| Algorithms and theory for multiple-source adaptation J Hoffman, M Mohri, N Zhang Advances in neural information processing systems 31, 2018 | 251 | 2018 |
| Region-based active learning C Cortes, G DeSalvo, C Gentile, M Mohri, N Zhang The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 35 | 2019 |
| Active learning with disagreement graphs C Cortes, G DeSalvo, M Mohri, N Zhang, C Gentile International Conference on Machine Learning, 1379-1387, 2019 | 26 | 2019 |
| Adaptive region-based active learning C Cortes, G DeSalvo, C Gentile, M Mohri, N Zhang International Conference on Machine Learning, 2144-2153, 2020 | 15 | 2020 |
| Online learning with dependent stochastic feedback graphs C Cortes, G DeSalvo, C Gentile, M Mohri, N Zhang International Conference on Machine Learning, 2154-2163, 2020 | 13 | 2020 |
| A discriminative technique for multiple-source adaptation C Cortes, M Mohri, AT Suresh, N Zhang International Conference on Machine Learning, 2132-2143, 2021 | 12 | 2021 |
| Multiple-source adaptation theory and algorithms N Zhang, M Mohri, J Hoffman Annals of Mathematics and Artificial Intelligence 89, 237-270, 2021 | 12 | 2021 |
| Multiple-source adaptation for regression problems J Hoffman, M Mohri, N Zhang arXiv preprint arXiv:1711.05037, 2017 | 10 | 2017 |
| Learning GANs and ensembles using discrepancy B Adlam, C Cortes, M Mohri, N Zhang Advances in Neural Information Processing Systems 32, 2019 | 9 | 2019 |
| Multiple-source adaptation with domain classifiers C Cortes, M Mohri, AT Suresh, N Zhang arXiv preprint arXiv:2008.11036, 2020 | 5 | 2020 |
| Multiple-source adaptation theory and algorithms–addendum J Hoffman, M Mohri, N Zhang Annals of Mathematics and Artificial Intelligence 90 (6), 569-572, 2022 | 3 | 2022 |
| Joint latent class trees: A tree-based approach to modeling time-to-event and longitudinal data N Zhang, JS Simonoff Statistical Methods in Medical Research 31 (4), 719-752, 2022 | 3 | 2022 |
| The potential for nonparametric joint latent class modeling of longitudinal and time-to-event data N Zhang, JS Simonoff Nonparametric Statistics: 4th ISNPS, Salerno, Italy, June 2018 4, 525-533, 2020 | 3 | 2020 |
| Adaptive Region-Based Active Learning C Gentile, C Cortes, G DeSalvo, M Mohri, N Zhang | | 2020 |
| Online Learning with Dependent Stochastic Feedback Graphs C Gentile, C Cortes, G DeSalvo, M Mohri, N Zhang | | 2020 |
| Fitting a deeply nested hierarchical model to a large book review dataset using a moment-based estimator N Zhang, K Schmaus, PO Perry | | 2019 |
| Active Learning with Disagreement Graphs C Gentile, C Cortes, G DeSalvo, M Mohri, N Zhang | | 2019 |
| Essays in Applied Statistics and Machine Learning N Zhang New York University, 2019 | | 2019 |
| Region-based Active Learning C Gentile, C Cortes, G DeSalvo, M Mohri, N Zhang | | 2019 |
| Joint latent class trees: A Tree-Based Approach to Joint Modeling of Time-to-event and Longitudinal Data N Zhang, JS Simonoff | | |
