Zachary C. Lipton

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Anima AnandkumarCalifornia Institute of Technology and NVIDIAVerified email at caltech.edu
Charles ElkanProfessor of Computer Science, University of California, San DiegoVerified email at ucsd.edu
Aston ZhangMeta Generative AIVerified email at meta.com
Kamyar AzizzadenesheliNvidiaVerified email at nvidia.com
Alex SmolaBoson AIVerified email at smola.org
Julian McAuleyProfessor, UC San DiegoVerified email at eng.ucsd.edu
Dave KaleData Science Manager, LiteratiVerified email at literati.com
Divyansh KaushikAssociate Director, Emerging Technologies and National Security, Federation of American ScientistsVerified email at fas.org
Jianfeng GaoMicrosoft Research, RedmondVerified email at microsoft.com
Saurabh GargPhD Student, Machine Learning Department, Carnegie Mellon UniversityVerified email at andrew.cmu.edu
Lihong Li (李力鸿)AmazonVerified email at amazon.com
John BerkowitzAppleVerified email at apple.com
Bhuwan DhingraAssistant Professor, Computer Science, Duke UniversityVerified email at cs.duke.edu
Mu LiAmazonVerified email at amazon.com
Sivaraman BalakrishnanCarnegie Mellon UniversityVerified email at stat.cmu.edu
Tommaso FurlanelloLeading Science at HK3 LabVerified email at usc.edu
Aran KhannaArchera.ai, Harvard University, Amazon AIVerified email at arankhanna.com
Li DengChief AI Officer, Citadel (former)Verified email at ieee.org
Eric XingPresident at Mohamed bin Zayed University of AI, Professor of Computer Science, Carnegie Mellon UVerified email at cs.cmu.edu
Balakrishnan NarayanaswamyPrincipal ML Scientist, AWS AI, AmazonVerified email at amazon.com

Zachary C. Lipton

Professor in the Machine Learning Department at Carnegie Mellon; Chief Scientific Officer at Abridge

Verified email at cmu.edu - Homepage

Machine Learning Healthcare Technology & Society Natural Language Processing Robustness & Causality


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
The Mythos of Model Interpretability ZC Lipton Communications of the ACM (CACM), 2016	4594*	2016
A critical review of recurrent neural networks for sequence learning ZC Lipton, J Berkowitz, C Elkan arXiv preprint arXiv:1506.00019, 2015	2887	2015
Learning to Diagnose with LSTM Recurrent Neural Networks ZC Lipton, DC Kale, C Elkan, R Wetzell International Conference on Learning Representations (ICLR), 2015	1298	2015
Dive into Deep Learning AJS Aston Zhang, Zachary C. Lipton, Mu Li pre-print, 2020	1014*	2020
Born again neural networks T Furlanello, ZC Lipton, M Tschannen, L Itti, A Anandkumar International Conference on Machine Learning (ICML), 2018	943	2018
Stochastic activation pruning for robust adversarial defense GS Dhillon, K Azizzadenesheli, ZC Lipton, J Bernstein, J Kossaifi, ... International Conference on Learning Representations (ICLR), 2018	598	2018
Optimal thresholding of classifiers to maximize F1 measure ZC Lipton, C Elkan, B Naryanaswamy European Conference on Machine Learning (ECML), 225-239, 2014	554	2014
Modeling Missing Data in Clinical Time Series with RNNs ZC Lipton, DC Kale, R Wetzel Machine Learning for Healthcare (MLHC), 2016	483*	2016
Deep Active Learning for Named Entity Recognition Y Shen, H Yun, ZC Lipton, Y Kronrod, A Anandkumar International Conference on Learning Representations (ICLR), 2018	471	2018
Learning the Difference that Makes a Difference with Counterfactually-Augmented Data D Kaushik, E Hovy, ZC Lipton International Conference on Learning Representations (ICLR), 2020	451	2020
Detecting and Correcting for Label Shift with Black Box Predictors ZC Lipton, YX Wang, A Smola International Conference on Machine Learning (ICML), 2018	437	2018
Learning robust global representations by penalizing local predictive power H Wang, S Ge, EP Xing, ZC Lipton Advances in Neural Information Processing Systems (NeurIPS), 2019	428	2019
What is the Effect of Importance Weighting in Deep Learning? J Byrd, Z Lipton International Conference on Machine Learning (ICML), 872-881, 2019	380	2019
Context Matters: Refining Object Detection in Video with Recurrent Neural Networks S Tripathi, ZC Lipton, S Belongie, T Nguyen British Machine Vision Conference (BMVC), 2016	330	2016
Troubling Trends in Machine Learning Scholarship ZC Lipton, J Steinhardt Communications of the ACM 62 (6), 45-53, 2019	313*	2019
Differential privacy and machine learning: a survey and review Z Ji, ZC Lipton, C Elkan arXiv preprint arXiv:1412.7584, 2014	299	2014
Failing loudly: An empirical study of methods for detecting dataset shift S Rabanser, S Günnemann, ZC Lipton Advances in Neural Information Processing Systems (NeurIPS), 2018	265	2018
Combating Adversarial Misspellings with Robust Word Recognition D Pruthi, B Dhingra, ZC Lipton Association for Computational Linguistics (ACL), 2019	260	2019
Efficient exploration for dialogue policy learning with BBQ-networks ZC Lipton, J Gao, L Li, X Li, F Ahmed, L Deng Association for the Advancement of Artificial Intelligence (AAAI), 2018	241*	2018
Precise Recovery of Latent Vectors from Generative Adversarial Networks ZC Lipton, S Tripathi ICLR Workshop 2017, 2017	234	2017

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