Zachary C. Lipton

Cited by

	All	Since 2015
Citations	6771	6748
h-index	31	31
i10-index	46	46

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20152016201720182019202026 142 527 1352 2364 2286

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 AzizzadenesheliPostdoctoral fellow at CaltechVerified email at caltech.edu
Tommaso FurlanelloPhd Candidate, University of Southern CaliforniaVerified email at usc.edu
Jean KossaifiSenior Research Scientist at NVIDIAVerified email at nvidia.com
Aran KhannaHarvard University, Amazon AIVerified email at arankhanna.com
Mu LiAmazonVerified email at amazon.com
Divyansh KaushikPhD Student at Language Technologies Institute, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Alex SmolaAmazon Web ServicesVerified email at smola.org
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
Bhuwan DhingraLanguage Technologies Institute, Carnegie Mellon UniversityVerified email at andrew.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	1599	2016
A critical review of recurrent neural networks for sequence learning ZC Lipton, J Berkowitz, C Elkan arXiv preprint arXiv:1506.00019, 2015	1350	2015
Learning to Diagnose with LSTM Recurrent Neural Networks ZC Lipton, DC Kale, C Elkan, R Wetzell International Conference on Learning Representations (ICLR), 2015	730	2015
Born again neural networks T Furlanello, ZC Lipton, M Tschannen, L Itti, A Anandkumar International Conference on Machine Learning (ICML), 2018	262	2018
Modeling Missing Data in Clinical Time Series with RNNs ZC Lipton, DC Kale, R Wetzel Machine Learning for Healthcare (MLHC), 2016	231*	2016
Stochastic activation pruning for robust adversarial defense GS Dhillon, K Azizzadenesheli, ZC Lipton, J Bernstein, J Kossaifi, ... International Conference on Learning Representations (ICLR), 2018	230	2018
Optimal thresholding of classifiers to maximize F1 measure ZC Lipton, C Elkan, B Naryanaswamy European Conference on Machine Learning (ECML), 225-239, 2014	202	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	156	2018
Differential privacy and machine learning: a survey and review Z Ji, ZC Lipton, C Elkan arXiv preprint arXiv:1412.7584, 2014	136	2014
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	132*	2018
Troubling Trends in Machine Learning Scholarship ZC Lipton, J Steinhardt Communications of the ACM 62 (6), 45-53, 2019	128	2019
Precise Recovery of Latent Vectors from Generative Adversarial Networks ZC Lipton, S Tripathi ICLR Workshop 2017, 2017	95	2017
Dive into deep learning A Zhang, ZC Lipton, M Li, AJ Smola Unpublished Draft. Retrieved 19, 2019, 2019	88*	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	88	2018
Detecting and Correcting for Label Shift with Black Box Predictors ZC Lipton, YX Wang, A Smola International Conference on Machine Learning (ICML), 2018	80	2018
Does mitigating ML’s impact disparity require treatment disparity? ZC Lipton, A Chouldechova, J McAuley Advances in Neural Information Processing Systems (NeurIPS), 2018	77*	2018
Semantically decomposing the latent spaces of generative adversarial networks C Donahue, A Balsubramani, J McAuley, ZC Lipton International Conference on Learning Representations (ICLR), 2018	77	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	77	2016
Learning From Noisy Singly-labeled Data A Khetan, ZC Lipton, A Anandkumar International Conference on Learning Representations (ICLR), 2018	67	2018
Generative concatenative nets jointly learn to write and classify reviews ZC Lipton, S Vikram, J McAuley arXiv preprint arXiv:1511.03683, 2015	66*	2015

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