Evolving quantum circuits using genetic programming. - Benjamin I. P. Rubinstein (CEC, 2001)

Machine learning in low-level microarray analysis. - Benjamin I. P. Rubinstein; Jon D. McAuliffe; Simon Cawley; Marimuthu Palaniswami; Kotagiri Ramamohanarao; Terence P. Speed (SIGKDD Explor., 2003)

Shifting, One-Inclusion Mistake Bounds and Tight Multiclass Expected Risk Bounds. - Benjamin I. P. Rubinstein; Peter L. Bartlett; J. Hyam Rubinstein (NIPS, 2006)

Open problems in the security of learning. - Marco Barreno; Peter L. Bartlett; Fuching Jack Chi; Anthony D. Joseph; Blaine Nelson; Benjamin I. P. Rubinstein; Udam Saini; J. Doug Tygar (AISec, 2008)

Geometric & Topological Representations of Maximum Classes with Applications to Sample Compression. - J. Hyam Rubinstein; Benjamin I. P. Rubinstein (COLT, 2008)

Exploiting Machine Learning to Subvert Your Spam Filter. - Blaine Nelson; Marco Barreno; Fuching Jack Chi; Anthony D. Joseph; Benjamin I. P. Rubinstein; Udam Saini; Charles Sutton; J. Doug Tygar; Kai Xia (LEET, 2008)

Evading Anomaly Detection through Variance Injection Attacks on PCA. - Benjamin I. P. Rubinstein; Blaine Nelson; Ling Huang 001; Anthony D. Joseph; Shing-hon Lau; Nina Taft; J. Doug Tygar (RAID, 2008)

ANTIDOTE: understanding and defending against poisoning of anomaly detectors. - Benjamin I. P. Rubinstein; Blaine Nelson; Ling Huang 001; Anthony D. Joseph; Shing-hon Lau; Satish Rao; Nina Taft; J. D. Tygar (Internet Measurement Conference, 2009)

Adaptive bidding for display advertising. - Arpita Ghosh; Benjamin I. P. Rubinstein; Sergei Vassilvitskii; Martin Zinkevich (WWW, 2009)

Shifting: One-inclusion mistake bounds and sample compression. - Benjamin I. P. Rubinstein; Peter L. Bartlett; J. Hyam Rubinstein (J. Comput. Syst. Sci., 2009)

Stealthy poisoning attacks on PCA-based anomaly detectors. - Benjamin I. P. Rubinstein; Blaine Nelson; Ling Huang 001; Anthony D. Joseph; Shing-hon Lau; Satish Rao; Nina Taft; J. D. Tygar (SIGMETRICS Perform. Evaluation Rev., 2009)

Classifier Evasion: Models and Open Problems. - Blaine Nelson; Benjamin I. P. Rubinstein; Ling Huang 001; Anthony D. Joseph; J. D. Tygar (PSDML, 2010)

Corrigendum to "Shifting: One-inclusion mistake bounds and sample compression" [J. Comput. System Sci 75 (1) (2009) 37-59]. - Benjamin I. P. Rubinstein; Peter L. Bartlett; J. Hyam Rubinstein (J. Comput. Syst. Sci., 2010)

Near-Optimal Evasion of Convex-Inducing Classifiers. - Blaine Nelson; Benjamin I. P. Rubinstein; Ling Huang 001; Anthony D. Joseph; Shing-hon Lau; Steven J. Lee; Satish Rao; Anthony Tran; J. Doug Tygar (AISTATS, 2010)

Secure Learning and Learning for Security: Research in the Intersection. - Benjamin I. P. Rubinstein (2010)

Adversarial machine learning. - Ling Huang 001; Anthony D. Joseph; Blaine Nelson; Benjamin I. P. Rubinstein; J. D. Tygar (AISec, 2011)

Link prediction by de-anonymization: How We Won the Kaggle Social Network Challenge. - Arvind Narayanan; Elaine Shi; Benjamin I. P. Rubinstein (IJCNN, 2011)

Improving Entity Resolution with Global Constraints - Jim Gemmell; Benjamin I. P. Rubinstein; Ashok K. Chandra (CoRR, 2011)

