| Adversarial Robustness Toolbox v1. 0.0 MI Nicolae, M Sinn, MN Tran, B Buesser, A Rawat, M Wistuba, ... arXiv preprint arXiv:1807.01069, 2018 | 686 | 2018 |
| A survey on neural architecture search M Wistuba, A Rawat, T Pedapati arXiv preprint arXiv:1905.01392, 2019 | 393 | 2019 |
| Efficient defenses against adversarial attacks V Zantedeschi, MI Nicolae, A Rawat Proceedings of the 10th ACM workshop on artificial intelligence and security …, 2017 | 377 | 2017 |
| 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 | 178 | 2020 |
| Federated unlearning: How to efficiently erase a client in fl? A Halimi, S Kadhe, A Rawat, N Baracaldo arXiv preprint arXiv:2207.05521, 2022 | 106 | 2022 |
| FAT: Federated Adversarial Training G Zizzo, A Rawat, M Sinn, B Buesser arXiv preprint arXiv:2012.01791, 2020 | 71 | 2020 |
| Adversarial phenomenon in the eyes of Bayesian deep learning A Rawat, M Wistuba, MI Nicolae arXiv preprint arXiv:1711.08244, 2017 | 57* | 2017 |
| Adversarial Robustness Toolbox v1. 0.0. arXiv 2018 MI Nicolae, M Sinn, MN Tran, B Buesser, A Rawat, M Wistuba, ... arXiv preprint arXiv:1807.01069, 1807 | 31 | 1807 |
| Survey on Automated End-to-End Data Science D Bouneffouf, C Aggarwal, H Samulowitz, B Buesser, T Hoang, ... 2020 International Joint Conference on Neural Networks (IJCNN), 1-9, 2020 | 27* | 2020 |
| The devil is in the GAN: backdoor attacks and defenses in deep generative models A Rawat, K Levacher, M Sinn European Symposium on Research in Computer Security, 776-783, 2022 | 23* | 2022 |
| Searching for machine learning pipelines using a context-free grammar R Marinescu, A Kishimoto, P Ram, A Rawat, M Wistuba, PP Palmes, ... Proceedings of the AAAI Conference on Artificial Intelligence 35 (10), 8902-8911, 2021 | 20 | 2021 |
| Non-parametric estimation of jensen-shannon divergence in generative adversarial network training M Sinn, A Rawat International Conference on Artificial Intelligence and Statistics, 642-651, 2018 | 20 | 2018 |
| Towards an accountable and reproducible federated learning: A FactSheets approach N Baracaldo, A Anwar, M Purcell, A Rawat, M Sinn, B Altakrouri, D Balta, ... arXiv preprint arXiv:2202.12443, 2022 | 15 | 2022 |
| Open-world visual recognition using knowledge graphs V Lonij, A Rawat, MI Nicolae arXiv preprint arXiv:1708.08310, 2017 | 15 | 2017 |
| Detectors for safe and reliable llms: Implementations, uses, and limitations S Achintalwar, AA Garcia, A Anaby-Tavor, I Baldini, SE Berger, ... arXiv preprint arXiv:2403.06009, 2024 | 14 | 2024 |
| Protecting a machine learning model NM Tran, M Sinn, A Rawat, MI Nicolae, M Wistuba US Patent 11,036,857, 2021 | 14 | 2021 |
| Machine learning platform for extreme scale computing on compressed IoT data S Tirupathi, D Salwala, G Zizzo, A Rawat, M Purcell, SK Jensen, ... 2022 IEEE International Conference on Big Data (Big Data), 3179-3185, 2022 | 12 | 2022 |
| Certified federated adversarial training G Zizzo, A Rawat, M Sinn, S Maffeis, C Hankin arXiv preprint arXiv:2112.10525, 2021 | 12 | 2021 |
| Defending deep generative models against adversarial attacks M Sinn, K Levacher, A Rawat US Patent App. 17/643,896, 2023 | 10 | 2023 |
| Fairsisa: Ensemble post-processing to improve fairness of unlearning in llms SR Kadhe, A Halimi, A Rawat, N Baracaldo arXiv preprint arXiv:2312.07420, 2023 | 9 | 2023 |
Mathieu SinnIBM Research Staff MemberVerifierad e-postadress på ie.ibm.com