Bhavya Kailkhura

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Pramod VarshneyDistinguished Professor of EECS, Syracuse UniversityVerified email at syr.edu
Jayaraman J. ThiagarajanMachine Learning Researcher, Lawrence Livermore National LabsVerified email at asu.edu
T. Yong-Jin HanLawrence Livermore National LaboratoryVerified email at llnl.gov
Pin-Yu ChenPrincipal Research Scientist, IBM Research AI; MIT-IBM Watson AI Lab; RPI-IBM AIRCVerified email at ibm.com
Peer-Timo BremerLawrence Livermore National LaboratoryVerified email at llnl.gov
Rushil AnirudhML Scientist, LLNLVerified email at llnl.gov
Swastik BrahmaAssociate Professor, Department of Computer Science, University of CincinnatiVerified email at ucmail.uc.edu
Xue LinNortheastern UniversityVerified email at northeastern.edu
Kaidi XuAssistant Professor, Drexel UniversityVerified email at northeastern.edu
Anna M. HiszpanskiLawrence Livermore National LaboratoryVerified email at llnl.gov
Sijia LiuMichigan State University & MIT-IBM Watson AI LabVerified email at msu.edu
Yunghsiang S. HanUniversity of Electronic Science and Technology of ChinaVerified email at uestc.edu.cn
Donald LovelandPhD Student, University of MichiganVerified email at umich.edu
Bo LiUniversity of Illinois at Urbana–ChampaignVerified email at illinois.edu
刘树森 Shusen LiuResearch Scientist, Lawrence Livermore National LaboratoryVerified email at sci.utah.edu
James DiffenderferUniversity of Florida, Lawrence Livermore National LaboratoryVerified email at llnl.gov
Jize ZhangHong Kong University of Science and TechnologyVerified email at ust.hk
Akshay MehraTulane UniversityVerified email at tulane.edu
Jihun HammTulane UniversityVerified email at tulane.edu
Thakshila WimalajeewaSenior Professional Staff, Johns Hopkins University Applied Physics Laboratory, Laurel, MD 20723Verified email at ieee.org

Bhavya Kailkhura

Research Scientist, Lawrence Livermore National Laboratory

Verified email at llnl.gov - Homepage

AI Safety/Alignment Efficient/Green AI


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Anomalous example detection in deep learning: A survey S Bulusu, B Kailkhura, B Li, PK Varshney, D Song IEEE Access 8, 132330-132347, 2020	204*	2020
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	188	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	144	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	144	2018
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	130	2020
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	127	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	107	2015
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	103	2019
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	96*	2016
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	84*	2020
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	72	2021
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	62	2019
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	58	2022
Treeview: Peeking into deep neural networks via feature-space partitioning JJ Thiagarajan, B Kailkhura, P Sattigeri, KN Ramamurthy arXiv preprint arXiv:1611.07429, 2016	58	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	57	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	56	2017
Distributed inference in the presence of eavesdroppers: A survey B Kailkhura, VSS Nadendla, PK Varshney IEEE Communications Magazine 53 (6), 40-46, 2015	56	2015
Optimal distributed detection in the presence of byzantines B Kailkhura, S Brahma, YS Han, PK Varshney 2013 IEEE International Conference on Acoustics, Speech and Signal …, 2013	52	2013
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	46	2021
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	43	2022

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