60 citations · 163 across the 27 of their papers we have counts for
7 papers · 1 filter
Ambulance Demand Prediction via Convolutional Neural Networks
Maximiliane Rautenstrauß, Maximilian Schiffer
Minimizing response times is crucial for emergency medical services to reduce patients' waiting times and to increase their survival rates. Many models exist to optimize operationa…
Dynamic Neighborhood Construction for Structured Large Discrete Action Spaces
Fabian Akkerman, Julius Luy, Wouter van Heeswijk +1
Large discrete action spaces (LDAS) remain a central challenge in reinforcement learning. Existing solution approaches can handle unstructured LDAS with up to a few million actions…
Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems
Tobias Enders, James Harrison, Marco Pavone +1
We consider the sequential decision-making problem of making proactive request assignment and rejection decisions for a profit-maximizing operator of an autonomous mobility on dema…
Bilevel Optimization for Feature Selection in the Data-Driven Newsvendor Problem
Breno Serrano, Stefan Minner, Maximilian Schiffer +1
We study the feature-based newsvendor problem, in which a decision-maker has access to historical data consisting of demand observations and exogenous features. In this setting, we…
Support Vector Machines with the Hard-Margin Loss: Optimal Training via Combinatorial Benders' Cuts
Ítalo Santana, Breno Serrano, Maximilian Schiffer +1
The classical hinge-loss support vector machines (SVMs) model is sensitive to outlier observations due to the unboundedness of its loss function. To circumvent this issue, recent s…
Optimal Decision Diagrams for Classification
Alexandre M. Florio, Pedro Martins, Maximilian Schiffer +2
Decision diagrams for classification have some notable advantages over decision trees, as their internal connections can be determined at training time and their width is not bound…
Born-Again Tree Ensembles
Thibaut Vidal, Toni Pacheco, Maximilian Schiffer
The use of machine learning algorithms in finance, medicine, and criminal justice can deeply impact human lives. As a consequence, research into interpretable machine learning has…