19 citations · 51 across the 26 of their papers we have counts for
29 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…
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…
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…
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…
Continual Learning Should Move Beyond Incremental Classification
Rupert Mitchell, Antonio Alliegro, Raffaello Camoriano +17
Continual learning (CL) is the sub-field of machine learning concerned with accumulating knowledge in dynamic environments. So far, CL research has mainly focused on incremental cl…
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…
Semantic Properties of cosine based bias scores for word embeddings
Sarah Schröder, Alexander Schulz, Fabian Hinder +1
Plenty of works have brought social biases in language models to attention and proposed methods to detect such biases. As a result, the literature contains a great deal of differen…
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…
The SAME score: Improved cosine based bias score for word embeddings
Sarah Schröder, Alexander Schulz, Barbara Hammer
With the enourmous popularity of large language models, many researchers have raised ethical concerns regarding social biases incorporated in such models. Several methods to measur…
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…
Evaluating Metrics for Bias in Word Embeddings
Sarah Schröder, Alexander Schulz, Philip Kenneweg +3
Over the last years, word and sentence embeddings have established as text preprocessing for all kinds of NLP tasks and improved the performances significantly. Unfortunately, it h…
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…