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ORCIDBenjamin I. P. Rubinstein
Person information
- 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
- 2023
[i61]Neil G. Marchant, Benjamin I. P. Rubinstein, Rebecca C. Steorts:
Bayesian Graphical Entity Resolution Using Exchangeable Random Partition Priors. CoRR abs/2301.02962 (2023)- [i60]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) - [i59]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) - 2022
- [j20]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) - [j19]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) - [c65]Neil G. Marchant, Benjamin I. P. Rubinstein, Scott Alfeld:
Hard to Forget: Poisoning Attacks on Certified Machine Unlearning. AAAI 2022: 7691-7700 - [c64]Jun Wang, Benjamin I. P. Rubinstein, Trevor Cohn:
Measuring and Mitigating Name Biases in Neural Machine Translation. ACL (1) 2022: 2576-2590 - [c63]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 - [c62]Jun Wang, Xuanli He, Benjamin I. P. Rubinstein, Trevor Cohn:
Foiling Training-Time Attacks on Neural Machine Translation Systems. EMNLP (Findings) 2022: 5906-5913 - [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. IEEE Symposium on Security and Privacy 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
- [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]