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ORCIDBenjamin I. P. Rubinstein
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- affiliation: University of Melbourne, School of Computing and Information Systems, Australia
- affiliation (former): Microsoft Research
- affiliation (PhD 2010): University of California Berkeley, CA, USA
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2020 – today
- 2024
[j26]Xuanli He, Qiongkai Xu, Jun Wang, Benjamin I. P. Rubinstein, Trevor Cohn:
SEEP: Training Dynamics Grounds Latent Representation Search for Mitigating Backdoor Poisoning Attacks. Trans. Assoc. Comput. Linguistics 12: 996-1010 (2024)- [j25]Guoxin Sun
, Tansu Alpcan, Benjamin I. P. Rubinstein, Seyit Camtepe:
To Act or Not to Act: An Adversarial Game for Securing Vehicle Platoons. IEEE Trans. Inf. Forensics Secur. 19: 163-177 (2024) - [c80]Jiankai Jin, Olga Ohrimenko, Benjamin I. P. Rubinstein:
Getting a-Round Guarantees: Floating-Point Attacks on Certified Robustness. AISec@CCS 2024: 53-64 - [c79]Jiankai Jin, Chitchanok Chuengsatiansup, Toby Murray, Benjamin I. P. Rubinstein, Yuval Yarom, Olga Ohrimenko:
Elephants Do Not Forget: Differential Privacy with State Continuity for Privacy Budget. CCS 2024: 1909-1923 - [c78]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:
Mitigating Challenges of the Space Environment for Onboard Artificial Intelligence: Design Overview of the Imaging Payload on SpIRIT. CVPR Workshops 2024: 6789-6798 - [c77]Zhuoqun Huang, Neil G. Marchant, Olga Ohrimenko, Benjamin I. P. Rubinstein:
CERT-ED: Certifiably Robust Text Classification for Edit Distance. EMNLP (Findings) 2024: 10813-10835 - [c76]Andrew C. Cullen, Shijie Liu, Paul Montague, Sarah Monazam Erfani, Benjamin I. P. Rubinstein:
Et Tu Certifications: Robustness Certificates Yield Better Adversarial Examples. ICML 2024 - [c75]Jun Wang, Qiongkai Xu, Xuanli He, Benjamin I. P. Rubinstein, Trevor Cohn:
Backdoor Attacks on Multilingual Machine Translation. NAACL-HLT 2024: 4515-4534 - [c74]Andrew C. Cullen, Paul Montague, Shijie Liu, Sarah M. Erfani, Benjamin I. P. Rubinstein:
It's Simplex! Disaggregating Measures to Improve Certified Robustness. SP 2024: 2886-2900 - [i73]Jiankai Jin, Chitchanok Chuengsatiansup, Toby Murray, Benjamin I. P. Rubinstein, Yuval Yarom, Olga Ohrimenko:
Elephants Do Not Forget: Differential Privacy with State Continuity for Privacy Budget. CoRR abs/2401.17628 (2024) - [i72]Jun Wang, Qiongkai Xu, Xuanli He, Benjamin I. P. Rubinstein, Trevor Cohn:
Backdoor Attack on Multilingual Machine Translation. CoRR abs/2404.02393 (2024) - [i71]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:
Mitigating Challenges of the Space Environment for Onboard Artificial Intelligence: Design Overview of the Imaging Payload on SpIRIT. CoRR abs/2404.08399 (2024) - [i70]Xuanli He, Jun Wang, Qiongkai Xu, Pasquale Minervini, Pontus Stenetorp, Benjamin I. P. Rubinstein, Trevor Cohn:
Transferring Troubles: Cross-Lingual Transferability of Backdoor Attacks in LLMs with Instruction Tuning. CoRR abs/2404.19597 (2024) - [i69]Aref Miri Rekavandi, Olga Ohrimenko, Benjamin I. P. Rubinstein:
RS-Reg: Probabilistic and Robust Certified Regression Through Randomized Smoothing. CoRR abs/2405.08892 (2024) - [i68]Xuanli He, Qiongkai Xu, Jun Wang, Benjamin I. P. Rubinstein, Trevor Cohn:
SEEP: Training Dynamics Grounds Latent Representation Search for Mitigating Backdoor Poisoning Attacks. CoRR abs/2405.11575 (2024) - [i67]Neil G. Marchant, Benjamin I. P. Rubinstein:
Adaptive Data Analysis for Growing Data. CoRR abs/2405.13375 (2024) - [i66]Zhuoqun Huang, Neil G. Marchant, Olga Ohrimenko, Benjamin I. P. Rubinstein:
CERT-ED: Certifiably Robust Text Classification for Edit Distance. CoRR abs/2408.00728 (2024) - 2023
- [j24]Andrew C. Cullen, Benjamin I. P. Rubinstein, Sithamparanathan Kandeepan, Barry Flower, Philip H. W. Leong:
Predicting dynamic spectrum allocation: a review covering simulation, modelling, and prediction. Artif. Intell. Rev. 56(10): 10921-10959 (2023) - [j23]Bastian Oetomo, R. Malinga Perera, Renata Borovica-Gajic, Benjamin I. P. Rubinstein:
Cutting to the chase with warm-start contextual bandits. Knowl. Inf. Syst. 65(9): 3533-3565 (2023) - [j22]R. Malinga Perera, Bastian Oetomo, Benjamin I. P. Rubinstein, Renata Borovica-Gajic:
