Machine Learning

Crowdsourcing the frontier: Advancing hybrid physics-ML climate simulation via a $50,000 Kaggle competition

Jerry Lin, Zeyuan Hu, Tom Beucler, Katherine Frields, Hannah Christensen, Walter Hannah, Helge Heuer, Peter Ukkonen, Laura a Mansfield, Tian Zheng

Structured exploration of machine learning model complexity for spatio-temporal forecasting of urban flooding

Candace M. Agonafir, Tian Zheng

Probabilistic data assimilation for ensemble distribution projections with generative machine learning: A Lorenz '96 proof-of-concept

Shuolin Li, Tian Zheng, Alban Farchi, Marc Bocquet, Pierre Gentine

Spatiotemporal upscaling of sparse air-sea pCO2 data via physics-informed transfer learning

Siyeon Kim, Juan Nathaniel, Zhewen Hou, Tian Zheng, Pierre Gentine

Toward a Taxonomy of Trust for Probabilistic Machine Learning

Probabilistic machine learning increasingly informs critical decisions in medicine, economics, politics, and beyond. We need evidence …

Tamara Broderick, Andrew Gelman, Rachael Meager, Anna Smith, Tian Zheng

Next Waves in Veridical Network Embedding

Embedding nodes of a large network into a metric (e.g., Euclidean) space has become an area of active research in statistical machine …

Owen Ward, Zhen Huang, Andrew Davison, Tian Zheng

Stabilized Sparse Online Learning for Sparse Data

Stochastic gradient descent (SGD) is commonly used for optimization in large-scale machine learning problems. Langford et al. (2009) …

Yuting Ma, Tian Zheng