The research activities of our workgroup are focused on machine learning, a scientific discipline in the intersection of computer science, statistics, and applied mathematics, the importance of which has continuously grown in the recent past. Meanwhile, machine learning has developed into one of the main pillars of modern artificial intelligence as well as the emerging research field of data science.
Extensions of Supervised Learning
Many of our research works are dealing with extensions or generalizations of the standard setting of supervised learning. For example, while machine learning methods typically assume data to be represented in vectorial form, representations in terms of structured objects, such as graphs, sequences, or order relations, appear to be more natural in many applications. Moreover, representations in terms of sets or distributions are important to capture uncertainty and imprecision. Developing algorithms for learning from such kind of data is specifically challenging. Our activities in this field include research on machine learning methods for structured output and multi-target prediction, predictive modelling for complex structures, weakly supervised learning and so-called preference learning.
Online Learning and Data Streams
Another focus of our research is online learning in dynamic environments, including bandit algorithms, reinforcement learning, and learning on data streams. In contrast to the standard batch setting, in which the entire training data is assumed to be available a priori, these settings require incremental algorithms for learning on continuous and potentially unbounded streams of data. Thus, the training and prediction phase are no longer separated but tightly interleaved. The development of algorithms for online learning is especially challenging due to various constraints the learner needs to obey, such as bounded time and memory resources (adaptation and prediction must be fast, perhaps in real-time, and data cannot be stored in its entirety). Besides, learning algorithms must be able to react to possibly changing environmental conditions, including changes of the underlying data-generating process.
Uncertainty in Machine Learning
Machine learning is essentially concerned with extracting models from data and using these models to make predictions. As such, it is inseparably connected with uncertainty. Indeed, learning in the sense of generalizing beyond the data seen so far is necessarily based on a process of induction, i.e., replacing specific observations by general models of the data-generating process. Such models are always hypothetical, and the same holds true for the predictions produced by a model. In addition to the uncertainty inherent in inductive inference, other sources of uncertainty exist, including incorrect model assumptions and noisy data. Our research addresses questions regarding appropriate representations of uncertainty in machine learning, how to learn from uncertain and imprecise data, and how to produce reliable predictions in safety-critical applications.
Although the focus of our research is on theoretical foundations and methodological problems, we are also interested in practical applications of machine learning and artificial intelligence. Jointly with colleagues from other disciplines, we have been working on applications in engineering, economics, the life sciences, and the humanities. Besides, we are also collaborating with partners from industry.
Social and Societal Implications
Artificial intelligence and machine learning have a far-reaching influence on our society. Being aware of the potential impact of algorithms for data analytics and automated decision making on people and daily life, we critically analyse the implications of AI research together with colleagues from the social sciences.