Nicolas Riesterer
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View article: Distributed Biomarker Discovery for Immuno-Oncology
Distributed Biomarker Discovery for Immuno-Oncology Open
Background: Identifying robust predictive biomarkers for immuno-oncology (IO) therapy response remains challenging due to the complexity of tumor host interactions and the limited availability of public datasets. While achieving clinical r…
View article: Fully dynamic reorder policies with deep reinforcement learning for multi-echelon inventory management
Fully dynamic reorder policies with deep reinforcement learning for multi-echelon inventory management Open
The operation of inventory systems plays an important role in the success of manufacturing companies, making it a highly relevant domain for optimization. In particular, the domain lends itself to being approached via Deep Reinforcement Le…
View article: Multi-Echelon Inventory Optimization Using Deep Reinforcement Learning
Multi-Echelon Inventory Optimization Using Deep Reinforcement Learning Open
In this chapter, we provide an overview of inventory management within the pharmaceutical industry and how to model and optimize it. Inventory management is a highly relevant topic, as it causes high costs such as holding, shortage, and re…
View article: Model‐Based Explanation of Feedback Effects in Syllogistic Reasoning
Model‐Based Explanation of Feedback Effects in Syllogistic Reasoning Open
For decades, a significant number of models explaining human syllogistic inference processes were developed. There is profound work fitting the models' parameters and analyzing each model's ability to account for the data in order to suppo…
View article: The Predictive Power of Spatial Relational Reasoning Models: A New Evaluation Approach
The Predictive Power of Spatial Relational Reasoning Models: A New Evaluation Approach Open
In the last few decades, cognitive theories for explaining human spatial relational reasoning have increased. Few of these theories have been implemented as computational models, however, even fewer have been compared computationally to ea…
View article: Unifying Models for Belief and Syllogistic Reasoning
Unifying Models for Belief and Syllogistic Reasoning Open
Judging if a conclusion follows logically from a given set of\npremises can depend much more on the believability than on\nthe logical validity of the conclusion. This so-called belief bias\neffect has been replicated repeatedly for many d…
View article: Predictive Modeling of Individual Human Cognition: Upper Bounds and a New Perspective on Performance
Predictive Modeling of Individual Human Cognition: Upper Bounds and a New Perspective on Performance Open
Model evaluation is commonly performed by relying on aggregated data as well as relative metrics for model comparison and selection. In light of recent criticism about the prevailing perspectives on cognitive modeling, we investigate model…
View article: Uncovering the Data-Related Limits of Human Reasoning Research: An Analysis based on Recommender Systems
Uncovering the Data-Related Limits of Human Reasoning Research: An Analysis based on Recommender Systems Open
Understanding the fundamentals of human reasoning is central to the development of any system built to closely interact with humans. Cognitive science pursues the goal of modeling human-like intelligence from a theory-driven perspective wi…
View article: Do Models Capture Individuals? Evaluating Parameterized Models for Syllogistic Reasoning
Do Models Capture Individuals? Evaluating Parameterized Models for Syllogistic Reasoning Open
The prevailing focus on aggregated data and the lacking group-to-individual generalizability it entails have recently been iden-tified as a major cause for the low performance of cognitivemodels in the field of syllogistic reasoning resear…
View article: Analyzing the Differences in Human Reasoning via Joint Nonnegative Matrix Factorization
Analyzing the Differences in Human Reasoning via Joint Nonnegative Matrix Factorization Open
Joint Nonnegative Matrix Factorization (JNMF) is a method for factor analysis that is capable of simultaneously decomposing two datasets into related latent state representations. Enabling factor analysis for contrasting applications, i.e.…
View article: Modeling Human Syllogistic Reasoning: The Role of “No Valid Conclusion”
Modeling Human Syllogistic Reasoning: The Role of “No Valid Conclusion” Open
Syllogistic reasoning, that is the drawing of inferences for categorical‐quantified assertions, is one of the oldest branches of deductive reasoning research with a history exceeding 100 years. In syllogistic reasoning experiments, “No Val…