Machine learning has evolved into an enabling technology for a wide range of highly
successful applications. The potential for this success to continue and accelerate has placed …

While the training parameters of machine learning models are adapted during the training
phase, the values of the hyperparameters (or meta-parameters) have to be specified before …

Data-driven algorithms are only as good as the data they work with, while datasets,
especially social data, often fail to represent minorities adequately. Representation Bias in …

Nearly all model algorithms used in machine learning use two different sets of parameters:
the training parameters and the meta-parameters (hyperparameters). While the training …

We introduce an improved version of Random Search (RS), used here for hyperparameter
optimization of machine learning algorithms. Unlike the standard RS, which generates for …

Solar photovoltaic power generation accurate prediction is crucial for optimizing the
efficiency and reliability of solar power plants. This research work focuses on predicting …

Background Machine learning models for repeated measurements are limited. Using
topological data analysis (TDA), we present a classifier for repeated measurements which …

We propose the use of persistent homology in a supervised way. We believe homological
persistence is fundamentally not about decomposition theorems but a central role is played …

Clustering has become a very popular machine learning technique for identifying groups of
data points with common features in a set of data points. In several applications, there is a …

We introduce a dynamic early stopping condition for Random Search optimization
algorithms. We test our algorithm for SVM hyperparameter optimization for classification …