No DBA? No Regret! Multi-Armed Bandits for Index Tuning of Analytical and HTAP Workloads With Provable Guarantees. IEEE Trans. Knowl. Data Eng. 35(12): 12855-12872 (2023) - [c73]Shijie Liu, Andrew C. Cullen, Paul Montague, Sarah M. Erfani, Benjamin I. P. Rubinstein:
Enhancing the Antidote: Improved Pointwise Certifications against Poisoning Attacks. AAAI 2023: 8861-8869 - [c72]Guoxin Sun, Tansu Alpcan, Seyit Camtepe, Andrew C. Cullen, Benjamin I. P. Rubinstein:
An Adversarial Strategic Game for Machine Learning as a Service using System Features. AAMAS 2023: 2508-2510 - [c71]Xuanli He, Qiongkai Xu, Jun Wang, Benjamin I. P. Rubinstein, Trevor Cohn:
Mitigating Backdoor Poisoning Attacks through the Lens of Spurious Correlation. EMNLP 2023: 953-967 - [c70]Wentao Gao, Van-Thuan Pham, Dongge Liu, Oliver Chang, Toby Murray, Benjamin I. P. Rubinstein:
Beyond the Coverage Plateau: A Comprehensive Study of Fuzz Blockers (Registered Report). FUZZING 2023: 47-55 - [c69]Guanli Liu, Jianzhong Qi, Lars Kulik, Kazuya Soga, Renata Borovica-Gajic, Benjamin I. P. Rubinstein:
Efficient Index Learning via Model Reuse and Fine-tuning. ICDEW 2023: 60-66 - [c68]Zhuoqun Huang, Neil G. Marchant, Keane Lucas, Lujo Bauer, Olga Ohrimenko, Benjamin I. P. Rubinstein:
RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized Deletion. NeurIPS 2023 - [i65]Neil G. Marchant, Benjamin I. P. Rubinstein, Rebecca C. Steorts:
Bayesian Graphical Entity Resolution Using Exchangeable Random Partition Priors. CoRR abs/2301.02962 (2023) - [i64]Zhuoqun Huang, Neil G. Marchant, Keane Lucas, Lujo Bauer, Olga Ohrimenko, Benjamin I. P. Rubinstein:
Certified Robustness of Learning-based Static Malware Detectors. CoRR abs/2302.01757 (2023) - [i63]Andrew C. Cullen, Paul Montague, Shijie Liu, Sarah M. Erfani, Benjamin I. P. Rubinstein:
Exploiting Certified Defences to Attack Randomised Smoothing. CoRR abs/2302.04379 (2023) - [i62]Xuanli He, Qiongkai Xu, Jun Wang, Benjamin I. P. Rubinstein, Trevor Cohn:
Mitigating Backdoor Poisoning Attacks through the Lens of Spurious Correlation. CoRR abs/2305.11596 (2023) - [i61]Xuanli He, Jun Wang, Benjamin I. P. Rubinstein, Trevor Cohn:
IMBERT: Making BERT Immune to Insertion-based Backdoor Attacks. CoRR abs/2305.16503 (2023) - [i60]Shijie Liu, Andrew C. Cullen, Paul Montague, Sarah M. Erfani, Benjamin I. P. Rubinstein:
Enhancing the Antidote: Improved Pointwise Certifications against Poisoning Attacks. CoRR abs/2308.07553 (2023) - [i59]Andrew C. Cullen, Paul Montague, Shijie Liu, Sarah M. Erfani, Benjamin I. P. Rubinstein:
It's Simplex! Disaggregating Measures to Improve Certified Robustness. CoRR abs/2309.11005 (2023) - 2022
- [j21]R. Malinga Perera, Bastian Oetomo, Benjamin I. P. Rubinstein, Renata Borovica-Gajic:
HMAB: Self-Driving Hierarchy of Bandits for Integrated Physical Database Design Tuning. Proc. VLDB Endow. 16(2): 216-229 (2022) - [j20]Tobias Edwards, Benjamin I. P. Rubinstein, Zuhe Zhang, Sanming Zhou:
A Graph Symmetrization Bound on Channel Information Leakage Under Blowfish Privacy. IEEE Trans. Inf. Theory 68(1): 538-548 (2022) - [c67]Neil G. Marchant, Benjamin I. P. Rubinstein, Scott Alfeld:
Hard to Forget: Poisoning Attacks on Certified Machine Unlearning. AAAI 2022: 7691-7700 - [c66]Jun Wang, Benjamin I. P. Rubinstein, Trevor Cohn:
Measuring and Mitigating Name Biases in Neural Machine Translation. ACL (1) 2022: 2576-2590 - [c65]Sandamal Weerasinghe, Tamas Abraham, Tansu Alpcan, Sarah M. Erfani, Christopher Leckie, Benjamin I. P. Rubinstein:
Local Intrinsic Dimensionality Signals Adversarial Perturbations. CDC 2022: 6118-6125 - [c64]Jun Wang, Xuanli He, Benjamin I. P. Rubinstein, Trevor Cohn:
Foiling Training-Time Attacks on Neural Machine Translation Systems. EMNLP (Findings) 2022: 5906-5913 - [c63]Andrew C. Cullen, Paul Montague, Shijie Liu, Sarah M. Erfani, Benjamin I. P. Rubinstein:
Double Bubble, Toil and Trouble: Enhancing Certified Robustness through Transitivity. NeurIPS 2022 - [c62]Joachim Hyam Rubinstein, Benjamin I. P. Rubinstein:
Unlabelled Sample Compression Schemes for Intersection-Closed Classes and Extremal Classes. NeurIPS 2022 - [c61]Guoxin Sun, Tansu Alpcan, Benjamin I. P. Rubinstein, Seyit Camtepe:
