67 citations · 118 across the 22 of their papers we have counts for
5 papers · 1 filter
Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees
Guiliang Liu, Oliver Schulte, Wang Zhu +1
Deep Reinforcement Learning (DRL) has achieved impressive success in many applications. A key component of many DRL models is a neural network representing a Q function, to estimat…
Inference, Learning, and Population Size: Projectivity for SRL Models
Manfred Jaeger, Oliver Schulte
A subtle difference between propositional and relational data is that in many relational models, marginal probabilities depend on the population or domain size. This paper connects…
Model-based Exception Mining for Object-Relational Data
Fatemeh Riahi, Oliver Schulte
This paper is based on a previous publication [29]. Our work extends exception mining and outlier detection to the case of object-relational data. Object-relational data represent…
Deep Reinforcement Learning in Ice Hockey for Context-Aware Player Evaluation
Guiliang Liu, Oliver Schulte
A variety of machine learning models have been proposed to assess the performance of players in professional sports. However, they have only a limited ability to model how player p…
Model Trees for Identifying Exceptional Players in the NHL Draft
Oliver Schulte, Yejia Liu, Chao Li
Drafting strong players is crucial for the team success. We describe a new data-driven interpretable approach for assessing draft prospects in the National Hockey League. Successfu…