Supriyo Chakraborty
Supriyo Chakraborty
Distinguished Applied Researcher, AI Foundations, Capital One
Verified email at - Homepage
Cited by
Cited by
Analyzing federated learning through an adversarial lens
AN Bhagoji, S Chakraborty, P Mittal, S Calo
International Conference on Machine Learning, 634-643, 2019
Interpretability of deep learning models: A survey of results
S Chakraborty, R Tomsett, R Raghavendra, D Harborne, M Alzantot, ...
2017 IEEE smartworld, ubiquitous intelligence & computing, advanced …, 2017
Genattack: Practical black-box attacks with gradient-free optimization
M Alzantot, Y Sharma, S Chakraborty, H Zhang, CJ Hsieh, MB Srivastava
Proceedings of the genetic and evolutionary computation conference, 1111-1119, 2019
Dependence makes you vulnberable: Differential privacy under dependent tuples.
C Liu, S Chakraborty, P Mittal
NDSS 16, 21-24, 2016
Stakeholders in explainable AI
A Preece, D Harborne, D Braines, R Tomsett, S Chakraborty
arXiv preprint arXiv:1810.00184, 2018
Interpretable to whom? A role-based model for analyzing interpretable machine learning systems
R Tomsett, D Braines, D Harborne, A Preece, S Chakraborty
arXiv preprint arXiv:1806.07552, 2018
Sensegen: A deep learning architecture for synthetic sensor data generation
M Alzantot, S Chakraborty, M Srivastava
2017 IEEE International Conference on Pervasive Computing and Communications …, 2017
Neighborhood based fast graph search in large networks
A Khan, N Li, X Yan, Z Guan, S Chakraborty, S Tao
Proceedings of the 2011 ACM SIGMOD International Conference on Management of …, 2011
Sanity checks for saliency metrics
R Tomsett, D Harborne, S Chakraborty, P Gurram, A Preece
Proceedings of the AAAI conference on artificial intelligence 34 (04), 6021-6029, 2020
Ibm federated learning: an enterprise framework white paper v0. 1
H Ludwig, N Baracaldo, G Thomas, Y Zhou, A Anwar, S Rajamoni, Y Ong, ...
arXiv preprint arXiv:2007.10987, 2020
Rapid trust calibration through interpretable and uncertainty-aware AI
R Tomsett, A Preece, D Braines, F Cerutti, S Chakraborty, M Srivastava, ...
Patterns 1 (4), 2020
{ipShield}: A Framework For Enforcing {Context-Aware} Privacy
S Chakraborty, C Shen, KR Raghavan, Y Shoukry, M Millar, M Srivastava
11th USENIX symposium on networked systems design and implementation (NSDI …, 2014
Fair transfer learning with missing protected attributes
A Coston, KN Ramamurthy, D Wei, KR Varshney, S Speakman, ...
Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 91-98, 2019
Blockchain analytics and artificial intelligence
DN Dillenberger, P Novotny, Q Zhang, P Jayachandran, H Gupta, S Hans, ...
IBM Journal of Research and Development 63 (2/3), 5: 1-5: 14, 2019
Sparsefed: Mitigating model poisoning attacks in federated learning with sparsification
A Panda, S Mahloujifar, AN Bhagoji, S Chakraborty, P Mittal
International Conference on Artificial Intelligence and Statistics, 7587-7624, 2022
A framework for context-aware privacy of sensor data on mobile systems
S Chakraborty, KR Raghavan, MP Johnson, MB Srivastava
Proceedings of the 14th Workshop on Mobile Computing Systems and …, 2013
Compressive oversampling for robust data transmission in sensor networks
Z Charbiwala, S Chakraborty, S Zahedi, T He, C Bisdikian, Y Kim, ...
2010 Proceedings IEEE INFOCOM, 1-9, 2010
Sat: Improving adversarial training via curriculum-based loss smoothing
C Sitawarin, S Chakraborty, D Wagner
Proceedings of the 14th ACM Workshop on Artificial Intelligence and Security …, 2021
PrOLoc: Resilient localization with private observers using partial homomorphic encryption
A Alanwar, Y Shoukry, S Chakraborty, P Martin, P Tabuada, M Srivastava
Proceedings of the 16th ACM/IEEE International Conference on Information …, 2017
Sensorsafe: a framework for privacy-preserving management of personal sensory information
H Choi, S Chakraborty, ZM Charbiwala, MB Srivastava
Secure Data Management: 8th VLDB Workshop, SDM 2011, Seattle, WA, USA …, 2011
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