Special feature: computational statistics and machine learning

Machine Learning in Applied Statistics

This special issue of Model Assisted Statistics and Applications (MASA) focused on knowing how current machine learning methods can be applied to diverse statistics areas. We have ten papers about the recent machine learning developments and applications, including survey sampling, biostatistics, bioinformatics, genetics, time series analysis, and technology forecasting. The issue starts with a...

Proximal Algorithms in Statistics and Machine Learning

In this paper we develop proximal methods for statistical learning. Proximal point algorithms are useful in statistics and machine learning for obtaining optimization solutions for composite functions. Our approach exploits closedform solutions of proximal operators and envelope representations based on the Moreau, Forward-Backward, Douglas-Rachford and Half-Quadratic envelopes. Envelope repres...

Algorithmic Statistics, Prediction and Machine Learning

Algorithmic statistics considers the following problem: given a binary string x (e.g., some experimental data), find a “good” explanation of this data. It uses algorithmic information theory to define formally what is a good explanation. In this paper we extend this framework in two directions. First, the explanations are not only interesting in themselves but also used for prediction: we want ...

Statistics , Data Mining and Machine Learning

Machine Learning and Computational Statistics Homework 4: Kernel Methods and Lagrangian Duality

Due: Monday, March 27, 2017, at 10pm (Submit via Gradescope) Instructions: Your answers to the questions below, including plots and mathematical work, should be submitted as a single PDF file. It’s preferred that you write your answers using software that typesets mathematics (e.g. LATEX, LYX, or MathJax via iPython), though if you need to you may scan handwritten work. You may find the minted ...