Optimal Robust Decision Trees: A dynamic programming approach

Decision trees are integral to machine learning, with their robustness being a critical measure of effectiveness against adversarial data manipulations. Despite advancements in algorithms, current solutions are either optimal but lack scalability or scale well, but do not guarrantee optimality. This paper presents a novel adaptation of the...

User Interface Optimization for Quantified Self

Self-tracking has expanded exponentially in an era defined by the ubiquitous presence of wearable technologies and smart devices. From health and fitness to finances and productivity, these devices empower users to delve into their quantified self (QS) through an almost infinite amount of visualizations. However, a user has limited time to...

An Investigation Into Predict-and-Optimize Machine Learning

Predict-and-Optimize (PnO) is a relatively new machine learning paradigm that has attracted recent interest: it concerns the prediction of parameters that determine the value of solutions to an optimization problem, such that the optimizer ends up picking a good solution. Training estimators with standard loss functions like mean squared error...

Deepification: Learning Variable Ordering Heuristics in Constraint Optimization Problems

Constraint programming is a paradigm for solving combinatorial problems by checking whether constraints are satisfied in a constraint satisfaction problem or by optimizing an objective in a constraint optimization problem. To find solutions, the solver needs to find a variable and value ordering. Numerous heuristics designed by human experts...

Relocatable Energy Storage Systems for Congestion Management

In 2018 the Dutch government, businesses and other stakeholders started negotiating an agreement to combat climate change (het Klimaatakkoord). Energy consumption is changing and more Renewable Energy Sources (RES) are implemented. While these changes are ongoing, the Distribution System Operators (DSOs) must ensure no congestion occurs in the...

Optimal scheduling for agile Earth observation satellites

The past decade has seen a continuous increase of Earth observation missions, since they are regarded as an important tool to address global problems such as climate change or disaster mitigation. A commercial trend exists now towards higher resolution imagery, which drives the use of agile satellites. Nevertheless, a disadvantage of agile...