3 citations · 7 across the 11 of their papers we have counts for
12 papers
Space-sampled Value Decay: Forgetting Mechanisms for Non-stationary Reinforcement Learning
Felix Störck, Fabian Hinder, Philipp Hartmann +2
Reinforcement Learning agents deployed on physical systems must adapt continually, since degradation and shifting environment conditions change the dynamics (they \emph{drift}) ove…
Extending Fair Null-Space Projections for Continuous Attributes to Kernel Methods
Felix Störck, Fabian Hinder, Barbara Hammer
With the on-going integration of machine learning systems into the everyday social life of millions the notion of fairness becomes an ever increasing priority in their development.…
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…
Conceptualizing Uncertainty: A Concept-based Approach to Explaining Uncertainty
Isaac Roberts, Alexander Schulz, Sarah Schroeder +2
Uncertainty in machine learning refers to the degree of confidence or lack thereof in a model's predictions. While uncertainty quantification methods exist, explanations of uncerta…
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…
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…