Institutsseminar/2023-03-24
| Datum | Freitag, 24. März 2023 | |
|---|---|---|
| Uhrzeit | 11:30 – 12:30 Uhr (Dauer: 60 min) | |
| Ort | Raum 301 (Gebäude 50.34) | |
| Webkonferenz | https://kit-lecture.zoom.us/j/67744231815 | |
| Vorheriger Termin | Do 16. Februar 2023 | |
| Nächster Termin | Do 9. März 2023 |
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Vorträge
| Vortragende(r) | Yannick Ettwein |
|---|---|
| Titel | Explainable Artificial Intelligence for Decision Support |
| Vortragstyp | Bachelorarbeit |
| Betreuer(in) | Vadim Arzamasov |
| Vortragssprache | |
| Vortragsmodus | in Präsenz |
| Kurzfassung | Policy makers face the difficult task to make far-reaching decisions that impact the life of the the entire population based on uncertain parameters that they have little to no control
over, such as environmental impacts. Often, they use scenarios in their decision making process. Scenarios provide a common and intuitive way to communicate and characterize different uncertain outcomes in many decision support applications, especially in broad public debates. However, they often fall short of their potential, particularly when applied for groups with diverse interests and worldviews, due to the difficulty of choosing a small number of scenarios to summarize the entire range of uncertain future outcomes. Scenario discovery addresses these problems by using statistical or data-mining algorithms to find easy-to-interpret, policy-relevant regions in the space of uncertain input parameters of computer simulation models. One of many approaches to scenario discovery is subgroup discovery, an approach from the domain of explainable Artificial Intelligence. In this thesis, we test and evaluate multiple different subgroup discovery methods for their applicabilty to scenario discovery applications. |
| Vortragende(r) | Georg Gntuni |
|---|---|
| Titel | Streaming MMD Change Detection |
| Vortragstyp | Bachelorarbeit |
| Betreuer(in) | Florian Kalinke |
| Vortragssprache | |
| Vortragsmodus | in Präsenz |
| Kurzfassung | Kernel methods are among the most well-known approaches in data science. Their ability to represent probability distributions as elements in a reproducing kernel Hilbert space gives rise to maximum mean discrepancy (MMD). MMD quantifies the dissimilarity of two distributions and allows powerful two-sample tests on many domains. One important application of general two-sample tests is change detection in data streams: Here, one tests the null hypothesis that the distributions of data within the stream do not change versus the alternative hypothesis that the distributions do change; a change in distribution then indicates a change point. The broad applicability of kernel-based two-sample tests renders their use for change detection in data streams highly desirable. But, their quadratic runtime complexity prohibits their application. While approximations for kernel methods that reduce their runtime in the static setting exist, their application to data streams is challenging.
In this thesis, we propose a novel change detector, RADMAN, which leverages the random Fourier feature-based kernel approximation to efficiently detect changes in data streams with a polylogarithmic runtime complexity of O(log^2 n) per insert operation, with n the total number of observations. The proposed approach runs significantly faster than existing methods but obtains similar result quality. Our experiments on synthetic and real-world data sets show that it performs better than current state-of-the-art approaches. |
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