How Open Should Open Source Be? - Adam Barth; Saung Li; Benjamin I. P. Rubinstein; Dawn Song (CoRR, 2011)

Fifth ACM workshop on artificial intelligence and security (AISec 2012). - Alvaro A. Cárdenas; Blaine Nelson; Benjamin I. P. Rubinstein (CCS, 2012)

Scaling multiple-source entity resolution using statistically efficient transfer learning. - Sahand Negahban; Benjamin I. P. Rubinstein; Jim Gemmell (CIKM, 2012)

Query Strategies for Evading Convex-Inducing Classifiers. - Blaine Nelson; Benjamin I. P. Rubinstein; Ling Huang 001; Anthony D. Joseph; Steven J. Lee; Satish Rao; J. D. Tygar (J. Mach. Learn. Res., 2012)

A Geometric Approach to Sample Compression. - Benjamin I. P. Rubinstein; J. Hyam Rubinstein (J. Mach. Learn. Res., 2012)

Learning in a Large Function Space: Privacy-Preserving Mechanisms for SVM Learning. - Benjamin I. P. Rubinstein; Peter L. Bartlett; Ling Huang 001; Nina Taft (J. Priv. Confidentiality, 2012)

A Bayesian Approach to Discovering Truth from Conflicting Sources for Data Integration. - Bo Zhao 001; Benjamin I. P. Rubinstein; Jim Gemmell; Jiawei Han 001 (Proc. VLDB Endow., 2012)

A Learning-Based Approach to Reactive Security. - Adam Barth; Benjamin I. P. Rubinstein; Mukund Sundararajan; John C. Mitchell; Dawn Song; Peter L. Bartlett (IEEE Trans. Dependable Secur. Comput., 2012)

On the Stability of Empirical Risk Minimization in the Presence of Multiple Risk Minimizers. - Benjamin I. P. Rubinstein; Aleksandr Simma (IEEE Trans. Inf. Theory, 2012)

On the challenges of balancing privacy and utility of open health data. - Christian Guttmann; Xingzhi Sun 001; Chaitanya Rao; Carlos Queiroz; Benjamin I. P. Rubinstein (AIIP/Semantic Cities@IJCAI, 2013)

Robust, Secure and Private Bayesian Inference. - Christos Dimitrakakis; Blaine Nelson; Aikaterini Mitrokotsa; Benjamin I. P. Rubinstein (CoRR, 2013)

Robust and Private Bayesian Inference. - Christos Dimitrakakis; Blaine Nelson; Aikaterini Mitrokotsa; Benjamin I. P. Rubinstein (ALT, 2014)

Workshop Summary of AISec'14: 2014 Workshop on Artificial Intelligent and Security. - Christos Dimitrakakis; Aikaterini Mitrokotsa; Benjamin I. P. Rubinstein (CCS, 2014)

Security Evaluation of Support Vector Machines in Adversarial Environments. - Battista Biggio; Igino Corona; Blaine Nelson; Benjamin I. P. Rubinstein; Davide Maiorca; Giorgio Fumera; Giorgio Giacinto; Fabio Roli (CoRR, 2014)

Bounding Embeddings of VC Classes into Maximum Classes. - J. Hyam Rubinstein; Benjamin I. P. Rubinstein; Peter L. Bartlett (CoRR, 2014)

Identifying At-Risk Students in Massive Open Online Courses. - Jiazhen He; James Bailey 001; Benjamin I. P. Rubinstein; Rui Zhang 003 (AAAI, 2015)

Sub-Merge: Diving Down to the Attribute-Value Level in Statistical Schema Matching. - Zhe Lim; Benjamin I. P. Rubinstein (AAAI, 2015)

The CASE histogram: privacy-aware processing of trajectory data using aggregates. - Maryam Fanaeepour; Lars Kulik; Egemen Tanin; Benjamin I. P. Rubinstein (GeoInformatica, 2015)

Principled Graph Matching Algorithms for Integrating Multiple Data Sources. - Duo Zhang; Benjamin I. P. Rubinstein; Jim Gemmell (IEEE Trans. Knowl. Data Eng., 2015)

