Specifically, it first learns the conditional distribution of input features of one domain given input features of another domain, and then it estimates the domain-invariant relationship by predicting labels with the learned conditional distribution.
Analyzing observational data from multiple sources can be useful for increasing statistical power to detect a treatment effect; however, practical constraints such as privacy considerations may restrict individual-level information sharing across data sets.
no code implementations • 21 Apr 2020 • Allison Koenecke, Michael Powell, Ruoxuan Xiong, Zhu Shen, Nicole Fischer, Sakibul Huq, Adham M. Khalafallah, Marco Trevisan, Pär Sparen, Juan J Carrero, Akihiko Nishimura, Brian Caffo, Elizabeth A. Stuart, Renyuan Bai, Verena Staedtke, David L. Thomas, Nickolas Papadopoulos, Kenneth W. Kinzler, Bert Vogelstein, Shibin Zhou, Chetan Bettegowda, Maximilian F. Konig, Brett Mensh, Joshua T. Vogelstein, Susan Athey
Here, we conducted retrospective analyses in two cohorts of patients with acute respiratory distress (ARD, n=18, 547) and three cohorts with pneumonia (n=400, 907).
Then, these weights are used in the weighted regression to improve the accuracy of estimation on the effect of each variable, thus help to improve the stability of prediction across unknown test data.
In the general setting where outcomes depend on latent covariates, we show that historical data can be utilized in designing experiments.
We present a machine learning approach to the solution of chance constrained optimizations in the context of voltage regulation problems in power system operation.
In this paper, we propose a novel Deep Global Balancing Regression (DGBR) algorithm to jointly optimize a deep auto-encoder model for feature selection and a global balancing model for stable prediction across unknown environments.
We have applied a Long Short-Term Memory neural network to model S&P 500 volatility, incorporating Google domestic trends as indicators of the public mood and macroeconomic factors.