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07/17/2023
Fairness in KI-Systemen
The more AI-assisted decisions affect people's lives, the more important...
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06/13/2023
iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios
Post-hoc explanation techniques such as the well-established partial dep...
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06/13/2023
For Better or Worse: The Impact of Counterfactual Explanations' Directionality on User Behavior in xAI
Counterfactual explanations (CFEs) are a popular approach in explainable...
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03/16/2023
Model Based Explanations of Concept Drift
The notion of concept drift refers to the phenomenon that the distributi...
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03/02/2023
iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams
Explainable Artificial Intelligence (XAI) focuses mainly on batch learni...
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03/02/2023
SHAP-IQ: Unified Approximation of any-order Shapley Interactions
Predominately in explainable artificial intelligence (XAI) research, the...
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02/08/2023
Combining self-labeling and demand based active learning for non-stationary data streams
Learning from non-stationary data streams is a research direction that g...
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12/02/2022
On the Change of Decision Boundaries and Loss in Learning with Concept Drift
The notion of concept drift refers to the phenomenon that the distributi...
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12/01/2022
Explainable Artificial Intelligence for Improved Modeling of Processes
In modern business processes, the amount of data collected has increased...
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11/27/2022
"Explain it in the Same Way!" – Model-Agnostic Group Fairness of Counterfactual Explanations
Counterfactual explanations are a popular type of explanation for making...
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11/23/2022
Unsupervised Unlearning of Concept Drift with Autoencoders
The phenomena of concept drift refers to a change of the data distributi...
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11/21/2022
Novel transfer learning schemes based on Siamese networks and synthetic data
Transfer learning schemes based on deep networks which have been trained...
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11/17/2022
Spatial Graph Convolution Neural Networks for Water Distribution Systems
We investigate the task of missing value estimation in graphs as given b...
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09/05/2022
Incremental Permutation Feature Importance (iPFI): Towards Online Explanations on Data Streams
Explainable Artificial Intelligence (XAI) has mainly focused on static l...
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07/05/2022
"Even if ..." – Diverse Semifactual Explanations of Reject
Machine learning based decision making systems applied in safety critica...
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06/15/2022
"Why Here and Not There?" – Diverse Contrasting Explanations of Dimensionality Reduction
Dimensionality reduction is a popular preprocessing and a widely used to...
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05/18/2022
One Explanation to Rule them All – Ensemble Consistent Explanations
Transparency is a major requirement of modern AI based decision making s...
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05/16/2022
Model Agnostic Local Explanations of Reject
The application of machine learning based decision making systems in saf...
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05/13/2022
Precise Change Point Detection using Spectral Drift Detection
The notion of concept drift refers to the phenomenon that the data gener...
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05/11/2022
Keep Your Friends Close and Your Counterfactuals Closer: Improved Learning From Closest Rather Than Plausible Counterfactual Explanations in an Abstract Setting
Counterfactual explanations (CFEs) highlight what changes to a model's i...
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05/06/2022
Let's Go to the Alien Zoo: Introducing an Experimental Framework to Study Usability of Counterfactual Explanations for Machine Learning
To foster usefulness and accountability of machine learning (ML), it is ...
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04/14/2022
Stream-based Active Learning with Verification Latency in Non-stationary Environments
Data stream classification is an important problem in the field of machi...
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04/04/2022
SAM-kNN Regressor for Online Learning in Water Distribution Networks
Water distribution networks are a key component of modern infrastructure...
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03/28/2022
The SAME score: Improved cosine based bias score for word embeddings
Over the last years, word and sentence embeddings have established as te...
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02/21/2022
BERT WEAVER: Using WEight AVERaging to Enable Lifelong Learning for Transformer-based Models
Recent developments in transfer learning have boosted the advancements i...
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02/15/2022
Explaining Reject Options of Learning Vector Quantization Classifiers
While machine learning models are usually assumed to always output a pre...
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02/11/2022
A Graph-based U-Net Model for Predicting Traffic in unseen Cities
Accurate traffic prediction is a key ingredient to enable traffic manage...
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11/15/2021
Evaluating Metrics for Bias in Word Embeddings
Over the last years, word and sentence embeddings have established as te...
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10/10/2021
Application of Graph Convolutions in a Lightweight Model for Skeletal Human Motion Forecasting
Prediction of movements is essential for successful cooperation with int...
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08/16/2021
Task-Sensitive Concept Drift Detector with Constraint Embedding
Detecting drifts in data is essential for machine learning applications,...
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06/30/2021
Single-Step Adversarial Training for Semantic Segmentation
Even though deep neural networks succeed on many different tasks includi...
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05/17/2021
Convex optimization for actionable & plausible counterfactual explanations
Transparency is an essential requirement of machine learning based decis...
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05/04/2021
Reservoir Stack Machines
Memory-augmented neural networks equip a recurrent neural network with a...
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05/01/2021
Estimating the electrical power output of industrial devices with end-to-end time-series classification in the presence of label noise
In complex industrial settings, it is common practice to monitor the ope...
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04/06/2021
Contrastive Explanations for Explaining Model Adaptations
Many decision making systems deployed in the real world are not static -...
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03/03/2021
Fairness and Robustness of Contrasting Explanations
Fairness and explainability are two important and closely related requir...
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12/25/2020
Intuitiveness in Active Teaching
Machine learning is a double-edged sword: it gives rise to astonishing r...
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12/01/2020
Analysis of Drifting Features
The notion of concept drift refers to the phenomenon that the distributi...
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11/08/2020
Locally Adaptive Nearest Neighbors
When training automated systems, it has been shown to be beneficial to a...
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10/20/2020
Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation
Motivation: Innovative microfluidic systems carry the promise to greatly...
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10/06/2020
Efficient computation of contrastive explanations
With the increasing deployment of machine learning systems in practice, ...
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09/14/2020
Reservoir Memory Machines as Neural Computers
Differentiable neural computers extend artificial neural networks with a...
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06/23/2020
Counterfactual Explanations of Concept Drift
The notion of concept drift refers to the phenomenon that the distributi...
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05/21/2020
Supervised Learning in the Presence of Concept Drift: A modelling framework
We present a modelling framework for the investigation of supervised lea...
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04/01/2020
Sequential Feature Classification in the Context of Redundancies
The problem of all-relevant feature selection is concerned with finding ...
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02/12/2020
Convex Density Constraints for Computing Plausible Counterfactual Explanations
The increasing deployment of machine learning as well as legal regulatio...
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12/10/2019
Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information
Advances in machine learning technologies have led to increasingly power...
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12/04/2019
A probability theoretic approach to drifting data in continuous time domains
The notion of drift refers to the phenomenon that the distribution, whic...
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11/15/2019
On the computation of counterfactual explanations – A survey
Due to the increasing use of machine learning in practice it becomes mor...
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11/12/2019