MOOCs Meet Measurement Theory: A Topic-Modelling Approach. - Jiazhen He; Benjamin I. P. Rubinstein; James Bailey 001; Rui Zhang 003; Sandra Milligan; Jeffrey Chan (AAAI, 2016)

On the Differential Privacy of Bayesian Inference. - Zuhe Zhang; Benjamin I. P. Rubinstein; Christos Dimitrakakis (AAAI, 2016)

Large-scale strategic games and adversarial machine learning. - Tansu Alpcan; Benjamin I. P. Rubinstein; Christopher Leckie (CDC, 2016)

Beyond Points and Paths: Counting Private Bodies. - Maryam Fanaeepour; Benjamin I. P. Rubinstein (ICDM, 2016)

Large Scale Metric learning. - Zay Maung Maung Aye; Kotagiri Ramamohanarao; Benjamin I. P. Rubinstein (IJCNN, 2016)

Fast trajectory clustering using Hashing methods. - Iván Sánchez; Zay Maung Maung Aye; Benjamin I. P. Rubinstein; Kotagiri Ramamohanarao (IJCNN, 2016)

Validity: a framework for cross-disciplinary collaboration in mining indicators of learning from MOOC forums. - Sandra Milligan; Jiazhen He; James Bailey 001; Rui Zhang 003; Benjamin I. P. Rubinstein (LAK, 2016)

TopicResponse: A Marriage of Topic Modelling and Rasch Modelling for Automatic Measurement in MOOCs. - Jiazhen He; Rui Zhang 003; James Bailey 001; Benjamin I. P. Rubinstein; Sandra Milligan (CoRR, 2016)

A Game Theoretical Approach to Defend Against Co-Resident Attacks in Cloud Computing: Preventing Co-Residence Using Semi-Supervised Learning. - Yi Han 003; Tansu Alpcan; Jeffrey Chan; Christopher Leckie; Benjamin I. P. Rubinstein (IEEE Trans. Inf. Forensics Secur., 2016)

The Bernstein Mechanism: Function Release under Differential Privacy. - Francesco Aldà; Benjamin I. P. Rubinstein (AAAI, 2017)

Pain-Free Random Differential Privacy with Sensitivity Sampling. - Benjamin I. P. Rubinstein; Francesco Aldà (ICML, 2017)

E-Storm: Replication-Based State Management in Distributed Stream Processing Systems. - Xunyun Liu; Aaron Harwood; Shanika Karunasekera; Benjamin I. P. Rubinstein; Rajkumar Buyya (ICPP, 2017)

Privacy Assessment of De-identified Opal Data: A report for Transport for NSW. - Chris Culnane; Benjamin I. P. Rubinstein; Vanessa Teague (CoRR, 2017)

End-to-End Differentially-Private Parameter Tuning in Spatial Histograms. - Maryam Fanaeepour; Benjamin I. P. Rubinstein (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. - Chris Culnane; Benjamin I. P. Rubinstein; Vanessa Teague (CoRR, 2017)

Health Data in an Open World. - Chris Culnane; Benjamin I. P. Rubinstein; Vanessa Teague (CoRR, 2017)

Differential Privacy for Bayesian Inference through Posterior Sampling. - Christos Dimitrakakis; Blaine Nelson; Zuhe Zhang; Aikaterini Mitrokotsa; Benjamin I. P. Rubinstein (J. Mach. Learn. Res., 2017)

In Search of an Entity Resolution OASIS: Optimal Asymptotic Sequential Importance Sampling. - Neil G. Marchant; Benjamin I. P. Rubinstein (Proc. VLDB Endow., 2017)

Adequacy of the Gradient-Descent Method for Classifier Evasion Attacks. - Yi Han 003; Benjamin I. P. Rubinstein (AAAI Workshops, 2018)

Reinforcement Learning for Autonomous Defence in Software-Defined Networking. - Yi Han 003; Benjamin I. P. Rubinstein; Tamas Abraham; Tansu Alpcan; Olivier Y. de Vel; Sarah M. Erfani; David Hubczenko; Christopher Leckie; Paul Montague (GameSec, 2018)

Histogramming Privately Ever After: Differentially-Private Data-Dependent Error Bound Optimisation. - Maryam Fanaeepour; Benjamin I. P. Rubinstein (ICDE, 2018)

