| Analyzing federated learning through an adversarial lens AN Bhagoji, S Chakraborty, P Mittal, S Calo International Conference on Machine Learning, 634-643, 2019 | 1307 | 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 | 564 | 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 | 301 | 2019 |
| Dependence makes you vulnberable: Differential privacy under dependent tuples. C Liu, S Chakraborty, P Mittal NDSS 16, 21-24, 2016 | 271 | 2016 |
| Stakeholders in explainable AI A Preece, D Harborne, D Braines, R Tomsett, S Chakraborty arXiv preprint arXiv:1810.00184, 2018 | 265 | 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 | 226 | 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 | 203 | 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 | 184 | 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 | 180 | 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 | 173 | 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 | 142 | 2020 |
| 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 | 118 | 2019 |
| {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 | 116 | 2014 |
| Treatment of a textile effluent: application of a combination method involving adsorption and nanofiltration S Chakraborty, S De, JK Basu, S DasGupta Desalination 174 (1), 73-85, 2005 | 116 | 2005 |
| 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 | 104 | 2022 |
| 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 | 104 | 2019 |
| 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 | 79 | 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 | 77 | 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 | 72* | 2021 |
| Model poisoning attacks in federated learning AN Bhagoji, S Chakraborty, P Mittal, S Calo Proc. Workshop Secur. Mach. Learn.(SecML) 32nd Conf. Neural Inf. Process …, 2018 | 64 | 2018 |
