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Found 8 papers, 1 papers with code
Learning Domain-Invariant Relationship with Instrumental Variable for Domain Generalization
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.
Federated Causal Inference in Heterogeneous Observational Data
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.
Alpha-1 adrenergic receptor antagonists to prevent hyperinflammation and death from lower respiratory tract infection
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).
Stable Prediction with Model Misspecification and Agnostic Distribution Shift
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.
Optimal Experimental Design for Staggered Rollouts
In the general setting where outcomes depend on latent covariates, we show that historical data can be utilized in designing experiments.
A tractable ellipsoidal approximation for voltage regulation problems
We present a machine learning approach to the solution of chance constrained optimizations in the context of voltage regulation problems in power system operation.
Stable Prediction across Unknown Environments
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.
Deep Learning Stock Volatility with Google Domestic Trends
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.
Computational Finance