Sublinear-Time Adaptive Data Analysis. - Benjamin Fish; Lev Reyzin; Benjamin I. P. Rubinstein (ISAIM, 2018)

Fast Manifold Landmarking Using Locality-Sensitive Hashing. - Zay Maung Maung Aye; Benjamin I. P. Rubinstein; Kotagiri Ramamohanarao (PAKDD (3), 2018)

Options for encoding names for data linking at the Australian Bureau of Statistics. - Chris Culnane; Benjamin I. P. Rubinstein; Vanessa Teague (CoRR, 2018)

Differentially private counting of users' spatial regions. - Maryam Fanaeepour; Benjamin I. P. Rubinstein (Knowl. Inf. Syst., 2018)

PPFA: Privacy Preserving Fog-Enabled Aggregation in Smart Grid. - Lingjuan Lyu; Karthik Nandakumar; Benjamin I. P. Rubinstein; Jiong Jin; Justin Bedo; Marimuthu Palaniswami (IEEE Trans. Ind. Informatics, 2018)

Attacking Data Transforming Learners at Training Time. - Scott Alfeld; Ara Vartanian; Lucas Newman-Johnson; Benjamin I. P. Rubinstein (AAAI, 2019)

Exploiting Worker Correlation for Label Aggregation in Crowdsourcing. - Yuan Li 012; Benjamin I. P. Rubinstein; Trevor Cohn (ICML, 2019)

Differentially-Private Two-Party Egocentric Betweenness Centrality. - Leyla Roohi; Benjamin I. P. Rubinstein; Vanessa Teague (INFOCOM, 2019)

Truth Inference at Scale: A Bayesian Model for Adjudicating Highly Redundant Crowd Annotations. - Yuan Li 012; Benjamin I. P. Rubinstein; Trevor Cohn (WWW, 2019)

A Note on Bounding Regret of the C$^2$UCB Contextual Combinatorial Bandit. - Bastian Oetomo; Malinga Perera; Renata Borovica-Gajic; Benjamin I. P. Rubinstein (CoRR, 2019)

Adversarial Reinforcement Learning under Partial Observability in Software-Defined Networking. - Yi Han 003; David Hubczenko; Paul Montague; Olivier Y. de Vel; Tamas Abraham; Benjamin I. P. Rubinstein; Christopher Leckie; Tansu Alpcan; Sarah M. Erfani (CoRR, 2019)

Stop the Open Data Bus, We Want to Get Off. - Chris Culnane; Benjamin I. P. Rubinstein; Vanessa Teague (CoRR, 2019)

Function Interpolation for Learned Index Structures. - Naufal Fikri Setiawan; Benjamin I. P. Rubinstein; Renata Borovica-Gajic (ADC, 2020)

Sampling Without Compromising Accuracy in Adaptive Data Analysis. - Benjamin Fish; Lev Reyzin; Benjamin I. P. Rubinstein (ALT, 2020)

Legion: Best-First Concolic Testing (Competition Contribution). - Dongge Liu; Gidon Ernst; Toby Murray; Benjamin I. P. Rubinstein (FASE, 2020)

Adversarial Reinforcement Learning under Partial Observability in Autonomous Computer Network Defence. - Yi Han 003; David Hubczenko; Paul Montague; Olivier Y. de Vel; Tamas Abraham; Benjamin I. P. Rubinstein; Christopher Leckie; Tansu Alpcan; Sarah M. Erfani (IJCNN, 2020)

LEGION: Best-First Concolic Testing. - Dongge Liu; Gidon Ernst; Toby Murray; Benjamin I. P. Rubinstein (ASE, 2020)

Assessing Centrality Without Knowing Connections. - Leyla Roohi; Benjamin I. P. Rubinstein; Vanessa Teague (PAKDD (2), 2020)

A general framework for label-efficient online evaluation with asymptotic guarantees. - Neil G. Marchant; Benjamin I. P. Rubinstein (CoRR, 2020)

Discrete Few-Shot Learning for Pan Privacy. - Roei Gelbhart; Benjamin I. P. Rubinstein (CoRR, 2020)

