Rubinstein, Benjamin I. P.

30 publications

TMLR 2026 Semantic-Aware Adversarial Fine-Tuning for CLIP Jiacheng Zhang, Jinhao Li, Hanxun Huang, Sarah Monazam Erfani, Benjamin I. P. Rubinstein, Feng Liu

NeurIPS 2025 AdaptDel: Adaptable Deletion Rate Randomized Smoothing for Certified Robustness Zhuoqun Huang, Neil G Marchant, Olga Ohrimenko, Benjamin I. P. Rubinstein

NeurIPS 2025 Adaptive Data Analysis for Growing Data Neil G Marchant, Benjamin I. P. Rubinstein

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

ICML 2025 One Stone, Two Birds: Enhancing Adversarial Defense Through the Lens of Distributional Discrepancy Jiacheng Zhang, Benjamin I. P. Rubinstein, Jingfeng Zhang, Feng Liu

ICML 2025 Position: Certified Robustness Does Not (Yet) Imply Model Security Andrew Craig Cullen, Paul Montague, Sarah Monazam Erfani, Benjamin I. P. Rubinstein

TMLR 2025 RS-Reg: Probabilistic and Robust Certified Regression Through Randomized Smoothing Aref Miri Rekavandi, Olga Ohrimenko, Benjamin I. P. Rubinstein

NeurIPS 2024 Certified Adversarial Robustness via Randomized $\alpha$-Smoothing for Regression Models Aref Miri Rekavandi, Farhad Farokhi, Olga Ohrimenko, Benjamin I.P. Rubinstein

ICML 2024 Et Tu Certifications: Robustness Certificates Yield Better Adversarial Examples Andrew Craig Cullen, Shijie Liu, Paul Montague, Sarah Monazam Erfani, Benjamin I. P. Rubinstein

CVPRW 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, James Bailey, Airlie Chapman, Michele Trenti

AAAI 2023 Enhancing the Antidote: Improved Pointwise Certifications Against Poisoning Attacks Shijie Liu, Andrew C. Cullen, Paul Montague, Sarah M. Erfani, Benjamin I. P. Rubinstein

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

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

IJCAI 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

AAAI 2021 Invertible Concept-Based Explanations for CNN Models with Non-Negative Concept Activation Vectors Ruihan Zhang, Prashan Madumal, Tim Miller, Krista A. Ehinger, Benjamin I. P. Rubinstein

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

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

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

JMLR 2017 Differential Privacy for Bayesian Inference Through Posterior Sampling Christos Dimitrakakis, Blaine Nelson, Zuhe Zhang, Aikaterini Mitrokotsa, Benjamin I. P. Rubinstein

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

AAAI 2017 The Bernstein Mechanism: Function Release Under Differential Privacy Francesco Aldà, Benjamin I. P. Rubinstein

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

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

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

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

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

IJCAI 2013 On the Challenges of Balancing Privacy and Utility of Open Health Data Christian Guttmann, Xingzhi Sun, Chaitanya Rao, Carlos Queiroz, Benjamin I. P. Rubinstein

JMLR 2012 A Geometric Approach to Sample Compression Benjamin I.P. Rubinstein, J. Hyam Rubinstein

JMLR 2012 Query Strategies for Evading Convex-Inducing Classifiers Blaine Nelson, Benjamin I. P. Rubinstein, Ling Huang, Anthony D. Joseph, Steven J. Lee, Satish Rao, J. D. Tygar

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