Zachary C. Lipton

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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
Kamyar AzizzadenesheliAssistant Professor at Purdue UniversityVerified email at purdue.edu
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
Tommaso FurlanelloPhd Candidate, University of Southern CaliforniaVerified email at usc.edu
Divyansh KaushikPhD Student at Language Technologies Institute, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Balakrishnan NarayanaswamyAWS AI, AmazonVerified email at amazon.com
Bhuwan DhingraAssistant Professor, Computer Science, Duke UniversityVerified email at cs.duke.edu
Jean KossaifiSenior Research Scientist at NVIDIAVerified email at nvidia.com
Aran KhannaReserved.ai, Harvard University, Amazon AIVerified email at arankhanna.com
Alex SmolaAmazon Web ServicesVerified email at smola.org
Faisal Ahmed, PhDSUNY Stony BrookVerified email at cs.stonybrook.edu
Laurent IttiProfessor of Computer Science, University of Southern CaliforniaVerified email at usc.edu
Danish PruthiCarnegie Mellon UniversityVerified email at cs.cmu.edu
Subarna TripathiIntel LabsVerified email at intel.com

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	2151*	2016
A critical review of recurrent neural networks for sequence learning ZC Lipton, J Berkowitz, C Elkan arXiv preprint arXiv:1506.00019, 2015	1716	2015
Learning to Diagnose with LSTM Recurrent Neural Networks ZC Lipton, DC Kale, C Elkan, R Wetzell International Conference on Learning Representations (ICLR), 2015	882	2015
Born again neural networks T Furlanello, ZC Lipton, M Tschannen, L Itti, A Anandkumar International Conference on Machine Learning (ICML), 2018	402	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	304	2018
Modeling Missing Data in Clinical Time Series with RNNs ZC Lipton, DC Kale, R Wetzel Machine Learning for Healthcare (MLHC), 2016	284*	2016
Optimal thresholding of classifiers to maximize F1 measure ZC Lipton, C Elkan, B Naryanaswamy European Conference on Machine Learning (ECML), 225-239, 2014	265	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	230	2018
Dive into Deep Learning AJS Aston Zhang, Zachary C. Lipton, Mu Li pre-print, 2020	175*	2020
Troubling Trends in Machine Learning Scholarship ZC Lipton, J Steinhardt Communications of the ACM 62 (6), 45-53, 2019	163*	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	163*	2018
Differential privacy and machine learning: a survey and review Z Ji, ZC Lipton, C Elkan arXiv preprint arXiv:1412.7584, 2014	158	2014
Detecting and Correcting for Label Shift with Black Box Predictors ZC Lipton, YX Wang, A Smola International Conference on Machine Learning (ICML), 2018	130	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	126	2020
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	123	2018
Precise Recovery of Latent Vectors from Generative Adversarial Networks ZC Lipton, S Tripathi ICLR Workshop 2017, 2017	123	2017
Does mitigating ML’s impact disparity require treatment disparity? ZC Lipton, A Chouldechova, J McAuley Advances in Neural Information Processing Systems (NeurIPS), 2018	103*	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	102	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	91	2018
Learning From Noisy Singly-labeled Data A Khetan, ZC Lipton, A Anandkumar International Conference on Learning Representations (ICLR), 2018	84	2018

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