Securing Cyber-Physical Systems: Physics-Enhanced Adversarial Learning for Autonomous Platoons. ECML/PKDD (3) 2022: 269-285 - [c60]Jiankai Jin, Eleanor McMurtry, Benjamin I. P. Rubinstein, Olga Ohrimenko:
Are We There Yet? Timing and Floating-Point Attacks on Differential Privacy Systems. SP 2022: 473-488 - [c59]Dongge Liu, Van-Thuan Pham, Gidon Ernst, Toby Murray, Benjamin I. P. Rubinstein:
State Selection Algorithms and Their Impact on The Performance of Stateful Network Protocol Fuzzing. SANER 2022: 720-730 - [i58]Jiankai Jin, Olga Ohrimenko, Benjamin I. P. Rubinstein:
Getting a-Round Guarantees: Floating-Point Attacks on Certified Robustness. CoRR abs/2205.10159 (2022) - [i57]Matthias Bachfischer, Renata Borovica-Gajic, Benjamin I. P. Rubinstein:
Testing the Robustness of Learned Index Structures. CoRR abs/2207.11575 (2022) - [i56]J. Hyam Rubinstein, Benjamin I. P. Rubinstein:
Unlabelled Sample Compression Schemes for Intersection-Closed Classes and Extremal Classes. CoRR abs/2210.05455 (2022) - [i55]Andrew C. Cullen, Paul Montague, Shijie Liu, Sarah M. Erfani, Benjamin I. P. Rubinstein:
Double Bubble, Toil and Trouble: Enhancing Certified Robustness through Transitivity. CoRR abs/2210.06077 (2022) - 2021
- [j19]Song Wang, Juan Fernando Balarezo, Sithamparanathan Kandeepan, Akram Al-Hourani, Karina Gomez Chavez, Benjamin I. P. Rubinstein:
Machine Learning in Network Anomaly Detection: A Survey. IEEE Access 9: 152379-152396 (2021) - [j18]Neil G. Marchant, Andee Kaplan, Daniel N. Elazar, Benjamin I. P. Rubinstein, Rebecca C. Steorts:
d-blink: Distributed End-to-End Bayesian Entity Resolution. J. Comput. Graph. Stat. 30(2): 406-421 (2021) - [c58]Ruihan Zhang, Prashan Madumal, Tim Miller, Krista A. Ehinger, Benjamin I. P. Rubinstein:
Invertible Concept-based Explanations for CNN Models with Non-negative Concept Activation Vectors. AAAI 2021: 11682-11690 - [c57]Jun Wang, Chang Xu, Francisco Guzmán, Ahmed El-Kishky, Yuqing Tang, Benjamin I. P. Rubinstein, Trevor Cohn:
Putting words into the system's mouth: A targeted attack on neural machine translation using monolingual data poisoning. ACL/IJCNLP (Findings) 2021: 1463-1473 - [c56]Jun Wang, Chang Xu, Francisco Guzmán, Ahmed El-Kishky, Benjamin I. P. Rubinstein, Trevor Cohn:
As Easy as 1, 2, 3: Behavioural Testing of NMT Systems for Numerical Translation. ACL/IJCNLP (Findings) 2021: 4711-4717 - [c55]Guoxin Sun, Tansu Alpcan, Benjamin I. P. Rubinstein, Seyit Camtepe:
A Communication Security Game on Switched Systems for Autonomous Vehicle Platoons. CDC 2021: 2690-2695 - [c54]Chang Xu, Jun Wang, Francisco Guzmán, Benjamin I. P. Rubinstein, Trevor Cohn:
Mitigating Data Poisoning in Text Classification with Differential Privacy. EMNLP (Findings) 2021: 4348-4356 - [c53]R. Malinga Perera, Bastian Oetomo, Benjamin I. P. Rubinstein, Renata Borovica-Gajic:
DBA bandits: Self-driving index tuning under ad-hoc, analytical workloads with safety guarantees. ICDE 2021: 600-611 - [c52]Bastian Oetomo, R. Malinga Perera, Renata Borovica-Gajic, Benjamin I. P. Rubinstein:
Cutting to the Chase with Warm-Start Contextual Bandits. ICDM 2021: 459-468 - [c51]Sandamal Weerasinghe, Tamas Abraham, Tansu Alpcan, Sarah M. Erfani, Christopher Leckie, Benjamin I. P. Rubinstein:
Closing the BIG-LID: An Effective Local Intrinsic Dimensionality Defense for Nonlinear Regression Poisoning. IJCAI 2021: 3176-3184 - [c50]Van-Thuan Pham, Manh-Dung Nguyen, Quang-Trung Ta, Toby Murray, Benjamin I. P. Rubinstein:
Towards Systematic and Dynamic Task Allocation for Collaborative Parallel Fuzzing. ASE 2021: 1337-1341 - [c49]Neil G. Marchant, Benjamin I. P. Rubinstein:
Needle in a Haystack: Label-Efficient Evaluation under Extreme Class Imbalance. KDD 2021: 1180-1190 - [c48]Zhuolin Yang, Linyi Li, Xiaojun Xu, Shiliang Zuo, Qian Chen, Pan Zhou, Benjamin I. P. Rubinstein, Ce Zhang, Bo Li:
TRS: Transferability Reduced Ensemble via Promoting Gradient Diversity and Model Smoothness. NeurIPS 2021: 17642-17655 - [c47]Guoxin Sun, Tansu Alpcan, Benjamin I. P. Rubinstein, Seyit Camtepe:
Strategic Mitigation Against Wireless Attacks on Autonomous Platoons. ECML/PKDD (4) 2021: 69-84 - [c46]Chang Xu, Jun Wang, Yuqing Tang, Francisco Guzmán, Benjamin I. P. Rubinstein, Trevor Cohn:
A Targeted Attack on Black-Box Neural Machine Translation with Parallel Data Poisoning. WWW 2021: 3638-3650 - [i54]Zhuolin Yang, Linyi Li, Xiaojun Xu, Shiliang Zuo, Qian Chen, Benjamin I. P. Rubinstein, Ce Zhang, Bo Li:
