Developing fair classification models is a crucial aspect of machine learning research. However, unintended distortion in training data - biased data - can lead to discriminatory decisions. In this paper, we developed a workflow for detecting and mitigating bias in data using a shallow, interpretable machine learning models: the Generalized Matrix Learning Vector Quantization. We extent the approach by a relevance-based analysis to identify and reduce bias in the data. Combining similarity metric adaptation and relevance-based analysis, we can develop fair classification models that minimize the influence of bias in the data. Our results demonstrate that this method is effective in reducing bias in classification models and therefore supports fair decision-making.

Privacy-Preserving Machine Learning has become an important field in the age of Big Data and AI Hype . Methods like Differential Privacy and Homomorphic Encryption (HE) became key ideas to preserve privacy and to counter well-known attacks. Yet, practice shows, that both approaches are not without pitfalls. HE suffers under severe computational overhead, which makes the training of larger Machine Learning models as encrypted circuit not feasible. However, training shallow or sparse networks, like Prototype-Based Models may be realized. In this work, we present a proof-of-concept for the realization of Learning Vector Quantization - 1 (LVQ-1) - a shallow Nearest Prototype Classifier (NPC) - as an encrypted circuit by using TFHE as the encryption scheme. Our results indicate, that the feasibility is influenced by the dimensionality of the dataset and its respective encoding, but also that both, feasibility and performance, depend on the chosen distance function. Beyond our practical work, we provide an overview of TFHE and how LVQ may violate privacy.

With federated learning scenarios gaining popularity to outsource computational heavy tasks or to increase generalizability of machine learning models, there is also a rise of research in terms of the security and privacy of the respective data used for these tasks.While differential privacy is studied well for Learning Vector Quantization, we want to present steps towards Homomorphic Encryption.In this regard, we will show theoretically how LVQ-1 can be adapted to be compatible with the TFHE encryption scheme and present experimental results.

We propose a bias detection and mitigating scheme for data in the context of classification tasks based on learning vector quantizers (LVQ) as classifier.For this purpose generalized LVQ endowed with an advanced matrix adaptation scheme is used for bias detection.The bias removal from data is realized applying a nullspace data projection using the adjusted matrix.The usefulness of the approach is demonstrated and illustrated in terms of two real world datasets.* M.K. is

An interpretable approach to classification learning using cross-entropy loss is the Probabilistic Learning Vector Quantizer (PLVQ) as a robust prototype-based classifier.We propose a variant of the PLVQ, that allows the integration of domain knowledge.This strategy is becoming increasingly popular as a means of developing intelligent models that can enhance performance and gain acceptance from domain experts.In this paper, we put forth the idea of incorporating externally known class relations as supplementary information.We present theoretical aspects of the model and demonstrate its capabilities through numerical experiments.

In this work, we will consider how privacy for vector quantization models can be broken in a federated learning environment.We show how a potential attacker can expose data from the prototype updates without needing to know about the specific model used by exploiting the transparency of vector quantization.Finally, a 1-user environment example based on GLVQ will be shown.* R. S. is supported by grants of the

Approximation problems, and thus regression problems, have been widely considered as machine learning problems.A popular model to tackle such tasks are radial-basis-function networks (RBFN) and variants thereof.However, due to the global approximation scheme, RBFN, when trained in a supervised manner without additional constraints, may lack local representation.To this end, we propose approaches that aim to preserve locality in terms of the regression problem by using the Neural Gas algorithm.The models are tested on different data sets and compared to the supervised RBFN approach.* R. S. is supported by

One-class-classification remains an important problem in machine learning, which is related to data representation and outlier detection, but different from them in several aspects. In the present contribution we propose an one-class-classifier based on a prototype vector quantization model. We modeled a corresponding cost function to account for aspects of representation learning and to appropriately evaluate the one-class classifier. The prototype-based model ensures a local representation of the target class. After this introduction, we obtain an interpretable one-class classifier model. We demonstrate the capabilities of the approach by applying the classifier to illustrative toy data examples as well as on real data in a medical context.

In this paper we present a realization of the informationbottleneck-paradigm by means of an improved counter propagation network.It combines an unsupervised vector quantizer for data compression with a subsequent supervised learning vector quantization model.The approach is mathematically justified and yields an interpretable model for classification under the constraint of data compression, which is not longer independently learned from the classification task.* M.K., M.