Smart “Predict, then Optimize”

Smart "Predict, then Optimize"

Many real-world analytics problems involve two significant challenges: prediction and optimization. Due to the typically complex nature of each challenge, the standard paradigm is to predict, then optimize. By and large, machine learning tools are intended to minimize prediction error and do not account for how the predictions will be used in a downstream optimization problem. In contrast, we p...

Improved Twitter Sentiment Prediction through Cluster-then-Predict Model

Over the past decade humans have experienced exponential growth in the use of online resources, in particular social media and microblogging websites such as Facebook, Twitter, YouTube and also mobile applications such as WhatsApp, Line, etc. Many companies have identified these resources as a rich mine of marketing knowledge. This knowledge provides valuable feedback which allows them to furth...

Capturing Multivariate Spatial Dependence: Model, Estimate, and then Predict

Smart alerts: development of software to optimize data monitoring.

BACKGROUND AND OBJECTIVES Monitoring is useful for vital follow-ups and prevention, diagnosis, and treatment of several events in anesthesia. Although alarms can be useful in monitoring they can cause dangerous user's desensitization. The objective of this study was to describe the development of specific software to integrate intraoperative monitoring parameters generating "smart alerts" that ...

Randomize-then-optimize for Sampling and Uncertainty Quantification in Electrical Impedance Tomography

In a typical inverse problem, a spatially distributed parameter in a physical model is estimated from indirect measurements of model output. Since measurements are stochastic in nature, so is any parameter estimate. Moreover, in the Bayesian setting, the choice of regularization corresponds to the definition of the prior probability density function, which in turn is an uncertainty model for th...