Improving Interpretability of CNN Models Using Non-Negative Concept Activation Vectors. - Ruihan Zhang 002; Prashan Madumal; Tim Miller 001; Krista A. Ehinger; Benjamin I. P. Rubinstein (CoRR, 2020)

A Graph Symmetrisation Bound on Channel Information Leakage under Blowfish Privacy. - Tobias Edwards; Benjamin I. P. Rubinstein; Zuhe Zhang; Sanming Zhou (CoRR, 2020)

Targeted Poisoning Attacks on Black-Box Neural Machine Translation. - Chang Xu; Jun Wang 126; Yuqing Tang; Francisco Guzmán; Benjamin I. P. Rubinstein; Trevor Cohn (CoRR, 2020)

Not fit for Purpose: A critical analysis of the 'Five Safes'. - Chris Culnane; Benjamin I. P. Rubinstein; David Watts (CoRR, 2020)

Invertible Concept-based Explanations for CNN Models with Non-negative Concept Activation Vectors. - Ruihan Zhang 002; Prashan Madumal; Tim Miller 001; Krista A. Ehinger; Benjamin I. P. Rubinstein (AAAI, 2021)

As Easy as 1, 2, 3: Behavioural Testing of NMT Systems for Numerical Translation. - Jun Wang 126; Chang Xu; Francisco Guzmán; Ahmed El-Kishky; Benjamin I. P. Rubinstein; Trevor Cohn (ACL/IJCNLP (Findings), 2021)

Putting words into the system's mouth: A targeted attack on neural machine translation using monolingual data poisoning. - Jun Wang 126; Chang Xu; Francisco Guzmán; Ahmed El-Kishky; Yuqing Tang; Benjamin I. P. Rubinstein; Trevor Cohn (ACL/IJCNLP (Findings), 2021)

A Communication Security Game on Switched Systems for Autonomous Vehicle Platoons. - Guoxin Sun; Tansu Alpcan; Benjamin I. P. Rubinstein; Seyit Camtepe (CDC, 2021)

Mitigating Data Poisoning in Text Classification with Differential Privacy. - Chang Xu; Jun Wang 126; Francisco Guzmán; Benjamin I. P. Rubinstein; Trevor Cohn (EMNLP (Findings), 2021)

DBA bandits: Self-driving index tuning under ad-hoc, analytical workloads with safety guarantees. - R. Malinga Perera; Bastian Oetomo; Benjamin I. P. Rubinstein; Renata Borovica-Gajic (ICDE, 2021)

Closing the BIG-LID: An Effective Local Intrinsic Dimensionality Defense for Nonlinear Regression Poisoning. - Sandamal Weerasinghe; Tamas Abraham; Tansu Alpcan; Sarah M. Erfani; Christopher Leckie; Benjamin I. P. Rubinstein (IJCAI, 2021)

Towards Systematic and Dynamic Task Allocation for Collaborative Parallel Fuzzing. - Van-Thuan Pham; Manh-Dung Nguyen; Quang-Trung Ta; Toby Murray; Benjamin I. P. Rubinstein (ASE, 2021)

Needle in a Haystack: Label-Efficient Evaluation under Extreme Class Imbalance. - Neil G. Marchant; Benjamin I. P. Rubinstein (KDD, 2021)

TRS: Transferability Reduced Ensemble via Promoting Gradient Diversity and Model Smoothness. - Zhuolin Yang; Linyi Li 001; Xiaojun Xu; Shiliang Zuo; Qian Chen 003; Pan Zhou; Benjamin I. P. Rubinstein; Ce Zhang 001; Bo Li 026 (NeurIPS, 2021)

Strategic Mitigation Against Wireless Attacks on Autonomous Platoons. - Guoxin Sun; Tansu Alpcan; Benjamin I. P. Rubinstein; Seyit Camtepe (ECML/PKDD (4), 2021)

A Targeted Attack on Black-Box Neural Machine Translation with Parallel Data Poisoning. - Chang Xu; Jun Wang 126; Yuqing Tang; Francisco Guzmán; Benjamin I. P. Rubinstein; Trevor Cohn (WWW, 2021)

