Zachary C. Lipton

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	All	Since 2016
Citations	7644	7568
h-index	34	34
i10-index	48	48

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201520162017201820192020202126 138 528 1373 2379 3038 67

Co-authors

Charles ElkanProfessor of Computer Science, University of California, San DiegoVerified email at ucsd.edu
Anima AnandkumarCalifornia Institute of Technology and NVIDIAVerified email at caltech.edu
Julian McAuleyAssociate Professor, UC San DiegoVerified email at eng.ucsd.edu
David KaleSenior Machine Learning Scientist, NetflixVerified email at netflix.com
John BerkowitzIn-Q-Tel LabsVerified email at iqt.org
Jianfeng GaoMicrosoft Research, RedmondVerified email at microsoft.com
Lihong Li (李力鸿)AmazonVerified email at amazon.com
Li DengChief AI Officer, CitadelVerified email at ieee.org
Kamyar AzizzadenesheliAssistant Professor at Purdue UniversityVerified email at purdue.edu
Tommaso FurlanelloPhd Candidate, University of Southern CaliforniaVerified email at usc.edu
Alex SmolaAmazon Web ServicesVerified email at smola.org
Bhuwan DhingraLanguage Technologies Institute, Carnegie Mellon UniversityVerified email at andrew.cmu.edu
Mu LiAmazonVerified email at amazon.com
Jean KossaifiSenior Research Scientist at NVIDIAVerified email at nvidia.com
Aran KhannaReserved.ai, Harvard University, Amazon AIVerified email at arankhanna.com
Divyansh KaushikPhD Student at Language Technologies Institute, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Faisal Ahmed, PhDSUNY Stony BrookVerified email at cs.stonybrook.edu
Balakrishnan NarayanaswamyAWS AI, AmazonVerified email at amazon.com
Subarna TripathiIntel LabsVerified email at ucsd.edu
Laurent IttiProfessor of Computer Science, University of Southern CaliforniaVerified email at usc.edu

Zachary C. Lipton

Assistant Professor of Operations Research & Machine Learning at CMU

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) [Prev. ICML Workshop on Human …, 2016	1780	2016
A critical review of recurrent neural networks for sequence learning ZC Lipton, J Berkowitz, C Elkan arXiv preprint arXiv:1506.00019, 2015	1479	2015
Learning to Diagnose with LSTM Recurrent Neural Networks ZC Lipton, DC Kale, C Elkan, R Wetzell International Conference on Learning Representations (ICLR), 2015	786	2015
Born again neural networks T Furlanello, ZC Lipton, M Tschannen, L Itti, A Anandkumar International Conference on Machine Learning (ICML), 2018	319	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	258	2018
Modeling Missing Data in Clinical Time Series with RNNs ZC Lipton, DC Kale, R Wetzel Machine Learning for Healthcare (MLHC), 2016	253*	2016
Optimal thresholding of classifiers to maximize F1 measure ZC Lipton, C Elkan, B Naryanaswamy European Conference on Machine Learning (ECML), 225-239, 2014	225*	2014
Deep Active Learning for Named Entity Recognition Y Shen, H Yun, ZC Lipton, Y Kronrod, A Anandkumar International Conference on Learning Representations (ICLR), 2018	190	2018
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	149*	2018
Troubling Trends in Machine Learning Scholarship ZC Lipton, J Steinhardt Communications of the ACM 62 (6), 45-53, 2019	146	2019
Differential privacy and machine learning: a survey and review Z Ji, ZC Lipton, C Elkan arXiv preprint arXiv:1412.7584, 2014	146	2014
Dive into deep learning A Zhang, ZC Lipton, M Li, AJ Smola Unpublished Draft. Retrieved 19, 2019, 2019	115*	2019
How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks D Kaushik, ZC Lipton Empirical Methods in Natural Language Processing (EMNLP), 2018	103	2018
Precise Recovery of Latent Vectors from Generative Adversarial Networks ZC Lipton, S Tripathi ICLR Workshop 2017, 2017	102	2017
Detecting and Correcting for Label Shift with Black Box Predictors ZC Lipton, YX Wang, A Smola International Conference on Machine Learning (ICML), 2018	98	2018
Does mitigating ML’s impact disparity require treatment disparity? ZC Lipton, A Chouldechova, J McAuley Advances in Neural Information Processing Systems (NeurIPS), 2018	91*	2018
A user simulator for task-completion dialogues X Li, ZC Lipton, B Dhingra, L Li, J Gao, YN Chen arXiv preprint arXiv:1612.05688, 2016	87	2016
Semantically decomposing the latent spaces of generative adversarial networks C Donahue, A Balsubramani, J McAuley, ZC Lipton International Conference on Learning Representations (ICLR), 2018	79	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	77	2020
Learning From Noisy Singly-labeled Data A Khetan, ZC Lipton, A Anandkumar International Conference on Learning Representations (ICLR), 2018	71	2018

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