Concept drift -the change of the distribution over timeposes significant challenges for learning systems and is of central interest for monitoring.Understanding drift is thus paramount, and drift localization -determining which samples are affected by the drift -is essential.While several approaches exist, most rely on local testing schemes, which tend to fail in high-dimensional, low-signal settings.In this work, we consider a fundamentally different approach based on conformal predictions.We discuss and show the shortcomings of common approaches and demonstrate the performance of our approach on state-of-the-art image datasets.

Algorithmic fairness, a key concern in algorithmic decision making, is a well-studied topic in the batch setup.Recently, several extensions for improving fairness in classification tasks have been proposed for the important scenario of non-stationary data streams.Yet, the question of how to reliably evaluate fairness for non-stationary data streams is still open, as popular batch measures can lead to misleading results.Specifically, typically cumulative fairness measures can be problematic if concept drift results in significant changes in model fairness across the data stream.In this contribution, we propose novel fairness scores that are suitable for the streaming scenario, and we demonstrate their suitability on streaming data benchmarks.

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 system monitoring to detect malfunctions, system failures, and unexpected behavior.In the latter case, the robust and reliable detection of drifts is imperative.This work studies the shortcomings of commonly used drift detection schemes.We show that they are prone to adversarial attacks, i.e., streams with undetected drift.In particular, we give necessary and sufficient conditions for their existence, provide methods for their construction, and demonstrate this behavior in experiments.

Concept drift refers to the phenomenon that the underlying data distribution changes over time.While detection methods or model adjustment methods exist, a proper explanation of drift in high-dimensional settings is still widely unsolved.This problem is crucial since it enables an understanding of the most prominent drift characteristics.In this work, we propose to explain concept drift of high-dimensional data objects by means of concept activation vectors which give rise to local, phase, and a novel, global explanation called the Concept 2 Drift Distribution.

Catastrophic forgetting is a key challenge in continual learning.In the adjoining field of stream machine learning, few methods target the related problem of re-occurring drift by avoiding forgetting old data.In this work, we investigate whether we can transfer such strategies from the stream machine learning to the continual learning setup.Based on our consideration, we propose a simple yet efficient compression-based kNN scheme and evaluate it experimentally.

Prototype-based methods constitute a robust and transparent family of machine-learning models.To increase robustness in real-world applications, they are frequently coupled with reject options.While the state-of-the-art method, relative similarity, couples the rejection of samples with high aleatoric and epistemic uncertainty, the technique lacks transparency, i.e., an explanation of why a sample has been rejected.In this work, we analyze the relative similarity analytically and derive an explanation scheme for reject options in prototype-based classification.

Feature selection is one of the most relevant preprocessing and analysis techniques in machine learning, allowing for increases in model performance and knowledge discovery.In online setups, both can be affected by concept drift, i.e., changes of the underlying distribution.Recently, an adaption of classical feature relevance approaches to drift detection was introduced.While the method increases detection performance significantly, there is only little discussion on the explanatory aspects.In this work, we focus on understanding the structure of the ongoing drift by transferring the concept of strongly and weakly relevant features to it.We empirically evaluate our methodology using graphical models.

Supervised online learning relies on the assumption that ground truth information is available for model updates at each time step.As this is not realistic in every setting, alternatives such as active online learning, or online learning with verification latency have been proposed.In this work, we assume that no label information is available after intitial training.We argue that provided we can characterize the expected concept drift as incremental drift, we can rely on a self-labeling strategy to keep updated models.We derive a k-NN-based self-labeling online learner implementing the presented self-supervised scheme and experimentally show that this is an option for learning from incrementally drifting data streams in the absence of label information.

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 different bias tests and scores, each introduced with the premise to uncover yet more biases that other scores fail to detect. What severely lacks in the literature, however, are comparative studies that analyse such bias scores and help researchers to understand the benefits or limitations of the existing methods. In this work, we aim to close this gap for cosine based bias scores. By building on a geometric definition of bias, we propose requirements for bias scores to be considered meaningful for quantifying biases. Furthermore, we formally analyze cosine based scores from the literature with regard to these requirements. We underline these findings with experiments to show that the bias scores' limitations have an impact in the application case.

Facing climate change, the already limited availability of drinking water will decrease in the future, rendering drinking water an increasingly scarce resource. Considerable amounts of it are lost through leakages in water transportation and distribution networks. Thus, anomaly detection and localization, in particular for leakages, are crucial but challenging tasks due to the complex interactions and changing demands in water distribution networks. In this work, we conceptually analyze the effects of anomalies on the dynamics of critical infrastructure systems by modeling them with Bayesian networks. We then discuss how the problem is connected to and can be considered through the lens of concept drift. This analysis yields our proposal to leverage model-based drift explanations as a tool for localizing anomalies given limited information about the network. The methodology is experimentally evaluated using realistic benchmark scenarios. To showcase that our methodology applies to critical infrastructure more generally, in addition to considering leakages and sensor faults in water systems, we investigate the suitability of the derived technique to localize sensor faults in power systems.

Feature selection is one of the most relevant preprocessing and analysis techniques in machine learning. It can dramatically increase the performance of learning algorithms and at the same time provide relevant information on the data. In the scenario of online and stream learning, concept drift, i.e., changes of the underlying distribution over time, can cause significant problems for learning models and data analysis. While there do exist feature selection methods for online learning, none of the methods targets feature selection for drift detection, i.e., the challenge to increase the performance of drift detectors by analyzing the drift rather than increasing model accuracy. However, this challenge is particularly relevant for common unsupervised scenarios. In this work, we study feature selection for drift detection and drift monitoring. We develop a formal definition for a feature-wise notion of drift that allows semantic interpretation. Besides, we derive an efficient algorithm by reducing the problem to classical feature selection and analyze the applicability of our approach to feature selection for drift detection on a theoretical level. Finally, we empirically show the relevance of our considerations on several benchmarks.