Machine Learning in Network Anomaly Detection: A Survey. - Song Wang 020; Juan Fernando Balarezo; Sithamparanathan Kandeepan; Akram Al-Hourani; Karina Gomez Chavez; Benjamin I. P. Rubinstein (IEEE Access, 2021)

TRS: Transferability Reduced Ensemble via Encouraging Gradient Diversity and Model Smoothness. - Zhuolin Yang; Linyi Li 001; Xiaojun Xu; Shiliang Zuo; Qian Chen 003; Benjamin I. P. Rubinstein; Ce Zhang 001; Bo Li 026 (CoRR, 2021)

Local Intrinsic Dimensionality Signals Adversarial Perturbations. - Sandamal Weerasinghe; Tansu Alpcan; Sarah M. Erfani; Christopher Leckie; Benjamin I. P. Rubinstein (CoRR, 2021)

d-blink: Distributed End-to-End Bayesian Entity Resolution. - Neil G. Marchant; Andee Kaplan; Daniel N. Elazar; Benjamin I. P. Rubinstein; Rebecca C. Steorts (J. Comput. Graph. Stat., 2021)

Hard to Forget: Poisoning Attacks on Certified Machine Unlearning. - Neil G. Marchant; Benjamin I. P. Rubinstein; Scott Alfeld (AAAI, 2022)

Measuring and Mitigating Name Biases in Neural Machine Translation. - Jun Wang 126; Benjamin I. P. Rubinstein; Trevor Cohn (ACL (1), 2022)

Local Intrinsic Dimensionality Signals Adversarial Perturbations. - Sandamal Weerasinghe; Tamas Abraham; Tansu Alpcan; Sarah M. Erfani; Christopher Leckie; Benjamin I. P. Rubinstein (CDC, 2022)

Foiling Training-Time Attacks on Neural Machine Translation Systems. - Jun Wang 126; Xuanli He; Benjamin I. P. Rubinstein; Trevor Cohn (EMNLP (Findings), 2022)

Double Bubble, Toil and Trouble: Enhancing Certified Robustness through Transitivity. - Andrew C. Cullen; Paul Montague; Shijie Liu 004; Sarah M. Erfani; Benjamin I. P. Rubinstein (NeurIPS, 2022)

Unlabelled Sample Compression Schemes for Intersection-Closed Classes and Extremal Classes. - Joachim Hyam Rubinstein; Benjamin I. P. Rubinstein (NeurIPS, 2022)

Securing Cyber-Physical Systems: Physics-Enhanced Adversarial Learning for Autonomous Platoons. - Guoxin Sun; Tansu Alpcan; Benjamin I. P. Rubinstein; Seyit Camtepe (ECML/PKDD (3), 2022)

Are We There Yet? Timing and Floating-Point Attacks on Differential Privacy Systems. - Jiankai Jin; Eleanor McMurtry; Benjamin I. P. Rubinstein; Olga Ohrimenko (SP, 2022)

State Selection Algorithms and Their Impact on The Performance of Stateful Network Protocol Fuzzing. - Dongge Liu; Van-Thuan Pham; Gidon Ernst; Toby Murray; Benjamin I. P. Rubinstein (SANER, 2022)

Testing the Robustness of Learned Index Structures. - Matthias Bachfischer; Renata Borovica-Gajic; Benjamin I. P. Rubinstein (CoRR, 2022)

HMAB: Self-Driving Hierarchy of Bandits for Integrated Physical Database Design Tuning. - R. Malinga Perera; Bastian Oetomo; Benjamin I. P. Rubinstein; Renata Borovica-Gajic (Proc. VLDB Endow., 2022)

A Graph Symmetrization Bound on Channel Information Leakage Under Blowfish Privacy. - Tobias Edwards; Benjamin I. P. Rubinstein; Zuhe Zhang; Sanming Zhou (IEEE Trans. Inf. Theory, 2022)

Enhancing the Antidote: Improved Pointwise Certifications against Poisoning Attacks. - Shijie Liu 004; Andrew C. Cullen; Paul Montague; Sarah M. Erfani; Benjamin I. P. Rubinstein (AAAI, 2023)