TRS: Transferability Reduced Ensemble via Encouraging Gradient Diversity and Model Smoothness. CoRR abs/2104.00671 (2021) - [i53]Jun Wang, Chang Xu, Francisco Guzmán, Ahmed El-Kishky, Yuqing Tang, Benjamin I. P. Rubinstein, Trevor Cohn:
Putting words into the system's mouth: A targeted attack on neural machine translation using monolingual data poisoning. CoRR abs/2107.05243 (2021) - [i52]Jun Wang, Chang Xu, Francisco Guzmán, Ahmed El-Kishky, Benjamin I. P. Rubinstein, Trevor Cohn:
As Easy as 1, 2, 3: Behavioural Testing of NMT Systems for Numerical Translation. CoRR abs/2107.08357 (2021) - [i51]R. Malinga Perera, Bastian Oetomo, Benjamin I. P. Rubinstein, Renata Borovica-Gajic:
No DBA? No regret! Multi-armed bandits for index tuning of analytical and HTAP workloads with provable guarantees. CoRR abs/2108.10130 (2021) - [i50]Neil G. Marchant, Benjamin I. P. Rubinstein, Scott Alfeld:
Hard to Forget: Poisoning Attacks on Certified Machine Unlearning. CoRR abs/2109.08266 (2021) - [i49]Sandamal Weerasinghe, Tansu Alpcan, Sarah M. Erfani, Christopher Leckie, Benjamin I. P. Rubinstein:
Local Intrinsic Dimensionality Signals Adversarial Perturbations. CoRR abs/2109.11803 (2021) - [i48]Guoxin Sun, Tansu Alpcan, Benjamin I. P. Rubinstein, Seyit Camtepe:
A Communication Security Game on Switched Systems for Autonomous Vehicle Platoons. CoRR abs/2109.14208 (2021) - [i47]Jiankai Jin, Eleanor McMurtry, Benjamin I. P. Rubinstein, Olga Ohrimenko:
Are We There Yet? Timing and Floating-Point Attacks on Differential Privacy Systems. CoRR abs/2112.05307 (2021) - [i46]Dongge Liu, Van-Thuan Pham, Gidon Ernst, Toby Murray, Benjamin I. P. Rubinstein:
State Selection Algorithms and Their Impact on The Performance of Stateful Network Protocol Fuzzing. CoRR abs/2112.15498 (2021) - 2020
- [c45]Naufal Fikri Setiawan, Benjamin I. P. Rubinstein, Renata Borovica-Gajic:
Function Interpolation for Learned Index Structures. ADC 2020: 68-80 - [c44]Benjamin Fish, Lev Reyzin, Benjamin I. P. Rubinstein:
Sampling Without Compromising Accuracy in Adaptive Data Analysis. ALT 2020: 297-318 - [c43]Dongge Liu, Gidon Ernst, Toby Murray, Benjamin I. P. Rubinstein:
Legion: Best-First Concolic Testing (Competition Contribution). FASE 2020: 545-549 - [c42]Yi Han, David Hubczenko, Paul Montague, Olivier Y. de Vel, Tamas Abraham, Benjamin I. P. Rubinstein, Christopher Leckie, Tansu Alpcan, Sarah M. Erfani:
Adversarial Reinforcement Learning under Partial Observability in Autonomous Computer Network Defence. IJCNN 2020: 1-8 - [c41]Dongge Liu, Gidon Ernst, Toby Murray, Benjamin I. P. Rubinstein:
LEGION: Best-First Concolic Testing. ASE 2020: 54-65 - [c40]Leyla Roohi, Benjamin I. P. Rubinstein, Vanessa Teague:
Assessing Centrality Without Knowing Connections. PAKDD (2) 2020: 152-163 - [i45]Dongge Liu, Gidon Ernst, Toby Murray, Benjamin I. P. Rubinstein:
Legion: Best-First Concolic Testing. CoRR abs/2002.06311 (2020) - [i44]Leyla Roohi, Benjamin I. P. Rubinstein, Vanessa Teague:
Assessing Centrality Without Knowing Connections. CoRR abs/2005.13787 (2020) - [i43]Neil G. Marchant, Benjamin I. P. Rubinstein:
A general framework for label-efficient online evaluation with asymptotic guarantees. CoRR abs/2006.06963 (2020) - [i42]Roei Gelbhart, Benjamin I. P. Rubinstein:
Discrete Few-Shot Learning for Pan Privacy. CoRR abs/2006.13120 (2020) - [i41]Ruihan Zhang, Prashan Madumal, Tim Miller, Krista A. Ehinger, Benjamin I. P. Rubinstein:
Improving Interpretability of CNN Models Using Non-Negative Concept Activation Vectors. CoRR abs/2006.15417 (2020) - [i40]Tobias Edwards, Benjamin I. P. Rubinstein, Zuhe Zhang, Sanming Zhou:
A Graph Symmetrisation Bound on Channel Information Leakage under Blowfish Privacy. CoRR abs/2007.05975 (2020) - [i39]Malinga Perera, Bastian Oetomo, Benjamin I. P. Rubinstein, Renata Borovica-Gajic:
DBA bandits: Self-driving index tuning under ad-hoc, analytical workloads with safety guarantees. CoRR abs/2010.09208 (2020) - [i38]Chang Xu, Jun Wang, Yuqing Tang, Francisco Guzmán, Benjamin I. P. Rubinstein, Trevor Cohn:
Targeted Poisoning Attacks on Black-Box Neural Machine Translation. CoRR abs/2011.00675 (2020) - [i37]Chris Culnane, Benjamin I. P. Rubinstein, David Watts:
