| Automatic perturbation analysis for scalable certified robustness and beyond K Xu, Z Shi, H Zhang, Y Wang, KW Chang, M Huang, B Kailkhura, X Lin, ... Advances in Neural Information Processing Systems 33, 1129-1141, 2020 | 263 | 2020 |
| Anomalous example detection in deep learning: A survey S Bulusu, B Kailkhura, B Li, PK Varshney, D Song IEEE Access 8, 132330-132347, 2020 | 254* | 2020 |
| Mix-n-match: Ensemble and compositional methods for uncertainty calibration in deep learning J Zhang, B Kailkhura, TYJ Han International conference on machine learning, 11117-11128, 2020 | 204 | 2020 |
| A primer on zeroth-order optimization in signal processing and machine learning: Principals, recent advances, and applications S Liu, PY Chen, B Kailkhura, G Zhang, AO Hero III, PK Varshney IEEE Signal Processing Magazine 37 (5), 43-54, 2020 | 193 | 2020 |
| Zeroth-order stochastic variance reduction for nonconvex optimization S Liu, B Kailkhura, PY Chen, P Ting, S Chang, L Amini Advances in Neural Information Processing Systems 31 (2018): 3727-3737, 2018 | 175 | 2018 |
| Reliable and explainable machine-learning methods for accelerated material discovery B Kailkhura, B Gallagher, S Kim, A Hiszpanski, TYJ Han npj Computational Materials 5 (1), 108, 2019 | 153 | 2019 |
| Position: TrustLLM: Trustworthiness in Large Language Models Y Huang, L Sun, H Wang, S Wu, Q Zhang, Y Li, C Gao, Y Huang, W Lyu, ... International Conference on Machine Learning, 20166-20270, 2024 | 132* | 2024 |
| Explainable machine learning in materials science X Zhong, B Gallagher, S Liu, B Kailkhura, A Hiszpanski, TYJ Han npj computational materials 8 (1), 204, 2022 | 132* | 2022 |
| Generative counterfactual introspection for explainable deep learning S Liu, B Kailkhura, D Loveland, Y Han 2019 IEEE global conference on signal and information processing (GlobalSIP …, 2019 | 125 | 2019 |
| Distributed Bayesian detection in the presence of Byzantine data B Kailkhura, YS Han, S Brahma, PK Varshney IEEE transactions on signal processing 63 (19), 5250-5263, 2015 | 118 | 2015 |
| Data falsification attacks on consensus-based detection systems B Kailkhura, S Brahma, PK Varshney IEEE Transactions on Signal and Information Processing over Networks 3 (1 …, 2016 | 108* | 2016 |
| Benchmarking robustness of 3d point cloud recognition against common corruptions J Sun, Q Zhang, B Kailkhura, Z Yu, C Xiao, ZM Mao arXiv preprint arXiv:2201.12296, 2022 | 92 | 2022 |
| Multi-prize lottery ticket hypothesis: Finding accurate binary neural networks by pruning a randomly weighted network J Diffenderfer, B Kailkhura International Conference on Learning Representations. 2020., 2021 | 91 | 2021 |
| Mimicgan: Robust projection onto image manifolds with corruption mimicking R Anirudh, JJ Thiagarajan, B Kailkhura, PT Bremer International Journal of Computer Vision 128, 2459-2477, 2020 | 87* | 2020 |
| On the design of black-box adversarial examples by leveraging gradient-free optimization and operator splitting method P Zhao, S Liu, PY Chen, N Hoang, K Xu, B Kailkhura, X Lin Proceedings of the IEEE/CVF International Conference on Computer Vision, 121-130, 2019 | 74 | 2019 |
| Treeview: Peeking into deep neural networks via feature-space partitioning JJ Thiagarajan, B Kailkhura, P Sattigeri, KN Ramamurthy arXiv preprint arXiv:1611.07429, 2016 | 73 | 2016 |
| Nanomaterial synthesis insights from machine learning of scientific articles by extracting, structuring, and visualizing knowledge AM Hiszpanski, B Gallagher, K Chellappan, P Li, S Liu, H Kim, J Han, ... Journal of chemical information and modeling 60 (6), 2876-2887, 2020 | 71 | 2020 |
| Performance modeling under resource constraints using deep transfer learning A Marathe, R Anirudh, N Jain, A Bhatele, J Thiagarajan, B Kailkhura, ... Proceedings of the International Conference for High Performance Computing …, 2017 | 65 | 2017 |
| A Winning Hand: Compressing Deep Networks Can Improve Out-Of-Distribution Robustness J Diffenderfer, BR Bartoldson, S Chaganti, J Zhang, B Kailkhura NeurIPS 2021, 2021 | 64 | 2021 |
| G-pate: Scalable differentially private data generator via private aggregation of teacher discriminators Y Long, B Wang, Z Yang, B Kailkhura, A Zhang, C Gunter, B Li Advances in Neural Information Processing Systems 34, 2965-2977, 2021 | 61 | 2021 |
Pramod VarshneyDistinguished Professor of EECS, Syracuse University在 syr.edu 的電子郵件地址已通過驗證