An Adversarial Strategic Game for Machine Learning as a Service using System Features. - Guoxin Sun; Tansu Alpcan; Seyit Camtepe; Andrew C. Cullen; Benjamin I. P. Rubinstein (AAMAS, 2023)

Mitigating Backdoor Poisoning Attacks through the Lens of Spurious Correlation. - Xuanli He; Qiongkai Xu; Jun Wang 126; Benjamin I. P. Rubinstein; Trevor Cohn (EMNLP, 2023)

Beyond the Coverage Plateau: A Comprehensive Study of Fuzz Blockers (Registered Report). - Wentao Gao; Van-Thuan Pham; Dongge Liu; Oliver Chang; Toby Murray; Benjamin I. P. Rubinstein (FUZZING, 2023)

Efficient Index Learning via Model Reuse and Fine-tuning. - Guanli Liu; Jianzhong Qi 001; Lars Kulik; Kazuya Soga; Renata Borovica-Gajic; Benjamin I. P. Rubinstein (ICDEW, 2023)

RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized Deletion. - Zhuoqun Huang; Neil G. Marchant; Keane Lucas; Lujo Bauer; Olga Ohrimenko; Benjamin I. P. Rubinstein (NeurIPS, 2023)

Bayesian Graphical Entity Resolution Using Exchangeable Random Partition Priors. - Neil G. Marchant; Benjamin I. P. Rubinstein; Rebecca C. Steorts (CoRR, 2023)

Certified Robustness of Learning-based Static Malware Detectors. - Zhuoqun Huang; Neil G. Marchant; Keane Lucas; Lujo Bauer; Olga Ohrimenko; Benjamin I. P. Rubinstein (CoRR, 2023)

Exploiting Certified Defences to Attack Randomised Smoothing. - Andrew C. Cullen; Paul Montague; Shijie Liu 004; Sarah M. Erfani; Benjamin I. P. Rubinstein (CoRR, 2023)

IMBERT: Making BERT Immune to Insertion-based Backdoor Attacks. - Xuanli He; Jun Wang 126; Benjamin I. P. Rubinstein; Trevor Cohn (CoRR, 2023)

Cutting to the chase with warm-start contextual bandits. - Bastian Oetomo; R. Malinga Perera; Renata Borovica-Gajic; Benjamin I. P. Rubinstein (Knowl. Inf. Syst., 2023)

Predicting dynamic spectrum allocation: a review covering simulation, modelling, and prediction. - Andrew C. Cullen; Benjamin I. P. Rubinstein; Sithamparanathan Kandeepan; Barry Flower; Philip H. W. Leong (Artif. Intell. Rev., 2023)

No DBA? No Regret! Multi-Armed Bandits for Index Tuning of Analytical and HTAP Workloads With Provable Guarantees. - R. Malinga Perera; Bastian Oetomo; Benjamin I. P. Rubinstein; Renata Borovica-Gajic (IEEE Trans. Knowl. Data Eng., 2023)

Getting a-Round Guarantees: Floating-Point Attacks on Certified Robustness. - Jiankai Jin; Olga Ohrimenko; Benjamin I. P. Rubinstein (AISec@CCS, 2024)

Designing an Adaptive AI System for Operation on Board the SpIRIT Nano-Satellite. - Zaher Joukhadar; Jonathan Morgan; Christopher Bayliss; Miguel Ortiz del Castillo; Jack McRobbie; Robert Mearns; Krista A. Ehinger; Benjamin I. P. Rubinstein; Richard O. Sinnott; Michele Trenti; James Bailey 001 (AI (1), 2024)

Elephants Do Not Forget: Differential Privacy with State Continuity for Privacy Budget. - Jiankai Jin; Chitchanok Chuengsatiansup; Toby Murray; Benjamin I. P. Rubinstein; Yuval Yarom; Olga Ohrimenko (CCS, 2024)

Mitigating Challenges of the Space Environment for Onboard Artificial Intelligence: Design Overview of the Imaging Payload on SpIRIT. - Miguel Ortiz del Castillo; Jonathan Morgan; Jack McRobbie; Clint Therakam; Zaher Joukhadar; Robert Mearns; Simon Barraclough; Richard O. Sinnott; Andrew Woods; Chris Bayliss; Kris Ehinger; Benjamin I. P. Rubinstein; et al. (CVPR Workshops, 2024)

