Active surrogate estimators: An active learning approach to label-efficient model evaluation
Abstract We propose Active Surrogate Estimators (ASEs), a new method for label-efficient
model evaluation. Evaluating model performance is a challenging and important problem …
model evaluation. Evaluating model performance is a challenging and important problem …
[HTML][HTML] Active learning of molecular data for task-specific objectives
Active learning (AL) has shown promise to be a particularly data-efficient machine learning
approach. Yet, its performance depends on the application, and it is not clear when AL …
approach. Yet, its performance depends on the application, and it is not clear when AL …
Policy-Aware Experimentation: Strategic Sampling for Optimized Targeting Policies
With unprecedented access to consumer information, firms are increasingly interested in
designing highly effective data-driven targeting policies based on detailed consumer data …
designing highly effective data-driven targeting policies based on detailed consumer data …
Amortized Bayesian Experimental Design for Decision-Making
Many critical decisions, such as personalized medical diagnoses and product pricing, are
made based on insights gained from designing, observing, and analyzing a series of …
made based on insights gained from designing, observing, and analyzing a series of …
Active learning-based multistage sequential decision-making model with application on common bile duct stone evaluation
Multistage sequential decision-making occurs in many real-world applications such as
healthcare diagnosis and treatment. One concrete example is when the doctors need to …
healthcare diagnosis and treatment. One concrete example is when the doctors need to …