

Classifier Evasion: Models and Open Problems.
Proceedings of the Privacy and Security Issues in Data Mining and Machine Learning, 2010

2009
Stealthy poisoning attacks on PCA-based anomaly detectors.
SIGMETRICS Perform. Evaluation Rev., 2009

Shifting: One-inclusion mistake bounds and sample compression.
J. Comput. Syst. Sci., 2009

Adaptive bidding for display advertising.
Proceedings of the 18th International Conference on World Wide Web, 2009

ANTIDOTE: understanding and defending against poisoning of anomaly detectors.
Proceedings of the 9th ACM SIGCOMM Internet Measurement Conference, IMC 2009, Chicago, 2009

2008
Evading Anomaly Detection through Variance Injection Attacks on PCA.
Proceedings of the Recent Advances in Intrusion Detection, 11th International Symposium, 2008

Exploiting Machine Learning to Subvert Your Spam Filter.
Proceedings of the First USENIX Workshop on Large-Scale Exploits and Emergent Threats, 2008

Geometric & Topological Representations of Maximum Classes with Applications to Sample Compression.
Proceedings of the 21st Annual Conference on Learning Theory, 2008

Open problems in the security of learning.
Proceedings of the 1st ACM Workshop on Security and Artificial Intelligence, 2008

2006
Shifting, One-Inclusion Mistake Bounds and Tight Multiclass Expected Risk Bounds.
Proceedings of the Advances in Neural Information Processing Systems 19, 2006

2003
Machine learning in low-level microarray analysis.
SIGKDD Explor., 2003

2001
Evolving quantum circuits using genetic programming.
Proceedings of the 2001 Congress on Evolutionary Computation, 2001


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