| The Mythos of Model Interpretability ZC Lipton Communications of the ACM (CACM), 2016 | 6414* | 2016 |
| A Critical Review of Recurrent Neural Networks for Sequence Learning ZC Lipton arXiv Preprint, CoRR, abs/1506.00019, 2015 | 3786* | 2015 |
| Dive into Deep Learning AJS Aston Zhang, Zachary C. Lipton, Mu Li pre-print, 2020 | 1814* | 2020 |
| Learning to Diagnose with LSTM Recurrent Neural Networks ZC Lipton, DC Kale, C Elkan, R Wetzell International Conference on Learning Representations (ICLR), 2015 | 1535 | 2015 |
| Born again neural networks T Furlanello, ZC Lipton, M Tschannen, L Itti, A Anandkumar International Conference on Machine Learning (ICML), 2018 | 1262 | 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 | 998 | 2019 |
| Optimal thresholding of classifiers to maximize F1 measure ZC Lipton, C Elkan, B Naryanaswamy European Conference on Machine Learning (ECML), 225-239, 2014 | 765 | 2014 |
| Stochastic activation pruning for robust adversarial defense GS Dhillon, K Azizzadenesheli, ZC Lipton, J Bernstein, J Kossaifi, ... International Conference on Learning Representations (ICLR), 2018 | 727 | 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 | 647 | 2020 |
| Detecting and Correcting for Label Shift with Black Box Predictors ZC Lipton, YX Wang, A Smola International Conference on Machine Learning (ICML), 2018 | 644 | 2018 |
| Modeling Missing Data in Clinical Time Series with RNNs ZC Lipton, DC Kale, R Wetzel Machine Learning for Healthcare (MLHC), 2016 | 588* | 2016 |
| Deep Active Learning for Named Entity Recognition Y Shen, H Yun, ZC Lipton, Y Kronrod, A Anandkumar International Conference on Learning Representations (ICLR), 2018 | 587 | 2018 |
| What is the Effect of Importance Weighting in Deep Learning? J Byrd, Z Lipton International Conference on Machine Learning (ICML), 872-881, 2019 | 585 | 2019 |
| 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 | 429 | 2018 |
| Differential privacy and machine learning: a survey and review Z Ji, ZC Lipton, C Elkan arXiv preprint arXiv:1412.7584, 2014 | 402 | 2014 |
| Troubling Trends in Machine Learning Scholarship ZC Lipton, J Steinhardt Communications of the ACM 62 (6), 45-53, 2019 | 400* | 2019 |
| Combating Adversarial Misspellings with Robust Word Recognition D Pruthi, B Dhingra, ZC Lipton Association for Computational Linguistics (ACL), 2019 | 362 | 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 | 341 | 2016 |
| 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 | 281* | 2018 |
| Does mitigating ML’s impact disparity require treatment disparity? ZC Lipton, A Chouldechova, J McAuley Advances in Neural Information Processing Systems (NeurIPS), 2018 | 280 | 2018 |
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