Not fit for Purpose: A critical analysis of the 'Five Safes'. CoRR abs/2011.02142 (2020)
2010 – 2019
- 2019
- [c39]Scott Alfeld, Ara Vartanian, Lucas Newman-Johnson, Benjamin I. P. Rubinstein:
Attacking Data Transforming Learners at Training Time. AAAI 2019: 3167-3174 - [c38]Yuan Li, Benjamin I. P. Rubinstein, Trevor Cohn:
Exploiting Worker Correlation for Label Aggregation in Crowdsourcing. ICML 2019: 3886-3895 - [c37]Leyla Roohi, Benjamin I. P. Rubinstein, Vanessa Teague:
Differentially-Private Two-Party Egocentric Betweenness Centrality. INFOCOM 2019: 2233-2241 - [c36]Yuan Li, Benjamin I. P. Rubinstein, Trevor Cohn:
Truth Inference at Scale: A Bayesian Model for Adjudicating Highly Redundant Crowd Annotations. WWW 2019: 1028-1038 - [i36]Leyla Roohi, Benjamin I. P. Rubinstein, Vanessa Teague:
Differentially-Private Two-Party Egocentric Betweenness Centrality. CoRR abs/1901.05562 (2019) - [i35]Bastian Oetomo, Malinga Perera, Renata Borovica-Gajic, Benjamin I. P. Rubinstein:
A Note on Bounding Regret of the C$^2$UCB Contextual Combinatorial Bandit. CoRR abs/1902.07500 (2019) - [i34]Yuan Li, Benjamin I. P. Rubinstein, Trevor Cohn:
Truth Inference at Scale: A Bayesian Model for Adjudicating Highly Redundant Crowd Annotations. CoRR abs/1902.08918 (2019) - [i33]Yi Han, David Hubczenko, Paul Montague, Olivier Y. de Vel, Tamas Abraham, Benjamin I. P. Rubinstein, Christopher Leckie, Tansu Alpcan, Sarah M. Erfani:
Adversarial Reinforcement Learning under Partial Observability in Software-Defined Networking. CoRR abs/1902.09062 (2019) - [i32]Chris Culnane, Benjamin I. P. Rubinstein, Vanessa Teague:
Stop the Open Data Bus, We Want to Get Off. CoRR abs/1908.05004 (2019) - [i31]Neil G. Marchant, Rebecca C. Steorts, Andee Kaplan, Benjamin I. P. Rubinstein, Daniel N. Elazar:
d-blink: Distributed End-to-End Bayesian Entity Resolution. CoRR abs/1909.06039 (2019) - 2018
- [j17]Maryam Fanaeepour, Benjamin I. P. Rubinstein:
Differentially private counting of users' spatial regions. Knowl. Inf. Syst. 54(1): 5-32 (2018) - [j16]Lingjuan Lyu, Karthik Nandakumar, Benjamin I. P. Rubinstein, Jiong Jin, Justin Bedo, Marimuthu Palaniswami:
PPFA: Privacy Preserving Fog-Enabled Aggregation in Smart Grid. IEEE Trans. Ind. Informatics 14(8): 3733-3744 (2018) - [c35]Yi Han, Benjamin I. P. Rubinstein:
Adequacy of the Gradient-Descent Method for Classifier Evasion Attacks. AAAI Workshops 2018: 237-244 - [c34]Yi Han, Benjamin I. P. Rubinstein, Tamas Abraham, Tansu Alpcan, Olivier Y. de Vel, Sarah M. Erfani, David Hubczenko, Christopher Leckie, Paul Montague:
Reinforcement Learning for Autonomous Defence in Software-Defined Networking. GameSec 2018: 145-165 - [c33]Maryam Fanaeepour, Benjamin I. P. Rubinstein:
Histogramming Privately Ever After: Differentially-Private Data-Dependent Error Bound Optimisation. ICDE 2018: 1204-1207 - [c32]Benjamin Fish, Lev Reyzin, Benjamin I. P. Rubinstein:
Sublinear-Time Adaptive Data Analysis. ISAIM 2018 - [c31]Zay Maung Maung Aye, Benjamin I. P. Rubinstein, Kotagiri Ramamohanarao:
Fast Manifold Landmarking Using Locality-Sensitive Hashing. PAKDD (3) 2018: 452-464 - [i30]Chris Culnane, Benjamin I. P. Rubinstein, Vanessa Teague:
Options for encoding names for data linking at the Australian Bureau of Statistics. CoRR abs/1802.07975 (2018) - [i29]Yi Han, Benjamin I. P. Rubinstein, Tamas Abraham, Tansu Alpcan, Olivier Y. de Vel, Sarah M. Erfani, David Hubczenko, Christopher Leckie, Paul Montague:
Reinforcement Learning for Autonomous Defence in Software-Defined Networking. CoRR abs/1808.05770 (2018) - 2017
- [j15]Christos Dimitrakakis, Blaine Nelson, Zuhe Zhang, Aikaterini Mitrokotsa, Benjamin I. P. Rubinstein:
Differential Privacy for Bayesian Inference through Posterior Sampling. J. Mach. Learn. Res. 18: 11:1-11:39 (2017) - [j14]Neil G. Marchant, Benjamin I. P. Rubinstein:
In Search of an Entity Resolution OASIS: Optimal Asymptotic Sequential Importance Sampling. Proc. VLDB Endow. 10(11): 1322-1333 (2017) - [c30]Francesco Aldà, Benjamin I. P. Rubinstein:
The Bernstein Mechanism: Function Release under Differential Privacy. AAAI 2017: 1705-1711 - [c29]Benjamin I. P. Rubinstein, Francesco Aldà:
Pain-Free Random Differential Privacy with Sensitivity Sampling. ICML 2017: 2950-2959 - [c28]Xunyun Liu, Aaron Harwood, Shanika Karunasekera, Benjamin I. P. Rubinstein, Rajkumar Buyya:
E-Storm: Replication-Based State Management in Distributed Stream Processing Systems. ICPP 2017: 571-580 - [i28]Maryam Fanaeepour, Benjamin I. P. Rubinstein:
End-to-End Differentially-Private Parameter Tuning in Spatial Histograms. CoRR abs/1702.05607 (2017) - [i27]Neil G. Marchant, Benjamin I. P. Rubinstein:
In Search of an Entity Resolution OASIS: Optimal Asymptotic Sequential Importance Sampling. CoRR abs/1703.00617 (2017) - [i26]Yi Han, Benjamin I. P. Rubinstein:
Adequacy of the Gradient-Descent Method for Classifier Evasion Attacks. CoRR abs/1704.01704 (2017) - [i25]Chris Culnane, Benjamin I. P. Rubinstein, Vanessa Teague:
Privacy Assessment of De-identified Opal Data: A report for Transport for NSW. CoRR abs/1704.08547 (2017) - [i24]Benjamin I. P. Rubinstein, Francesco Aldà:
Pain-Free Random Differential Privacy with Sensitivity Sampling. CoRR abs/1706.02562 (2017) - [i23]Benjamin Fish, Lev Reyzin, Benjamin I. P. Rubinstein:
Sublinear-Time Adaptive Data Analysis. CoRR abs/1709.09778 (2017) - [i22]Chris Culnane, Benjamin I. P. Rubinstein, Vanessa Teague:
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 abs/1712.00871 (2017) - [i21]Chris Culnane, Benjamin I. P. Rubinstein, Vanessa Teague:
Health Data in an Open World. CoRR abs/1712.05627 (2017) - 2016
- [j13]Yi Han, Tansu Alpcan, Jeffrey Chan, Christopher Leckie, Benjamin I. P. Rubinstein:
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. 11(3): 556-570 (2016) - [c27]Jiazhen He, Benjamin I. P. Rubinstein, James Bailey, Rui Zhang, Sandra Milligan, Jeffrey Chan:
MOOCs Meet Measurement Theory: A Topic-Modelling Approach. AAAI 2016: 1195-1201 - [c26]Zuhe Zhang, Benjamin I. P. Rubinstein, Christos Dimitrakakis:
On the Differential Privacy of Bayesian Inference. AAAI 2016: 2365-2371 - [c25]Tansu Alpcan, Benjamin I. P. Rubinstein, Christopher Leckie:
Large-scale strategic games and adversarial machine learning. CDC 2016: 4420-4426 - [c24]Maryam Fanaeepour, Benjamin I. P. Rubinstein:
Beyond Points and Paths: Counting Private Bodies. ICDM 2016: 131-140 - [c23]Zay Maung Maung Aye, Kotagiri Ramamohanarao, Benjamin I. P. Rubinstein:
Large Scale Metric learning. IJCNN 2016: 1442-1449 - [c22]Iván Sánchez, Zay Maung Maung Aye, Benjamin I. P. Rubinstein, Kotagiri Ramamohanarao:
Fast trajectory clustering using Hashing methods. IJCNN 2016: 3689-3696 - [c21]Sandra Milligan, Jiazhen He, James Bailey, Rui Zhang, Benjamin I. P. Rubinstein:
Validity: a framework for cross-disciplinary collaboration in mining indicators of learning from MOOC forums. LAK 2016: 546-547 - [i20]Jiazhen He, Rui Zhang, James Bailey, Benjamin I. P. Rubinstein, Sandra Milligan:
TopicResponse: A Marriage of Topic Modelling and Rasch Modelling for Automatic Measurement in MOOCs. CoRR abs/1607.08720 (2016) - [i19]Tansu Alpcan, Benjamin I. P. Rubinstein, Christopher Leckie:
Large-Scale Strategic Games and Adversarial Machine Learning. CoRR abs/1609.06438 (2016) - [i18]Maryam Fanaeepour, Benjamin I. P. Rubinstein:
Beyond Points and Paths: Counting Private Bodies. CoRR abs/1609.07983 (2016) - 2015
- [j12]Maryam Fanaeepour, Lars Kulik, Egemen Tanin, Benjamin I. P. Rubinstein:
The CASE histogram: privacy-aware processing of trajectory data using aggregates. GeoInformatica 19(4): 747-798 (2015) - [j11]Duo Zhang, Benjamin I. P. Rubinstein, Jim Gemmell:
Principled Graph Matching Algorithms for Integrating Multiple Data Sources. IEEE Trans. Knowl. Data Eng. 27(10): 2784-2796 (2015) - [c20]Jiazhen He, James Bailey, Benjamin I. P. Rubinstein, Rui Zhang:
Identifying At-Risk Students in Massive Open Online Courses. AAAI 2015: 1749-1755 - [c19]Zhe Lim, Benjamin I. P. Rubinstein:
Sub-Merge: Diving Down to the Attribute-Value Level in Statistical Schema Matching. AAAI 2015: 1791-1797 - [i17]Jiazhen He, Benjamin I. P. Rubinstein, James Bailey, Rui Zhang, Sandra Milligan, Jeffrey Chan:
MOOCs Meet Measurement Theory: A Topic-Modelling Approach. CoRR abs/1511.07961 (2015) - [i16]Zuhe Zhang, Benjamin I. P. Rubinstein, Christos Dimitrakakis:
On the Differential Privacy of Bayesian Inference. CoRR abs/1512.06992 (2015) - 2014
- [c18]Christos Dimitrakakis, Blaine Nelson, Aikaterini Mitrokotsa, Benjamin I. P. Rubinstein:
