142 citations · 493 across the 17 of their papers we have counts for
17 papers
Measuring Successful Cooperation in Human-AI Teamwork: Development and Validation of the Perceived Cooperativity and Teaming Perception Scales
Christiane Attig, Christiane Wiebel-Herboth, Patricia Wollstadt +3
As human-AI cooperation becomes increasingly prevalent, reliable instruments for assessing the subjective quality of cooperative human-AI interaction are needed. We introduce two t…
Assistax: A Multi-Agent Hardware-Accelerated Reinforcement Learning Benchmark for Assistive Robotics
Leonard Hinckeldey, Elliot Fosong, Rimvydas Rubavicius +6
As embodied autonomous systems capable of assisting humans in daily activities remain a major goal for robotics, efficient and appropriate reinforcement learning (RL) simulation te…
A Real-World Energy Management Dataset from a Smart Company Building for Optimization and Machine Learning
Jens Engel, Andrea Castellani, Patricia Wollstadt +9
We present a large real-world dataset obtained from monitoring a smart company facility over the course of six years, from 2018 to 2023. The dataset includes energy consumption dat…
Partial Information Decomposition for Continuous Variables based on Shared Exclusions: Analytical Formulation and Estimation
David A. Ehrlich, Kyle Schick-Poland, Abdullah Makkeh +3
Describing statistical dependencies is foundational to empirical scientific research. For uncovering intricate and possibly non-linear dependencies between a single target variable…
Precision and Recall Reject Curves for Classification
Lydia Fischer, Patricia Wollstadt
For some classification scenarios, it is desirable to use only those classification instances that a trained model associates with a high certainty. To obtain such high-certainty i…
Understanding Concept Identification as Consistent Data Clustering Across Multiple Feature Spaces
Felix Lanfermann, Sebastian Schmitt, Patricia Wollstadt
Identifying meaningful concepts in large data sets can provide valuable insights into engineering design problems. Concept identification aims at identifying non-overlapping groups…
Information-theoretic analyses of neural data to minimize the effect of researchers' assumptions in predictive coding studies
Patricia Wollstadt, Daniel L. Rathbun, W. Martin Usrey and +4
Studies investigating neural information processing often implicitly ask both, which processing strategy out of several alternatives is used and how this strategy is implemented in…
Interaction-Aware Sensitivity Analysis for Aerodynamic Optimization Results using Information Theory
Patricia Wollstadt, Sebastian Schmitt
An important issue during an engineering design process is to develop an understanding which design parameters have the most influence on the performance. Especially in the context…
A partial information decomposition for discrete and continuous variables
Kyle Schick-Poland, Abdullah Makkeh, Aaron J. Gutknecht +3
Conceptually, partial information decomposition (PID) is concerned with separating the information contributions several sources hold about a certain target by decomposing the corr…
A Rigorous Information-Theoretic Definition of Redundancy and Relevancy in Feature Selection Based on (Partial) Information Decomposition
Patricia Wollstadt, Sebastian Schmitt, Michael Wibral
Selecting a minimal feature set that is maximally informative about a target variable is a central task in machine learning and statistics. Information theory provides a powerful f…
Quantifying the predictability of visual scanpaths using active information storage
Patricia Wollstadt, Martina Hasenjäger, Christiane B. Wiebel-Herboth
Entropy-based measures are an important tool for studying human gaze behavior under various conditions. In particular, gaze transition entropy (GTE) is a popular method to quantify…
A Compact Spectral Descriptor for Shape Deformations
Skylar Sible, Rodrigo Iza-Teran, Jochen Garcke +2
Modern product design in the engineering domain is increasingly driven by computational analysis including finite-element based simulation, computational optimization, and modern d…
Large-scale directed network inference with multivariate transfer entropy and hierarchical statistical testing
Leonardo Novelli, Patricia Wollstadt, Pedro Mediano +2
Network inference algorithms are valuable tools for the study of large-scale neuroimaging datasets. Multivariate transfer entropy is well suited for this task, being a model-free m…
IDTxl: The Information Dynamics Toolkit xl: a Python package for the efficient analysis of multivariate information dynamics in networks
Patricia Wollstadt, Joseph T. Lizier, Raul Vicente +5
The Information Dynamics Toolkit xl (IDTxl) is a comprehensive software package for efficient inference of networks and their node dynamics from multivariate time series data using…
Breakdown of local information processing may underlie isoflurane anesthesia effects
Patricia Wollstadt, Kristin K. Sellers, Lucas Rudelt +4
The disruption of coupling between brain areas has been suggested as the mechanism underlying loss of consciousness in anesthesia. This hypothesis has been tested previously by mea…
A Graph Algorithmic Approach to Separate Direct from Indirect Neural Interactions
Patricia Wollstadt, Ulrich Meyer, Michael Wibral
Network graphs have become a popular tool to represent complex systems composed of many interacting subunits; especially in neuroscience, network graphs are increasingly used to re…
Efficient transfer entropy analysis of non-stationary neural time series
Patricia Wollstadt, Mario Martínez-Zarzuela, Raul Vicente +2
Information theory allows us to investigate information processing in neural systems in terms of information transfer, storage and modification. Especially the measure of informati…