Diabetes is one of the most common diseases worldwide. Many Machine Learning (ML)
techniques have been utilized in predicting diabetes in the last couple of years. The …

A substantial school in the philosophy of science identifies Bayesian inference with inductive
inference and even rationality as such, and seems to be strengthened by the rise and …

We consider stochastic programs where the distribution of the uncertain parameters is only
observable through a finite training dataset. Using the Wasserstein metric, we construct a …

At publication, The Control Handbook immediately became the definitive resource that
engineers working with modern control systems required. Among its many accolades, that …

The goal of learning theory is to approximate a function from sample values. To attain this
goal learning theory draws on a variety of diverse subjects, specifically statistics …

The presence of uncertainty in a system description has always been a critical issue in
control. The main objective of Randomized Algorithms for Analysis and Control of Uncertain …

This paper presents a survey of the most significant results on robust control theory. In
particular, we study the modeling of uncertain systems, robust stability analysis for systems …

Delineating the tremendous growth in this area, the Handbook of Approximation Algorithms
and Metaheuristics covers fundamental, theoretical topics as well as advanced, practical …

We propose an approximation method for analyzing Ericson and Pakes (1995)‐style
dynamic models of imperfect competition. We define a new equilibrium concept that we call …

These are the notes for 36-402, Advanced Data Analysis, at Carnegie Mellon. If you are not
enrolled in the class, you should know that it's the methodological capstone of the core …