Zachary C. Lipton

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	All	Since 2016
Citations	8098	8004
h-index	34	34
i10-index	51	51

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201520162017201820192020202126 143 524 1375 2344 3121 465

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
Jianfeng GaoMicrosoft Research, RedmondVerified email at microsoft.com
Lihong Li (李力鸿)AmazonVerified email at amazon.com
John BerkowitzIn-Q-Tel LabsVerified email at iqt.org
Kamyar AzizzadenesheliAssistant Professor at Purdue UniversityVerified email at purdue.edu
Li DengChief AI Officer, CitadelVerified email at ieee.org
Tommaso FurlanelloPhd Candidate, University of Southern CaliforniaVerified email at usc.edu
Balakrishnan NarayanaswamyAWS AI, AmazonVerified email at amazon.com
Bhuwan DhingraGoogle AIVerified email at google.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
Laurent IttiProfessor of Computer Science, University of Southern CaliforniaVerified email at usc.edu
Subarna TripathiIntel LabsVerified email at ucsd.edu
Alex SmolaAmazon Web ServicesVerified email at smola.org
Danish PruthiCarnegie Mellon UniversityVerified email at cs.cmu.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	1893*	2016
A critical review of recurrent neural networks for sequence learning ZC Lipton, J Berkowitz, C Elkan arXiv preprint arXiv:1506.00019, 2015	1577	2015
Learning to Diagnose with LSTM Recurrent Neural Networks ZC Lipton, DC Kale, C Elkan, R Wetzell International Conference on Learning Representations (ICLR), 2015	819	2015
Born again neural networks T Furlanello, ZC Lipton, M Tschannen, L Itti, A Anandkumar International Conference on Machine Learning (ICML), 2018	338	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	273	2018
Modeling Missing Data in Clinical Time Series with RNNs ZC Lipton, DC Kale, R Wetzel Machine Learning for Healthcare (MLHC), 2016	260*	2016
Optimal thresholding of classifiers to maximize F1 measure ZC Lipton, C Elkan, B Naryanaswamy European Conference on Machine Learning (ECML), 225-239, 2014	232	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	200	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	150*	2018
Troubling Trends in Machine Learning Scholarship ZC Lipton, J Steinhardt Communications of the ACM 62 (6), 45-53, 2019	149*	2019
Differential privacy and machine learning: a survey and review Z Ji, ZC Lipton, C Elkan arXiv preprint arXiv:1412.7584, 2014	147	2014
Dive into Deep Learning AJS Aston Zhang, Zachary C. Lipton, Mu Li pre-print, 2020	131*	2020
Detecting and Correcting for Label Shift with Black Box Predictors ZC Lipton, YX Wang, A Smola International Conference on Machine Learning (ICML), 2018	106	2018
Precise Recovery of Latent Vectors from Generative Adversarial Networks ZC Lipton, S Tripathi ICLR Workshop 2017, 2017	106	2017
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	105	2018
Does mitigating ML’s impact disparity require treatment disparity? ZC Lipton, A Chouldechova, J McAuley Advances in Neural Information Processing Systems (NeurIPS), 2018	92*	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	91	2016
Learning the Difference that Makes a Difference with Counterfactually-Augmented Data D Kaushik, E Hovy, ZC Lipton International Conference on Learning Representations (ICLR), 2020	86	2020
Semantically decomposing the latent spaces of generative adversarial networks C Donahue, A Balsubramani, J McAuley, ZC Lipton International Conference on Learning Representations (ICLR), 2018	82	2018
Learning From Noisy Singly-labeled Data A Khetan, ZC Lipton, A Anandkumar International Conference on Learning Representations (ICLR), 2018	73	2018

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