AI is radically changing society and the main driver behind new AI methods and systems is machine learning. Machine learning focuses on finding solutions for, or patterns in, new data by learning from relevant existing data. Thus, machine learning algorithms are often applied to large datasets and then they more or less autonomously find good solutions by finding relevant information or patterns hidden in the data.

The search for nearest neighbors is essential but often inefficient in applications like clustering and classification, especially with high-dimensional big data. Traditional methods become impractical due to the curse of dimensionality, making approximate nearest neighbor (ANN) search methods a faster alternative despite their inexact results. ANN methods significantly enhance processing speed, impacting algorithmic decision-making processes by introducing trade-offs in accuracy, bias, and trustworthiness, which must be carefully considered for different use cases.

Our focus is on improving optimization algorithms in online decision-making. Using techniques from online algorithms for solving optimization problems, we can provide worst-case guarantees, but normal (averagecase) behavior may not be satisfactory. Using techniques from machine learning, we can often provide good behavior in practice, but guarantees are lacking, and in particular missing for situations not captured by the training data. We aim at combining the best features from these two areas.