Barbara Hammer

research

∙ 07/17/2023

Fairness in KI-Systemen

The more AI-assisted decisions affect people's lives, the more important...

0 Janine Strotherm, et al. ∙

research

∙ 06/13/2023

iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios

Post-hoc explanation techniques such as the well-established partial dep...

0 Maximilian Muschalik, et al. ∙

research

∙ 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...

0 Ulrike Kuhl, et al. ∙

research

∙ 03/16/2023

Model Based Explanations of Concept Drift

The notion of concept drift refers to the phenomenon that the distributi...

0 Fabian Hinder, et al. ∙

research

∙ 03/02/2023

iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams

Explainable Artificial Intelligence (XAI) focuses mainly on batch learni...

0 Maximilian Muschalik, et al. ∙

research

∙ 03/02/2023

SHAP-IQ: Unified Approximation of any-order Shapley Interactions

Predominately in explainable artificial intelligence (XAI) research, the...

0 Fabian Fumagalli, et al. ∙

research

∙ 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...

0 Valerie Vaquet, et al. ∙

research

∙ 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...

0 Fabian Hinder, et al. ∙

research

∙ 12/01/2022

Explainable Artificial Intelligence for Improved Modeling of Processes

In modern business processes, the amount of data collected has increased...

0 Riza Velioglu, et al. ∙

research

∙ 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...

0 André Artelt, et al. ∙

research

∙ 11/23/2022

Unsupervised Unlearning of Concept Drift with Autoencoders

The phenomena of concept drift refers to a change of the data distributi...

0 André Artelt, et al. ∙

research

∙ 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...

0 Dominik Stallmann, et al. ∙

research

∙ 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...

0 Inaam Ashraf, et al. ∙

research

∙ 09/05/2022

Incremental Permutation Feature Importance (iPFI): Towards Online Explanations on Data Streams

Explainable Artificial Intelligence (XAI) has mainly focused on static l...

0 Fabian Fumagalli, et al. ∙

research

∙ 07/05/2022

"Even if ..." – Diverse Semifactual Explanations of Reject

Machine learning based decision making systems applied in safety critica...

0 André Artelt, et al. ∙

research

∙ 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...

0 André Artelt, et al. ∙

research

∙ 05/18/2022

One Explanation to Rule them All – Ensemble Consistent Explanations

Transparency is a major requirement of modern AI based decision making s...

0 André Artelt, et al. ∙

research

∙ 05/16/2022

Model Agnostic Local Explanations of Reject

The application of machine learning based decision making systems in saf...

0 André Artelt, et al. ∙

research

∙ 05/13/2022

Precise Change Point Detection using Spectral Drift Detection

The notion of concept drift refers to the phenomenon that the data gener...

0 Fabian Hinder, et al. ∙

research

∙ 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...

0 Ulrike Kuhl, et al. ∙

research

∙ 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 ...

9 Ulrike Kuhl, et al. ∙

research

∙ 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...

0 Andrea Castellani, et al. ∙

research

∙ 04/04/2022

SAM-kNN Regressor for Online Learning in Water Distribution Networks

Water distribution networks are a key component of modern infrastructure...

0 Jonathan Jakob, et al. ∙

research

∙ 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...

0 Sarah Schröder, et al. ∙

research

∙ 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...

0 Lisa Langnickel, et al. ∙

research

∙ 02/15/2022

Explaining Reject Options of Learning Vector Quantization Classifiers

While machine learning models are usually assumed to always output a pre...

0 André Artelt, et al. ∙

research

∙ 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...

0 Luca Hermes, et al. ∙

research

∙ 11/15/2021

Evaluating Metrics for Bias in Word Embeddings

Over the last years, word and sentence embeddings have established as te...

0 Sarah Schröder, et al. ∙

research

∙ 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...

0 Luca Hermes, et al. ∙

research

∙ 08/16/2021

Task-Sensitive Concept Drift Detector with Constraint Embedding

Detecting drifts in data is essential for machine learning applications,...

0 Andrea Castellani, et al. ∙

research

∙ 06/30/2021

Single-Step Adversarial Training for Semantic Segmentation

Even though deep neural networks succeed on many different tasks includi...

0 Daniel Wiens, et al. ∙

research

∙ 05/17/2021

Convex optimization for actionable & plausible counterfactual explanations

Transparency is an essential requirement of machine learning based decis...

0 André Artelt, et al. ∙

research

∙ 05/04/2021

Reservoir Stack Machines

Memory-augmented neural networks equip a recurrent neural network with a...

0 Benjamin Paaßen, et al. ∙

research

∙ 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...

0 Andrea Castellani, et al. ∙

research

∙ 04/06/2021

Contrastive Explanations for Explaining Model Adaptations

Many decision making systems deployed in the real world are not static -...

0 André Artelt, et al. ∙

research

∙ 03/03/2021

Fairness and Robustness of Contrasting Explanations

Fairness and explainability are two important and closely related requir...

0 André Artelt, et al. ∙

research

∙ 12/25/2020

Intuitiveness in Active Teaching

Machine learning is a double-edged sword: it gives rise to astonishing r...

3 Jan Philip Göpfert, et al. ∙

research

∙ 12/01/2020

Analysis of Drifting Features

The notion of concept drift refers to the phenomenon that the distributi...

0 Fabian Hinder, et al. ∙

research

∙ 11/08/2020

Locally Adaptive Nearest Neighbors

When training automated systems, it has been shown to be beneficial to a...

0 Jan Philip Göpfert, et al. ∙

research

∙ 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...

10 Dominik Stallmann, et al. ∙

research

∙ 10/06/2020

Efficient computation of contrastive explanations

With the increasing deployment of machine learning systems in practice, ...

0 André Artelt, et al. ∙

research

∙ 09/14/2020

Reservoir Memory Machines as Neural Computers

Differentiable neural computers extend artificial neural networks with a...

0 Benjamin Paaßen, et al. ∙

research

∙ 06/23/2020

Counterfactual Explanations of Concept Drift

The notion of concept drift refers to the phenomenon that the distributi...

0 Fabian Hinder, et al. ∙

research

∙ 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...

0 Michiel Straat, et al. ∙

research

∙ 04/01/2020

Sequential Feature Classification in the Context of Redundancies

The problem of all-relevant feature selection is concerned with finding ...

0 Lukas Pfannschmidt, et al. ∙

research

∙ 02/12/2020

Convex Density Constraints for Computing Plausible Counterfactual Explanations

The increasing deployment of machine learning as well as legal regulatio...

0 André Artelt, et al. ∙

research

∙ 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...

0 Lukas Pfannschmidt, et al. ∙

research

∙ 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...

0 Fabian Hinder, et al. ∙

research

∙ 11/15/2019

On the computation of counterfactual explanations – A survey

Due to the increasing use of machine learning in practice it becomes mor...

0 André Artelt, et al. ∙

research

∙ 11/12/2019

Deep-Aligned Convolutional Neural Network for Skeleton-based Action Recognition and Segmentation

Convolutional neural networks (CNNs) are deep learning frameworks which ...

13 Babak Hosseini, et al. ∙

Barbara Hammer

Featured Co-authors