Bryan Wilder

TL;DR: It is demonstrated that effective screening depends largely on frequency of testing and speed of reporting and is only marginally improved by high test sensitivity, and should prioritize accessibility, frequency, and sample-to-answer time.

TL;DR: It is concluded that surveillance should prioritize accessibility, frequency, and sample-to-answer time; analytical limits of detection should be secondary.

TL;DR: This paper introduces a differentiable (smoothed) maximum satisfiability (MAXSAT) solver that can be integrated into the loop of larger deep learning systems and demonstrates that by integrating this solver into end-to-end learning systems, this approach shows promise in integrating logical structures within deep learning.

TL;DR: In this article, the authors introduce a general framework for decision-focused learning, where the machine learning model is directly trained in conjunction with the optimization algorithm to produce highquality decisions.

TL;DR: This work focuses on combinatorial optimization problems and introduces a general framework for decision-focused learning, where the machine learning model is directly trained in conjunction with the optimization algorithm to produce highquality decisions, and shows that decisionfocused learning often leads to improved optimization performance compared to traditional methods.