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Found 20 papers, 9 papers with code
Towards non-parametric drift detection via Dynamic Adapting Window Independence Drift Detection (DAWIDD)
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 become inaccurate and need adjustment.
Semantic Properties of cosine based bias scores for word embeddings
Furthermore, we formally analyze cosine based scores from the literature with regard to these requirements.
Investigating the Suitability of Concept Drift Detection for Detecting Leakages in Water Distribution Networks
In this work, we explore the potential of model-loss-based and distribution-based drift detection methods to tackle leakage detection.
A Remark on Concept Drift for Dependent Data
Concept drift, i. e., the change of the data generating distribution, can render machine learning models inaccurate.
Localizing Anomalies in Critical Infrastructure using Model-Based Drift Explanations
Facing climate change, the already limited availability of drinking water will decrease in the future rendering drinking water an increasingly scarce resource.
One or Two Things We know about Concept Drift -- A Survey on Monitoring Evolving Environments
In addition to providing a systematic literature review, this work provides precise mathematical definitions of the considered problems and contains standardized experiments on parametric artificial datasets allowing for a direct comparison of different strategies for detection and localization.
Model Based Explanations of Concept Drift
To do so, we propose a methodology to reduce the explanation of concept drift to an explanation of models that are trained in a suitable way extracting relevant information regarding the drift.
Combining self-labeling and demand based active learning for non-stationary data streams
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, smartphones, or industrial process monitoring.
On the Change of Decision Boundaries and Loss in Learning with Concept Drift
More precisely, we relate a change of the ITTE to the presence of real drift, i. e., a changed posterior, and to a change of the training result under the assumption of optimality.
Precise Change Point Detection using Spectral Drift Detection
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 need adjustment.
The SAME score: Improved cosine based bias score for word embeddings
Furthermore, we thoroughly investigate the existing cosine-based scores and their limitations in order to show why these scores fail to report biases in some situations.
Suitability of Different Metric Choices for Concept Drift Detection
In this paper, we analyze structural properties of the drift induced signals in the context of different metrics.
Evaluating Metrics for Bias in Word Embeddings
However, lately some works have raised doubts about these metrics showing that even though such metrics report low biases, other tests still show biases.
Contrastive Explanations for Explaining Model Adaptations
We also propose a method for automatically finding regions in data space that are affected by a given model adaptation and thus should be explained.
Evaluating Robustness of Counterfactual Explanations
Counterfactual explanations explain a behavior to the user by proposing actions -- as changes to the input -- that would cause a different (specified) behavior of the system.
Analysis of Drifting Features
The notion of concept drift refers to the phenomenon that the distribution, which is underlying the observed data, changes over time.
Counterfactual Explanations of Concept Drift
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 become inaccurate and need adjustment.
Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information
In this contribution, we focus on feature selection paradigms, which enable us to uncover relevant factors of a given regularity based on a sparse model.
A probability theoretic approach to drifting data in continuous time domains
The notion of drift refers to the phenomenon that the distribution, which is underlying the observed data, changes over time.
DeepView: Visualizing Classification Boundaries of Deep Neural Networks as Scatter Plots Using Discriminative Dimensionality Reduction
So far, most methods in the literature investigate the decision of the model for a single given input datum.