CERT-ED: Certifiably Robust Text Classification for Edit Distance. - Zhuoqun Huang; Neil G. Marchant; Olga Ohrimenko; Benjamin I. P. Rubinstein (EMNLP (Findings), 2024)

Warm-Starting Contextual Bandits Under Latent Reward Scaling. - Bastian Oetomo; R. Malinga Perera; Renata Borovica-Gajic; Benjamin I. P. Rubinstein (ICDM, 2024)

Et Tu Certifications: Robustness Certificates Yield Better Adversarial Examples. - Andrew C. Cullen; Shijie Liu 004; Paul Montague; Sarah Monazam Erfani; Benjamin I. P. Rubinstein (ICML, 2024)

Backdoor Attacks on Multilingual Machine Translation. - Jun Wang 126; Qiongkai Xu; Xuanli He; Benjamin I. P. Rubinstein; Trevor Cohn (NAACL-HLT, 2024)

Certified Adversarial Robustness via Randomized α-Smoothing for Regression Models. - Aref Miri Rekavandi; Farhad Farokhi; Olga Ohrimenko; Benjamin I. P. Rubinstein (NeurIPS, 2024)

It's Simplex! Disaggregating Measures to Improve Certified Robustness. - Andrew C. Cullen; Paul Montague; Shijie Liu 004; Sarah M. Erfani; Benjamin I. P. Rubinstein (SP, 2024)

Backdoor Attack on Multilingual Machine Translation. - Jun Wang 126; Qiongkai Xu; Xuanli He; Benjamin I. P. Rubinstein; Trevor Cohn (CoRR, 2024)

Transferring Troubles: Cross-Lingual Transferability of Backdoor Attacks in LLMs with Instruction Tuning. - Xuanli He; Jun Wang; Qiongkai Xu; Pasquale Minervini; Pontus Stenetorp; Benjamin I. P. Rubinstein; Trevor Cohn (CoRR, 2024)

Adaptive Data Analysis for Growing Data. - Neil G. Marchant; Benjamin I. P. Rubinstein (CoRR, 2024)

SEEP: Training Dynamics Grounds Latent Representation Search for Mitigating Backdoor Poisoning Attacks. - Xuanli He; Qiongkai Xu; Jun Wang 126; Benjamin I. P. Rubinstein; Trevor Cohn (Trans. Assoc. Comput. Linguistics, 2024)

To Act or Not to Act: An Adversarial Game for Securing Vehicle Platoons. - Guoxin Sun; Tansu Alpcan; Benjamin I. P. Rubinstein; Seyit Camtepe (IEEE Trans. Inf. Forensics Secur., 2024)

TUBA: Cross-Lingual Transferability of Backdoor Attacks in LLMs with Instruction Tuning. - Xuanli He; Jun Wang; Qiongkai Xu; Pasquale Minervini; Pontus Stenetorp; Benjamin I. P. Rubinstein; Trevor Cohn (ACL (Findings), 2025)

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning. - Shijie Liu 004; Andrew Craig Cullen; Paul Montague; Sarah Monazam Erfani; Benjamin I. P. Rubinstein (ICLR, 2025)

RS-Reg: Probabilistic and Robust Certified Regression through Randomized Smoothing. - Aref Miri Rekavandi; Olga Ohrimenko; Benjamin I. P. Rubinstein (Trans. Mach. Learn. Res., 2025)

DDAD: A Two-pronged Adversarial Defense Based on Distributional Discrepancy. - Jiacheng Zhang; Benjamin I. P. Rubinstein; Jingfeng Zhang; Feng Liu (CoRR, 2025)

Fox in the Henhouse: Supply-Chain Backdoor Attacks Against Reinforcement Learning. - Shijie Liu 004; Andrew C. Cullen; Paul Montague; Sarah M. Erfani; Benjamin I. P. Rubinstein (CoRR, 2025)

Position: Certified Robustness Does Not (Yet) Imply Model Security. - Andrew C. Cullen; Paul Montague; Sarah M. Erfani; Benjamin I. P. Rubinstein (CoRR, 2025)