Zachary C. Lipton
Zachary C. Lipton
Assistant Professor of Operations Research & Machine Learning at CMU
Verified email at cmu.edu - Homepage
Title
Cited by
Cited by
Year
The Mythos of Model Interpretability
ZC Lipton
Communications of the ACM (CACM) [Prev. ICML Workshop on Human …, 2016
17802016
A critical review of recurrent neural networks for sequence learning
ZC Lipton, J Berkowitz, C Elkan
arXiv preprint arXiv:1506.00019, 2015
14792015
Learning to Diagnose with LSTM Recurrent Neural Networks
ZC Lipton, DC Kale, C Elkan, R Wetzell
International Conference on Learning Representations (ICLR), 2015
7862015
Born again neural networks
T Furlanello, ZC Lipton, M Tschannen, L Itti, A Anandkumar
International Conference on Machine Learning (ICML), 2018
3192018
Stochastic activation pruning for robust adversarial defense
GS Dhillon, K Azizzadenesheli, ZC Lipton, J Bernstein, J Kossaifi, ...
International Conference on Learning Representations (ICLR), 2018
2582018
Modeling Missing Data in Clinical Time Series with RNNs
ZC Lipton, DC Kale, R Wetzel
Machine Learning for Healthcare (MLHC), 2016
253*2016
Optimal thresholding of classifiers to maximize F1 measure
ZC Lipton, C Elkan, B Naryanaswamy
European Conference on Machine Learning (ECML), 225-239, 2014
225*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
1902018
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
149*2018
Troubling Trends in Machine Learning Scholarship
ZC Lipton, J Steinhardt
Communications of the ACM 62 (6), 45-53, 2019
1462019
Differential privacy and machine learning: a survey and review
Z Ji, ZC Lipton, C Elkan
arXiv preprint arXiv:1412.7584, 2014
1462014
Dive into deep learning
A Zhang, ZC Lipton, M Li, AJ Smola
Unpublished Draft. Retrieved 19, 2019, 2019
115*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
1032018
Precise Recovery of Latent Vectors from Generative Adversarial Networks
ZC Lipton, S Tripathi
ICLR Workshop 2017, 2017
1022017
Detecting and Correcting for Label Shift with Black Box Predictors
ZC Lipton, YX Wang, A Smola
International Conference on Machine Learning (ICML), 2018
982018
Does mitigating ML’s impact disparity require treatment disparity?
ZC Lipton, A Chouldechova, J McAuley
Advances in Neural Information Processing Systems (NeurIPS), 2018
91*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
872016
Semantically decomposing the latent spaces of generative adversarial networks
C Donahue, A Balsubramani, J McAuley, ZC Lipton
International Conference on Learning Representations (ICLR), 2018
792018
Learning the Difference that Makes a Difference with Counterfactually-Augmented Data
D Kaushik, E Hovy, ZC Lipton
International Conference on Learning Representations (ICLR), 2020
772020
Learning From Noisy Singly-labeled Data
A Khetan, ZC Lipton, A Anandkumar
International Conference on Learning Representations (ICLR), 2018
712018
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Articles 1–20