Zachary C. Lipton

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Aston ZhangLlama team, MetaDirección de correo verificada de meta.com
Anima AnandkumarCalifornia Institute of Technology and NVIDIADirección de correo verificada de caltech.edu
Charles ElkanProfessor of Computer Science, University of California, San DiegoDirección de correo verificada de ucsd.edu
John BerkowitzAppleDirección de correo verificada de apple.com
Alex SmolaBoson AIDirección de correo verificada de smola.org
Kamyar AzizzadenesheliNvidiaDirección de correo verificada de nvidia.com
Saurabh GargMistral AI | Past: CMU ML PhDDirección de correo verificada de andrew.cmu.edu
Julian McAuleyProfessor, UC San DiegoDirección de correo verificada de eng.ucsd.edu
Divyansh KaushikVice President, Beacon Global StrategiesDirección de correo verificada de bgsdc.com
Dave KaleData Science Manager, LiteratiDirección de correo verificada de literati.com
Sivaraman BalakrishnanCarnegie Mellon UniversityDirección de correo verificada de stat.cmu.edu
Mu LiAmazonDirección de correo verificada de amazon.com
Bhuwan DhingraAssistant Professor, Computer Science, Duke UniversityDirección de correo verificada de cs.duke.edu
Jianfeng GaoMicrosoft Research, RedmondDirección de correo verificada de microsoft.com
Lihong Li (李力鸿)AmazonDirección de correo verificada de amazon.com
Zico KolterCarnegie Mellon UniversityDirección de correo verificada de cs.cmu.edu
Danish PruthiIndian Institute of Science, BangaloreDirección de correo verificada de iisc.ac.in
Eric XingPresident at Mohamed bin Zayed University of AI, Professor of Computer Science, Carnegie Mellon UDirección de correo verificada de cs.cmu.edu
Liu LeqiUT AustinDirección de correo verificada de princeton.edu
Tommaso FurlanelloLeading Science at HK3 LabDirección de correo verificada de usc.edu

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Zachary C. Lipton

Raj Reddy Associate Professor of Machine Learning @ Carnegie Mellon University; CTO + CSO @ Abridge

Dirección de correo verificada de cmu.edu - Página principal

Machine Learning Healthcare Technology & Society NLP Robustness & Adaptivity


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
The Mythos of Model Interpretability ZC Lipton Communications of the ACM (CACM), 2016	6515*	2016
A critical review of recurrent neural networks for sequence learning ZC Lipton, J Berkowitz, C Elkan arXiv preprint arXiv:1506.00019, 2015	3816	2015
Dive into Deep Learning AJS Aston Zhang, Zachary C. Lipton, Mu Li pre-print, 2020	1818*	2020
Learning to Diagnose with LSTM Recurrent Neural Networks ZC Lipton, DC Kale, C Elkan, R Wetzell International Conference on Learning Representations (ICLR), 2015	1544	2015
Born again neural networks T Furlanello, ZC Lipton, M Tschannen, L Itti, A Anandkumar International Conference on Machine Learning (ICML), 2018	1265	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	1041	2019
Stochastic activation pruning for robust adversarial defense GS Dhillon, K Azizzadenesheli, ZC Lipton, J Bernstein, J Kossaifi, ... International Conference on Learning Representations (ICLR), 2018	716	2018
Detecting and Correcting for Label Shift with Black Box Predictors ZC Lipton, YX Wang, A Smola International Conference on Machine Learning (ICML), 2018	659	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	643	2020
What is the Effect of Importance Weighting in Deep Learning? J Byrd, Z Lipton International Conference on Machine Learning (ICML), 872-881, 2019	600	2019
Deep Active Learning for Named Entity Recognition Y Shen, H Yun, ZC Lipton, Y Kronrod, A Anandkumar International Conference on Learning Representations (ICLR), 2018	594	2018
Modeling Missing Data in Clinical Time Series with RNNs ZC Lipton, DC Kale, R Wetzel Machine Learning for Healthcare (MLHC), 2016	591*	2016
Optimal thresholding of classifiers to maximize F1 measure ZC Lipton, C Elkan, B Naryanaswamy European Conference on Machine Learning (ECML), 225-239, 2014	567	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	436	2018
Differential privacy and machine learning: a survey and review Z Ji, ZC Lipton, C Elkan arXiv preprint arXiv:1412.7584, 2014	408	2014
Troubling Trends in Machine Learning Scholarship ZC Lipton, J Steinhardt Communications of the ACM 62 (6), 45-53, 2019	404*	2019
Combating Adversarial Misspellings with Robust Word Recognition D Pruthi, B Dhingra, ZC Lipton Association for Computational Linguistics (ACL), 2019	361	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	343	2016
Does mitigating ML’s impact disparity require treatment disparity? ZC Lipton, A Chouldechova, J McAuley Advances in Neural Information Processing Systems (NeurIPS), 2018	282	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	282*	2018

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