Feature selection is one of the most relevant preprocessing and analysis techniques in machine learning.It can dramatically increase the performance of learning algorithms and also provide relevant information on the data.In online and stream learning concept drift, i.e., the change of the underlying distribution over time, can cause tremendous problems for learning models and data analysis.While there do exist feature selection methods for online learning, to the best of our knowledge there do not exist methods to perform feature selection for drift detection, i.e., to increase the performance of drift detectors and to analyze the drift itself.In this work, we study feature selection for concept drift detection and provide a formal derivation and semantic interpretation thereof.We empirically show the relevance of our considerations on several benchmarks. 53

Hinder F, Vaquet V, Brinkrolf J, Hammer B. On the Hardness and Necessity of Supervised Concept Drift Detection. In: De Marsico M, Sanniti di Baja G, Fred A, eds. Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods ICPRAM. Vol. 1. Setúbal: SCITEPRESS - Science and Technology Publications; 2023: 164-175.

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 inaccurate and need adjustment. While there do exist methods to detect concept drift or to adjust models in the presence of observed drift, the question of explaining drift, i.e., describing the potentially complex and high dimensional change of distribution in a human-understandable fashion, has hardly been considered so far. This problem is of importance since it enables an inspection of the most prominent characteristics of how and where drift manifests itself. Hence, it enables human understanding of the change and it increases acceptance of life-long learning models. In this paper, we present a novel technology characterizing concept drift in terms of the characteristic change of spatial features based on various explanation techniques. 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 to extract relevant information regarding the drift. This way, a large variety of explanation schemes is available. Thus, a suitable method can be selected for the problem of drift explanation at hand. We outline the potential of this approach and demonstrate its usefulness in several examples.

Abstract Many of today’s decision making systems deployed in the real world are not static—they are changing and adapting over time, a phenomenon known as model adaptation takes place. Because of their wide reaching influence and potentially serious consequences, the need for transparency and interpretability of AI-based decision making systems is widely accepted and thus have been worked on extensively—e.g. a very prominent class of explanations are contrasting explanations which try to mimic human explanations. However, usually, explanation methods assume a static system that has to be explained. Explaining non-static systems is still an open research question, which poses the challenge how to explain model differences, adaptations and changes. In this contribution, we propose and (empirically) evaluate a general framework for explaining model adaptations and differences by contrasting explanations. We also propose a method for automatically finding regions in data space that are affected by a given model adaptation—i.e. regions where the internal reasoning of the other (e.g. adapted) model changed—and thus should be explained. Finally, we also propose a regularization for model adaptations to ensure that the internal reasoning of the adapted model does not change in an unwanted way.

Federated learning is an efficient methodology to reduce the data transmissions to the server when working with large amounts of (sen- sor) data from diverse physical locations. When using data from different sensor devices concept drift between the single sensors poses an additional challenge. In this contribution we define a formal framework for federated learning with concept drift and propose a version of federated LVQ dealing with concept drift induced by different hyperspectral cameras. We evalu- ate this approach experimentally and demonstrate its robustness to class imbalance and missing classes.

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. While there do exist methods to detect concept drift, to find change points in data streams, or to adjust models in the presence of observed drift, the problem of localizing drift, i.e. identifying it in data space, is yet widely unsolved - in particular from a formal perspective. This problem however is of importance, since it enables an inspection of the most prominent characteristics, e.g. features, where drift manifests itself and can therefore be used to make informed decisions, e.g. efficient updates of the training set of online learning algorithms, and perform precise adjustments of the learning model. In this paper we present a general theoretical framework that reduces drift localization to a supervised machine learning problem. We construct a new method for drift localization thereon and demonstrate the usefulness of our theory and the performance of our algorithm by comparing it to other methods from the literature.

The notion of concept drift refers to the phenomenon that the data distribution changes over time.If drift occurs, machine learning models need adjustment.Since drift can be inhomogeneous, suitable actions depending on the location in data space.In this paper we address the challenge to partition the data space into segments with homogeneous drift characteristics.We formalize this objective as an independence criterion, and derive a robust and efficient training algorithm based thereon.We evaluate the efficiency of the method in comparison to existing technologies: the identification of drifting clusters, and the estimation of a conditional density distribution.

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. Many online learning schemes include drift detection to actively detect and react to observed changes. Yet, reliable drift detection constitutes a challenging problem in particular in the context of high dimensional data, varying drift characteristics, and the absence of a parametric model such as a classification scheme which reflects the drift. In this paper we present a novel concept drift detection method, Dynamic Adapting Window Independence Drift Detection (DAWIDD), which aims for non-parametric drift detection of diverse drift characteristics. For this purpose, we establish a mathematical equivalence of the presence of drift to the dependency of specific random variables in an according drift process. This allows us to rely on independence tests rather than parametric models or the classification loss, resulting in a fairly robust scheme to universally detect different types of drift, as it is also confirmed in experiments.

Machine learning algorithms using deep architectures have been able to implement increasingly powerful and successful models. However, they also become increasingly more complex, more difficult to comprehend and easier to fool. So far, most methods in the literature investigate the decision of the model for a single given input datum. In this paper, we propose to visualize a part of the decision function of a deep neural network together with a part of the data set in two dimensions with discriminative dimensionality reduction. This enables us to inspect how different properties of the data are treated by the model, such as outliers, adversaries or poisoned data. Further, the presented approach is complementary to the mentioned interpretation methods from the literature and hence might be even more useful in combination with those. Code is available at https://github.com/LucaHermes/DeepView