Prof. Hüllermeier recently accepted an offer by the University of Munich (LMU) and left Paderborn University. Therefore, these websites are no longer maintained. Visit the website of the Chair of Artificial Intelligence and Machine Learning at LMU.
Students looking for a Master's or Bachelor's thesis in the area of "Intelligence and Data" should contact related workgroups:
Welcome to the Intelligent Systems and Machine Learning Group
The research activities of our group are focused on machine learning, a scientific discipline in the intersection of computer science, statistics, and applied mathematics. Over the last decades, the importance of machine learning has continuously grown, and meanwhile, the field has developed into one of the main pillars of modern artificial intelligence as well as the emerging research field of data science.
Uncertainty and Preference in Machine Learning
Much of our research centers around two key cognitive concepts of artificial intelligence: uncertainty and preference.
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.
The notion of "preference" has a long tradition in economics and operational research, where it has been formalised in various ways and studied extensively from different points of view. Nowadays, it is a topic of key importance in artificial intelligence, where is serves as a basic formalism for knowledge representation and problem solving. The emerging field of preference learning is concerned with methods for learning preference models from explicit or implicit preference information, which are typically used for predicting the preferences of an individual or a group of individuals in new decision contexts. While research on preference learning has been specifically triggered by applications such as "learning to rank" for information retrieval (e.g., Internet search engines) and recommender systems, the methods developed in this field are useful in many other domains as well.
Extensions of Supervised Learning
Other 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, and weakly supervised 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.
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.
Intelligent Systems and Machine Learning