Jack Goetz
Jack Goetz
Verified email at umich.edu
Title
Cited by
Cited by
Year
Online multiclass boosting
YH Jung, J Goetz, A Tewari
arXiv preprint arXiv:1702.07305, 2017
252017
Active federated learning
J Goetz, K Malik, D Bui, S Moon, H Liu, A Kumar
arXiv preprint arXiv:1909.12641, 2019
242019
What does the machine learn? Knowledge representations of chemical reactivity
JA Kammeraad, J Goetz, EA Walker, A Tewari, PM Zimmerman
Journal of chemical information and modeling 60 (3), 1290-1301, 2020
142020
Federated user representation learning
D Bui, K Malik, J Goetz, H Liu, S Moon, A Kumar, KG Shin
arXiv preprint arXiv:1909.12535, 2019
122019
Active learning for non-parametric regression using purely random trees.
J Goetz, A Tewari, P Zimmerman
Advances in Neural Information Processing Systems 31, 2018
112018
Federated learning via synthetic data
J Goetz, A Tewari
arXiv preprint arXiv:2008.04489, 2020
42020
Mining events with declassified diplomatic documents
Y Gao, J Goetz, M Connelly, R Mazumder
The Annals of Applied Statistics 14 (4), 1699-1723, 2020
22020
Learning From Others Without Sacrificing Privacy: Simulation Comparing Centralized and Federated Machine Learning on Mobile Health Data
JC Liu, J Goetz, S Sen, A Tewari
JMIR mHealth and uHealth 9 (3), e23728, 2021
12021
Not All are Made Equal: Consistency of Weighted Averaging Estimators Under Active Learning
J Goetz, A Tewari
arXiv preprint arXiv:1910.05321, 2019
12019
Understanding the limits of machines in learning chemical reactivity
J Kammeraad, J Goetz, E Walker, A Tewari, P Zimmerman
ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY 257, 2019
2019
Active Federated Learning Download PDF Open Website
J Goetz, K Malik, D Bui, S Moon, H Liu, A Kumar
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