5 citations · 18 across the 16 of their papers we have counts for
19 papers
Drift Localization using Conformal Predictions
Fabian Hinder, Valerie Vaquet, Johannes Brinkrolf +1
Concept drift -- the change of the distribution over time -- poses significant challenges for learning systems and is of central interest for monitoring. Understanding drift is thu…
Exploring the Limits of Large Language Models as a Reflection of Human Cognition: An Illustration in the Context of Moral Judgement
Sarah Schröder, Thekla Morgenroth, Ulrike Kuhl +2
Large Language Models (LLMs), such as ChatGPT, are increasingly used in research, ranging from simple writing assistance to complex data annotation tasks. Recently, some research h…
Causal Explanation of Concept Drift -- A Truly Actionable Approach
David Komnick, Kathrin Lammers, Barbara Hammer +2
In a world that constantly changes, it is crucial to understand how those changes impact different systems, such as industrial manufacturing or critical infrastructure. Explaining…
Continuous Fair SMOTE -- Fairness-Aware Stream Learning from Imbalanced Data
Kathrin Lammers, Valerie Vaquet, Barbara Hammer
As machine learning is increasingly applied in an online fashion to deal with evolving data streams, the fairness of these algorithms is a matter of growing ethical and legal conce…
An Algorithm-Centered Approach To Model Streaming Data
Fabian Hinder, Valerie Vaquet, David Komnick +1
Besides the classical offline setup of machine learning, stream learning constitutes a well-established setup where data arrives over time in potentially non-stationary environment…
Adversarial Attacks for Drift Detection
Fabian Hinder, Valerie Vaquet, Barbara Hammer
Concept drift refers to the change of data distributions over time. While drift poses a challenge for learning models, requiring their continual adaption, it is also relevant in sy…
Challenges, Methods, Data -- a Survey of Machine Learning in Water Distribution Networks
Valerie Vaquet, Fabian Hinder, André Artelt +5
Research on methods for planning and controlling water distribution networks gains increasing relevance as the availability of drinking water will decrease as a consequence of clim…
FairGLVQ: Fairness in Partition-Based Classification
Felix Störck, Fabian Hinder, Johannes Brinkrolf +3
Fairness is an important objective throughout society. From the distribution of limited goods such as education, over hiring and payment, to taxes, legislation, and jurisprudence.…
Investigating the Suitability of Concept Drift Detection for Detecting Leakages in Water Distribution Networks
Valerie Vaquet, Fabian Hinder, Barbara Hammer
Leakages are a major risk in water distribution networks as they cause water loss and increase contamination risks. Leakage detection is a difficult task due to the complex dynamic…
A Remark on Concept Drift for Dependent Data
Fabian Hinder, Valerie Vaquet, Barbara Hammer
Concept drift, i.e., the change of the data generating distribution, can render machine learning models inaccurate. Several works address the phenomenon of concept drift in the str…
One or Two Things We know about Concept Drift -- A Survey on Monitoring Evolving Environments
Fabian Hinder, Valerie Vaquet, Barbara Hammer
The world surrounding us is subject to constant change. These changes, frequently described as concept drift, influence many industrial and technical processes. As they can lead to…
Localizing Anomalies in Critical Infrastructure using Model-Based Drift Explanations
Valerie Vaquet, Fabian Hinder, Jonas Vaquet +3
Facing climate change, the already limited availability of drinking water will decrease in the future rendering drinking water an increasingly scarce resource. Considerable amounts…
Model Based Explanations of Concept Drift
Fabian Hinder, Valerie Vaquet, Johannes Brinkrolf +1
The notion of concept drift refers to the phenomenon that the distribution generating the observed data changes over time. If drift is present, machine learning models can become i…
Combining self-labeling and demand based active learning for non-stationary data streams
Valerie Vaquet, Fabian Hinder, Johannes Brinkrolf +1
Learning from non-stationary data streams is a research direction that gains increasing interest as more data in form of streams becomes available, for example from social media, s…
On the Change of Decision Boundaries and Loss in Learning with Concept Drift
Fabian Hinder, Valerie Vaquet, Johannes Brinkrolf +1
The notion of concept drift refers to the phenomenon that the distribution generating the observed data changes over time. If drift is present, machine learning models may become i…
Precise Change Point Detection using Spectral Drift Detection
Fabian Hinder, André Artelt, Valerie Vaquet +1
The notion of concept drift refers to the phenomenon that the data generating distribution changes over time; as a consequence machine learning models may become inaccurate and nee…
Suitability of Different Metric Choices for Concept Drift Detection
Fabian Hinder, Valerie Vaquet, Barbara Hammer
The notion of concept drift refers to the phenomenon that the distribution, which is underlying the observed data, changes over time; as a consequence machine learning models may b…
Contrastive Explanations for Explaining Model Adaptations
André Artelt, Fabian Hinder, Valerie Vaquet +2
Many decision making systems deployed in the real world are not static - a phenomenon known as model adaptation takes place over time. The need for transparency and interpretabilit…
Evaluating Robustness of Counterfactual Explanations
André Artelt, Valerie Vaquet, Riza Velioglu +4
Transparency is a fundamental requirement for decision making systems when these should be deployed in the real world. It is usually achieved by providing explanations of the syste…