In off-policy deep reinforcement learning, it is usually hard to collect sufficient successful
experiences with sparse rewards to learn from. Hindsight experience replay (HER) enables …

We study a new method (``Diverse Ensemble Evolution (DivE $^ 2$)'') to train an ensemble
of machine learning models that assigns data to models at each training epoch based on …

Deriving true clinical utility from models trained on multiple hospitals' data is a key challenge
in the adoption of Federated Learning (FL) systems in support of clinical collaborations …

Early prediction of the potential for neurological recovery after resuscitation from cardiac
arrest is difficult but important. Currently, no clinical finding or combination of findings are …

Objectives To support adjudication of alerts from monitoring critically ill patients, and to
reduce false alert rates while maintaining sensitivity of detection. Methods Our noninvasive …

The importance of interpretability of machine learning models has been increasing due to
emerging enterprise predictive analytics. Piecewise linear models have been actively …

Regulations can often limit stakeholders' modeling capabilities by preventing data sharing.
For example, in order to protect patient privacy, clinical centers may be unable to share their …

Data driven decision support systems often benefit from human participation to validate
outcomes produced by automated procedures. Perceived utility hinges on the system's …

We introduce an active learning framework designed to train classification models which use
informative projections. Our approach works with the obtained low-dimensional models in …

This paper proposes a distributed factorized asymptotic Bayesian (FAB) inference of
learning piece-wise sparse linear models on distributed memory architectures. The …