Bhavya Kailkhura

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Pramod VarshneyDistinguished Professor of EECS, Syracuse UniversityVerified email at syr.edu
Jayaraman J. ThiagarajanMachine Learning ResearcherVerified email at asu.edu
James DiffenderferUniversity of Florida, Lawrence 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
Sijia LiuMichigan State University & MIT-IBM Watson AI LabVerified email at msu.edu
Bo LiUniversity of Illinois at Urbana–ChampaignVerified email at illinois.edu
Brian BartoldsonLawrence Livermore National LaboratoryVerified email at llnl.gov
Dawn SongProfessor of Computer Science, UC BerkeleyVerified email at cs.berkeley.edu
Huan ZhangAssistant Professor at University of Illinois Urbana-ChampaignVerified email at illinois.edu
Cho-Jui HsiehUniversity of California, Los AngelesVerified email at cs.ucla.edu
Chaowei XiaoUniversity of Wisconsin - Madison/NVIDIAVerified email at umich.edu
Gauri JoshiCarnegie Mellon UniversityVerified email at andrew.cmu.edu
Ce ZhangTogether AI; University of ChicagoVerified email at together.xyz
Alfred HeroJohn H Holland Distinguished University Professor, University of MichiganVerified email at eecs.umich.edu
Dan HendrycksDirector of the Center for AI Safety (advisor for xAI and Scale)Verified email at berkeley.edu
Zhangyang (Atlas) WangXTX Markets & University of Texas at AustinVerified email at utexas.edu
Tianlong ChenAssistant Professor, CS@UNC Chapel Hill; PostDoc, CSAIL@MIT+BMI@Harvard; Ph.D., ECE@UT AustinVerified email at cs.unc.edu
Tom GoldsteinVolpi-Cupal Professor of Computer Science, University of MarylandVerified email at cs.umd.edu
Ruoxi JiaAssistant Professor, Virginia TechVerified email at vt.edu
Yingbin LiangThe Ohio State UniversityVerified email at osu.edu

Bhavya Kailkhura

Research Scientist, Lawrence Livermore National Laboratory

Verified email at llnl.gov - Homepage

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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	286	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	266*	2020
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	235*	2024
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	227	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	222	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	186	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	159	2019
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	152*	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	128	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	124	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	110*	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	100	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	96	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	88*	2020
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	76	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	76	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	76	2016
Neftune: Noisy embeddings improve instruction finetuning N Jain, P Chiang, Y Wen, J Kirchenbauer, HM Chu, G Somepalli, ... ICLR 2024, 2023	75*	2023
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	73	2021
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	69	2017

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