Eldorado Collection:
http://hdl.handle.net/2003/41204
2024-03-28T17:05:13ZVerification of unsupervised neural networks
http://hdl.handle.net/2003/42030
Title: Verification of unsupervised neural networks
Authors: Böing, Benedikt
Abstract: Neural networks are at the forefront of machine learning being responsible for
achievements such as AlphaGo. As they are being deployed in more and more
environments - even in safety-critical ones such as health care - we are naturally
interested in assuring their reliability. However, the discovery of so-called adver-
sarial attacks for supervised neural networks demonstrated that tiny distortions
in the input space can lead to misclassifications and thus, to potentially catas-
trophic errors: Patients could be diagnosed wrongly, or a car might confuse stop
signs and traffic lights. Thus, ideally, we would like to guarantee that these types
of attacks cannot occur.
In this thesis we extend the research on reliable neural networks to the realm
of unsupervised learning. This includes defining proper notions of reliability,
as well as analyzing and adapting unsupervised neural networks with respect
to this notion. Our definitions of reliability depend on the underlying neural
networks and the problems they are meant to solve. However, in all our cases, we
aim for guarantees on a continuous input space containing infinitely many points.
Therefore we extend the traditional setting of testing against a finite dataset such
that we require specialized tools to actually check a given network for reliability.
We will demonstrate how we can leverage neural network verification for these
purposes. Using neural network verification, however, entails a major challenge:
It does not scale up to large networks. To overcome this limitation, we design a
novel training procedure yielding networks that are both more reliable according
to our definition as well as more amenable for neural network verification. By
exploiting the piecewise affine structure of our networks, we can locally simplify
them and thus decrease verification runtime significantly. We also take a per-
spective that complements a neural network’s training by exploring how we can
repair non-reliable neural network ensembles. With this thesis, we paradigmatically show the necessity and the complications of unsupervised neural network verification. It aims to pave the way for more research to come and towards a safe usage of these simple-to-build yet difficult-to-understand models given by unsupervised neural networks.2023-01-01T00:00:00ZEvent impact analysis for time series
http://hdl.handle.net/2003/41207
Title: Event impact analysis for time series
Authors: Scharwächter, Erik
Abstract: Time series arise in a variety of application domains—whenever data points are recorded over time and stored for subsequent analysis. A critical question is whether the occurrence of events like natural disasters, technical faults, or political interventions leads to changes in a time series, for example, temporary deviations from its typical behavior. The vast majority of existing research on this topic focuses on the specific impact of a single event on a time series, while methods to generically capture the impact of a recurring event are scarce. In this thesis, we fill this gap by introducing a novel framework for event impact analysis in the case of randomly recurring events. We develop a statistical perspective on the problem and provide a generic notion of event impacts based on a statistical independence relation. The main problem we address is that of establishing the presence of event impacts in stationary time series using statistical independence tests. Tests for event impacts should be generic, powerful, and computationally efficient. We develop two algorithmic test strategies for event impacts that satisfy these properties. The first is based on coincidences between events and peaks in the time series, while the second is based on multiple marginal associations. We also discuss a selection of follow-up questions, including ways to measure, model and visualize event impacts, and the relationship between event impact analysis and anomaly detection in time series. At last, we provide a first method to study event impacts in nonstationary time series. We evaluate our methodological contributions on several real-world datasets and study their performance within large-scale simulation studies.2022-01-01T00:00:00Z