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Found 25 papers, 11 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.
Conceptualizing Uncertainty
In this work, we propose to explain the uncertainty in high-dimensional data classification settings by means of concept activation vectors which give rise to local and global explanations of uncertainty.
Continual Learning Should Move Beyond Incremental Classification
For each challenge, we provide specific recommendations to help move the field forward, including formalizing temporal dynamics through distribution processes, developing principled approaches for continuous task spaces, and incorporating density estimation and generative objectives.
An Algorithm-Centered Approach To Model Streaming Data
Besides the classical offline setup of machine learning, stream learning constitutes a well-established setup where data arrives over time in potentially non-stationary environments.
Adversarial Attacks for Drift Detection
Concept drift refers to the change of data distributions over time.
Challenges, Methods, Data -- a Survey of Machine Learning in Water Distribution Networks
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 climate change.
FairGLVQ: Fairness in Partition-Based Classification
The contribution of this work is twofold: 1) we develop a general framework for fair machine learning of partition-based models that does not depend on a specific fairness definition, and 2) we derive a fair version of learning vector quantization (LVQ) as a specific instantiation.
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.
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.
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.
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.
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.