Robust and Private Bayesian Inference. ALT 2014: 291-305 - [c17]Christos Dimitrakakis, Aikaterini Mitrokotsa, Benjamin I. P. Rubinstein:
Workshop Summary of AISec'14: 2014 Workshop on Artificial Intelligent and Security. CCS 2014: 1555 - [e2]Christos Dimitrakakis, Aikaterini Mitrokotsa, Benjamin I. P. Rubinstein, Gail-Joon Ahn:
Proceedings of the 2014 Workshop on Artificial Intelligent and Security Workshop, AISec 2014, Scottsdale, AZ, USA, November 7, 2014. ACM 2014, ISBN 978-1-4503-3153-1 [contents] - [i15]J. Hyam Rubinstein, Benjamin I. P. Rubinstein, Peter L. Bartlett:
Bounding Embeddings of VC Classes into Maximum Classes. CoRR abs/1401.7388 (2014) - [i14]Battista Biggio, Igino Corona, Blaine Nelson, Benjamin I. P. Rubinstein, Davide Maiorca, Giorgio Fumera, Giorgio Giacinto, Fabio Roli:
Security Evaluation of Support Vector Machines in Adversarial Environments. CoRR abs/1401.7727 (2014) - [i13]Duo Zhang, Benjamin I. P. Rubinstein, Jim Gemmell:
Principled Graph Matching Algorithms for Integrating Multiple Data Sources. CoRR abs/1402.0282 (2014) - 2013
- [c16]Christian Guttmann, Xingzhi Sun, Chaitanya Rao, Carlos Queiroz, Benjamin I. P. Rubinstein:
On the challenges of balancing privacy and utility of open health data. AIIP/Semantic Cities@IJCAI 2013: 43-47 - [i12]Christos Dimitrakakis, Blaine Nelson, Aikaterini Mitrokotsa, Benjamin I. P. Rubinstein:
Robust, Secure and Private Bayesian Inference. CoRR abs/1306.1066 (2013) - 2012
- [j10]Benjamin I. P. Rubinstein, J. Hyam Rubinstein:
A Geometric Approach to Sample Compression. J. Mach. Learn. Res. 13: 1221-1261 (2012) - [j9]Blaine Nelson, Benjamin I. P. Rubinstein, Ling Huang, Anthony D. Joseph, Steven J. Lee, Satish Rao, J. D. Tygar:
Query Strategies for Evading Convex-Inducing Classifiers. J. Mach. Learn. Res. 13: 1293-1332 (2012) - [j8]Benjamin I. P. Rubinstein, Peter L. Bartlett, Ling Huang, Nina Taft:
Learning in a Large Function Space: Privacy-Preserving Mechanisms for SVM Learning. J. Priv. Confidentiality 4(1) (2012) - [j7]Bo Zhao, Benjamin I. P. Rubinstein, Jim Gemmell, Jiawei Han:
A Bayesian Approach to Discovering Truth from Conflicting Sources for Data Integration. Proc. VLDB Endow. 5(6): 550-561 (2012) - [j6]Adam Barth, Benjamin I. P. Rubinstein, Mukund Sundararajan, John C. Mitchell, Dawn Song, Peter L. Bartlett:
A Learning-Based Approach to Reactive Security. IEEE Trans. Dependable Secur. Comput. 9(4): 482-493 (2012) - [j5]Benjamin I. P. Rubinstein, Aleksandr Simma:
On the Stability of Empirical Risk Minimization in the Presence of Multiple Risk Minimizers. IEEE Trans. Inf. Theory 58(7): 4160-4163 (2012) - [c15]Alvaro A. Cárdenas, Blaine Nelson, Benjamin I. P. Rubinstein:
Fifth ACM workshop on artificial intelligence and security (AISec 2012). CCS 2012: 1056-1057 - [c14]Sahand Negahban, Benjamin I. P. Rubinstein, Jim Gemmell:
Scaling multiple-source entity resolution using statistically efficient transfer learning. CIKM 2012: 2224-2228 - [i11]Bo Zhao, Benjamin I. P. Rubinstein, Jim Gemmell, Jiawei Han:
A Bayesian Approach to Discovering Truth from Conflicting Sources for Data Integration. CoRR abs/1203.0058 (2012) - [i10]Sahand Negahban, Benjamin I. P. Rubinstein, Jim Gemmell:
Scaling Multiple-Source Entity Resolution using Statistically Efficient Transfer Learning. CoRR abs/1208.1860 (2012) - 2011
- [c13]Ling Huang, Anthony D. Joseph, Blaine Nelson, Benjamin I. P. Rubinstein, J. D. Tygar:
Adversarial machine learning. AISec 2011: 43-58 - [c12]Arvind Narayanan, Elaine Shi, Benjamin I. P. Rubinstein:
Link prediction by de-anonymization: How We Won the Kaggle Social Network Challenge. IJCNN 2011: 1825-1834 - [e1]Yan Chen, Alvaro A. Cárdenas, Rachel Greenstadt, Benjamin I. P. Rubinstein:
Proceedings of the 4th ACM Workshop on Security and Artificial Intelligence, AISec 2011, Chicago, IL, USA, October 21, 2011. ACM 2011, ISBN 978-1-4503-1003-1 [contents] - [i9]Arvind Narayanan, Elaine Shi, Benjamin I. P. Rubinstein:
Link Prediction by De-anonymization: How We Won the Kaggle Social Network Challenge. CoRR abs/1102.4374 (2011) - [i8]Jim Gemmell, Benjamin I. P. Rubinstein, Ashok K. Chandra:
Improving Entity Resolution with Global Constraints. CoRR abs/1108.6016 (2011) - [i7]Adam Barth, Saung Li, Benjamin I. P. Rubinstein, Dawn Song:
How Open Should Open Source Be? CoRR abs/1109.0507 (2011) - 2010
- [b1]Benjamin I. P. Rubinstein:
