@inproceedings{29803,
  abstract     = {{Ultrasonic wire bonding is a solid-state joining process used to form electrical interconnections in micro and
power electronics and batteries. A high frequency oscillation causes a metallurgical bond deformation in
the contact area. Due to the numerous physical influencing factors, it is very difficult to accurately capture
this process in a model. Therefore, our goal is to determine a suitable feed-forward control strategy for the
bonding process even without detailed model knowledge. We propose the use of batch constrained Bayesian
optimization for the control design. Hence, Bayesian optimization is precisely adapted to the application of
bonding: the constraint is used to check one quality feature of the process and the use of batches leads to
more efficient experiments. Our approach is suitable to determine a feed-forward control for the bonding
process that provides very high quality bonds without using a physical model. We also show that the quality
of the Bayesian optimization based control outperforms random search as well as manual search by a user.
Using a simple prior knowledge model derived from data further improves the quality of the connection.
The Bayesian optimization approach offers the possibility to perform a sensitivity analysis of the control
parameters, which allows to evaluate the influence of each control parameter on the bond quality. In summary,
Bayesian optimization applied to the bonding process provides an excellent opportunity to develop a feedforward
control without full modeling of the underlying physical processes.}},
  author       = {{Hesse, Michael and Hunstig, Matthias and Timmermann, Julia and Trächtler, Ansgar}},
  booktitle    = {{Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM)}},
  isbn         = {{978-989-758-549-4}},
  keywords     = {{Bayesian optimization, Wire bonding, Feed-forward control, model-free design}},
  location     = {{Online}},
  pages        = {{383--394}},
  title        = {{{Batch Constrained Bayesian Optimization for UltrasonicWire Bonding Feed-forward Control Design}}},
  year         = {{2022}},
}

@article{21004,
  abstract     = {{Automated machine learning (AutoML) supports the algorithmic construction and data-specific customization of machine learning pipelines, including the selection, combination, and parametrization of machine learning algorithms as main constituents. Generally speaking, AutoML approaches comprise two major components: a search space model and an optimizer for traversing the space. Recent approaches have shown impressive results in the realm of supervised learning, most notably (single-label) classification (SLC). Moreover, first attempts at extending these approaches towards multi-label classification (MLC) have been made. While the space of candidate pipelines is already huge in SLC, the complexity of the search space is raised to an even higher power in MLC. One may wonder, therefore, whether and to what extent optimizers established for SLC can scale to this increased complexity, and how they compare to each other. This paper makes the following contributions: First, we survey existing approaches to AutoML for MLC. Second, we augment these approaches with optimizers not previously tried for MLC. Third, we propose a benchmarking framework that supports a fair and systematic comparison. Fourth, we conduct an extensive experimental study, evaluating the methods on a suite of MLC problems. We find a grammar-based best-first search to compare favorably to other optimizers.}},
  author       = {{Wever, Marcel Dominik and Tornede, Alexander and Mohr, Felix and Hüllermeier, Eyke}},
  issn         = {{0162-8828}},
  journal      = {{IEEE Transactions on Pattern Analysis and Machine Intelligence}},
  keywords     = {{Automated Machine Learning, Multi Label Classification, Hierarchical Planning, Bayesian Optimization}},
  pages        = {{1--1}},
  title        = {{{AutoML for Multi-Label Classification: Overview and Empirical Evaluation}}},
  doi          = {{10.1109/tpami.2021.3051276}},
  year         = {{2021}},
}