Yuyang (Bernie) Wang
Yuyang (Bernie) Wang
Principal Scientist, AWS AI
Dirección de correo verificada de mit.edu - Página principal
Citado por
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Deep state space models for time series forecasting
SS Rangapuram, MW Seeger, J Gasthaus, L Stella, Y Wang, ...
Advances in neural information processing systems 31, 7785-7794, 2018
Generalization Bounds for Online Learning Algorithms with Pairwise Loss Functions
Y Wang, R Khardon, D Pechyony, R Jones
Annual Conference on Learning Theory, 2012
Probabilistic Demand Forecasting at Scale
YW Joos-Hendrik Boese, Valentin Flunkert, Jan Gasthaus, Tim Januschowski ...
Proceedings of the VLDB Endowment 10 (12), 1694-1705, 2017
Random Matrix Theory and Its Innovative Applications
A Edelman, Y Wang
Advances in Applied Mathematics, Modeling, and Computational Science, 91-116, 2012
Deep Factors for Forecasting
Y Wang, A Smola, DC Maddix, J Gasthaus, D Foster, T Januschowski
ICML 2019, arXiv preprint arXiv:1905.12417, 2019
GluonTS: Probabilistic and Neural Time Series Modeling in Python
A Alexandrov, K Benidis, M Bohlke-Schneider, V Flunkert, J Gasthaus, ...
Journal of Machine Learning Research 21 (116), 1-6, 2020
Probabilistic forecasting with spline quantile function rnns
J Gasthaus, K Benidis, Y Wang, SS Rangapuram, D Salinas, V Flunkert, ...
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
Criteria for classifying forecasting methods
T Januschowski, J Gasthaus, Y Wang, D Salinas, V Flunkert, ...
International Journal of Forecasting 36 (1), 167-177, 2020
Nonparametric bayesian estimation of periodic light curves
Y Wang, R Khardon, P Protopapas
The Astrophysical Journal 756 (1), 67, 2012
Sparse Variational Inference for Generalized Gaussian Process Models
R Sheth, Y Wang, R Khardon
International Conference on Machine Learning, 2015
Forecasting big time series: old and new
C Faloutsos, J Gasthaus, T Januschowski, Y Wang
Proceedings of the VLDB Endowment 11 (12), 2102-2105, 2018
Elastic Machine Learning Algorithms in Amazon SageMaker
E Liberty, Z Karnin, B Xiang, L Rouesnel, B Coskun, R Nallapati, ...
SIGMOD, 2020
Sparse Gaussian processes for multi-task learning
Y Wang, R Khardon
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2012
Neural forecasting: Introduction and literature overview
K Benidis, SS Rangapuram, V Flunkert, B Wang, D Maddix, C Turkmen, ...
arXiv preprint arXiv:2004.10240, 2020
Shift-invariant grouped multi-task learning for Gaussian processes
Y Wang, R Khardon, P Protopapas
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2010
Random matrix theory, numerical computation and applications
A Edelman, BD Sutton, Y Wang
Modern Aspects of Random Matrix Theory 72, 53, 2014
Approximate bayesian inference in linear state space models for intermittent demand forecasting at scale
M Seeger, S Rangapuram, Y Wang, D Salinas, J Gasthaus, ...
arXiv preprint arXiv:1709.07638, 2017
Deep Learning for Forecasting: Current Trends and Challenges.
T Januschowski, J Gasthaus, Y Wang, SS Rangapuram, L Callot
Foresight: The International Journal of Applied Forecasting, 2018
Forecasting big time series: Theory and practice
C Faloutsos, V Flunkert, J Gasthaus, T Januschowski, Y Wang
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019
FastPoint: Scalable Deep Point Processes
AC Türkmen, Y Wang, AJ Smola
ECML, 2019
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