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Ningshan Zhang
Ningshan Zhang
Unknown affiliation
Verified email at stern.nyu.edu
Title
Cited by
Cited by
Year
Algorithms and theory for multiple-source adaptation
J Hoffman, M Mohri, N Zhang
Advances in Neural Information Processing Systems 31, 2018
1272018
Region-based active learning
C Cortes, G DeSalvo, C Gentile, M Mohri, N Zhang
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
182019
Active learning with disagreement graphs
C Cortes, G DeSalvo, M Mohri, N Zhang, C Gentile
International Conference on Machine Learning, 1379-1387, 2019
142019
Learning GANs and ensembles using discrepancy
B Adlam, C Cortes, M Mohri, N Zhang
Advances in Neural Information Processing Systems 32, 2019
92019
Multiple-source adaptation for regression problems
J Hoffman, M Mohri, N Zhang
arXiv preprint arXiv:1711.05037, 2017
92017
Adaptive region-based active learning
C Cortes, G DeSalvo, C Gentile, M Mohri, N Zhang
International Conference on Machine Learning, 2144-2153, 2020
72020
Multiple-source adaptation with domain classifiers
C Cortes, M Mohri, AT Suresh, N Zhang
arXiv preprint arXiv:2008.11036, 2020
42020
Multiple-source adaptation theory and algorithms
N Zhang, M Mohri, J Hoffman
Annals of Mathematics and Artificial Intelligence 89 (3), 237-270, 2021
32021
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
32020
A discriminative technique for multiple-source adaptation
C Cortes, M Mohri, AT Suresh, N Zhang
International Conference on Machine Learning, 2132-2143, 2021
22021
The potential for nonparametric joint latent class modeling of longitudinal and time-to-event data
N Zhang, JS Simonoff
Conference of the International Society for Non-Parametric Statistics, 525-533, 2018
22018
Multiple-source adaptation theory and algorithms–addendum
J Hoffman, M Mohri, N Zhang
Annals of Mathematics and Artificial Intelligence, 1-4, 2022
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
2022
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
The Annals of Applied Statistics 13 (4), 2260-2288, 2019
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
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