Secure Learning and Learning for Security: Research in the Intersection. University of California, Berkeley, USA, 2010 - [j4]Benjamin I. P. Rubinstein, Peter L. Bartlett, J. Hyam Rubinstein:
Corrigendum to "Shifting: One-inclusion mistake bounds and sample compression" [J. Comput. System Sci 75 (1) (2009) 37-59]. J. Comput. Syst. Sci. 76(3-4): 278-280 (2010) - [c11]Adam Barth, Benjamin I. P. Rubinstein, Mukund Sundararajan, John C. Mitchell, Dawn Song, Peter L. Bartlett:
A Learning-Based Approach to Reactive Security. Financial Cryptography 2010: 192-206 - [c10]Blaine Nelson, Benjamin I. P. Rubinstein, Ling Huang, Anthony D. Joseph, J. D. Tygar:
Classifier Evasion: Models and Open Problems. PSDML 2010: 92-98 - [c9]Blaine Nelson, Benjamin I. P. Rubinstein, Ling Huang, Anthony D. Joseph, Shing-hon Lau, Steven J. Lee, Satish Rao, Anthony Tran, J. Doug Tygar:
Near-Optimal Evasion of Convex-Inducing Classifiers. AISTATS 2010: 549-556 - [i6]Benjamin I. P. Rubinstein, Aleksandr Simma:
On the Stability of Empirical Risk Minimization in the Presence of Multiple Risk Minimizers. CoRR abs/1002.2044 (2010) - [i5]Blaine Nelson, Benjamin I. P. Rubinstein, Ling Huang, Anthony D. Joseph, Shing-hon Lau, Steven J. Lee, Satish Rao, Anthony Tran, J. D. Tygar:
Near-Optimal Evasion of Convex-Inducing Classifiers. CoRR abs/1003.2751 (2010) - [i4]Blaine Nelson, Benjamin I. P. Rubinstein, Ling Huang, Anthony D. Joseph, Steven J. Lee, Satish Rao, J. D. Tygar:
Query Strategies for Evading Convex-Inducing Classifiers. CoRR abs/1007.0484 (2010)
2000 – 2009
- 2009
- [j3]Benjamin I. P. Rubinstein, Peter L. Bartlett, J. Hyam Rubinstein:
Shifting: One-inclusion mistake bounds and sample compression. J. Comput. Syst. Sci. 75(1): 37-59 (2009) - [j2]Benjamin I. P. Rubinstein, Blaine Nelson, Ling Huang, Anthony D. Joseph, Shing-hon Lau, Satish Rao, Nina Taft, J. D. Tygar:
Stealthy poisoning attacks on PCA-based anomaly detectors. SIGMETRICS Perform. Evaluation Rev. 37(2): 73-74 (2009) - [c8]Benjamin I. P. Rubinstein, Blaine Nelson, Ling Huang, Anthony D. Joseph, Shing-hon Lau, Satish Rao, Nina Taft, J. D. Tygar:
ANTIDOTE: understanding and defending against poisoning of anomaly detectors. Internet Measurement Conference 2009: 1-14 - [c7]Arpita Ghosh, Benjamin I. P. Rubinstein, Sergei Vassilvitskii, Martin Zinkevich:
Adaptive bidding for display advertising. WWW 2009: 251-260 - [i3]Benjamin I. P. Rubinstein, J. Hyam Rubinstein:
A Geometric Approach to Sample Compression. CoRR abs/0911.3633 (2009) - [i2]Benjamin I. P. Rubinstein, Peter L. Bartlett, Ling Huang, Nina Taft:
Learning in a Large Function Space: Privacy-Preserving Mechanisms for SVM Learning. CoRR abs/0911.5708 (2009) - [i1]Adam Barth, Benjamin I. P. Rubinstein, Mukund Sundararajan, John C. Mitchell, Dawn Xiaodong Song, Peter L. Bartlett:
A Learning-Based Approach to Reactive Security. CoRR abs/0912.1155 (2009) - 2008
- [c6]Marco Barreno, Peter L. Bartlett, Fuching Jack Chi, Anthony D. Joseph, Blaine Nelson, Benjamin I. P. Rubinstein, Udam Saini, J. Doug Tygar:
Open problems in the security of learning. AISec 2008: 19-26 - [c5]J. Hyam Rubinstein, Benjamin I. P. Rubinstein:
Geometric & Topological Representations of Maximum Classes with Applications to Sample Compression. COLT 2008: 299-310 - [c4]Blaine Nelson, Marco Barreno, Fuching Jack Chi, Anthony D. Joseph, Benjamin I. P. Rubinstein, Udam Saini, Charles Sutton, J. Doug Tygar, Kai Xia:
Exploiting Machine Learning to Subvert Your Spam Filter. LEET 2008 - [c3]Benjamin I. P. Rubinstein, Blaine Nelson, Ling Huang, Anthony D. Joseph, Shing-hon Lau, Nina Taft, J. Doug Tygar:
Evading Anomaly Detection through Variance Injection Attacks on PCA. RAID 2008: 394-395 - 2006
- [c2]Benjamin I. P. Rubinstein, Peter L. Bartlett, J. Hyam Rubinstein:
Shifting, One-Inclusion Mistake Bounds and Tight Multiclass Expected Risk Bounds. NIPS 2006: 1193-1200 - 2003
- [j1]Benjamin I. P. Rubinstein, Jon D. McAuliffe, Simon Cawley, Marimuthu Palaniswami, Kotagiri Ramamohanarao, Terence P. Speed:
Machine learning in low-level microarray analysis. SIGKDD Explor. 5(2): 130-139 (2003) - 2001
- [c1]Benjamin I. P. Rubinstein:
Evolving quantum circuits using genetic programming. CEC 2001: 144-151
Coauthor Index
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