Barbara Hammer

Execution Traces as a Powerful Data Representation for Intelligent Tutoring Systems for Programming

2016 · PUB – Publications at Bielefeld University (Bielefeld University)

The first intelligent tutoring systems for computer programming have been proposed more than 30 years ago, mostly focusing on well defined programming tasks e.g. in the context of logic programming. Recent systems also teach complex …

Statistical Mechanics of On-Line Learning Under Concept Drift

2018 · Entropy

We introduce a modeling framework for the investigation of on-line machine learning processes in non-stationary environments. We exemplify the approach in terms of two specific model situations: In the first, we consider the learning of …

Progressive Data Science: Potential and Challenges

2018 · arXiv (Cornell University)

Data science requires time-consuming iterative manual activities. In particular, activities such as data selection, preprocessing, transformation, and mining, highly depend on iterative trial-and-error processes that could be sped-up significantly by providing quick feedback on the …

Adversarial Attacks Hidden in Plain Sight

2020 · Lecture notes in computer science

Convolutional neural networks have been used to achieve a string of successes during recent years, but their lack of interpretability remains a serious issue. Adversarial examples are designed to deliberately fool neural networks into making …

Localization of Concept Drift: Identifying the Drifting Datapoints

2022 · 2022 International Joint Conference on Neural Networks (IJCNN)

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 …

One or two things we know about concept drift—a survey on monitoring in evolving environments. Part B: locating and explaining concept drift

2024 · Frontiers in Artificial Intelligence

In an increasing number of industrial and technical processes, machine learning-based systems are being entrusted with supervision tasks. While they have been successfully utilized in many application areas, they frequently are not able to generalize …

Continuous Fair SMOTE -- Fairness-Aware Stream Learning from Imbalanced Data

2025 · arXiv (Cornell University)

As machine learning is increasingly applied in an online fashion to deal with evolving data streams, the fairness of these algorithms is a matter of growing ethical and legal concern. In many use cases, class …