@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}},
}

@inproceedings{20693,
  abstract     = {{In practical, large-scale networks, services are requested
by users across the globe, e.g., for video streaming.
Services consist of multiple interconnected components such as
microservices in a service mesh. Coordinating these services
requires scaling them according to continuously changing user
demand, deploying instances at the edge close to their users,
and routing traffic efficiently between users and connected instances.
Network and service coordination is commonly addressed
through centralized approaches, where a single coordinator
knows everything and coordinates the entire network globally.
While such centralized approaches can reach global optima, they
do not scale to large, realistic networks. In contrast, distributed
approaches scale well, but sacrifice solution quality due to their
limited scope of knowledge and coordination decisions.

To this end, we propose a hierarchical coordination approach
that combines the good solution quality of centralized approaches
with the scalability of distributed approaches. In doing so, we divide
the network into multiple hierarchical domains and optimize
coordination in a top-down manner. We compare our hierarchical
with a centralized approach in an extensive evaluation on a real-world
network topology. Our results indicate that hierarchical
coordination can find close-to-optimal solutions in a fraction of
the runtime of centralized approaches.}},
  author       = {{Schneider, Stefan Balthasar and Jürgens, Mirko and Karl, Holger}},
  booktitle    = {{IFIP/IEEE International Symposium on Integrated Network Management (IM)}},
  keywords     = {{network management, service management, coordination, hierarchical, scalability, nfv}},
  location     = {{Bordeaux, France}},
  publisher    = {{IFIP/IEEE}},
  title        = {{{Divide and Conquer: Hierarchical Network and Service Coordination}}},
  year         = {{2021}},
}

@article{3510,
  abstract     = {{Automated machine learning (AutoML) seeks to automatically select, compose, and parametrize machine learning algorithms, so as to achieve optimal performance on a given task (dataset). Although current approaches to AutoML have already produced impressive results, the field is still far from mature, and new techniques are still being developed. In this paper, we present ML-Plan, a new approach to AutoML based on hierarchical planning. To highlight the potential of this approach, we compare ML-Plan to the state-of-the-art frameworks Auto-WEKA, auto-sklearn, and TPOT. In an extensive series of experiments, we show that ML-Plan is highly competitive and often outperforms existing approaches.}},
  author       = {{Mohr, Felix and Wever, Marcel Dominik and Hüllermeier, Eyke}},
  issn         = {{1573-0565}},
  journal      = {{Machine Learning}},
  keywords     = {{AutoML, Hierarchical Planning, HTN planning, ML-Plan}},
  location     = {{Dublin, Ireland}},
  pages        = {{1495--1515}},
  publisher    = {{Springer}},
  title        = {{{ML-Plan: Automated Machine Learning via Hierarchical Planning}}},
  doi          = {{10.1007/s10994-018-5735-z}},
  year         = {{2018}},
}

@inproceedings{3852,
  abstract     = {{In automated machine learning (AutoML), the process of engineering machine learning applications with respect to a specific problem is (partially) automated.
Various AutoML tools have already been introduced to provide out-of-the-box machine learning functionality.
More specifically, by selecting machine learning algorithms and optimizing their hyperparameters, these tools produce a machine learning pipeline tailored to the problem at hand.
Except for TPOT, all of these tools restrict the maximum number of processing steps of such a pipeline.
However, as TPOT follows an evolutionary approach, it suffers from performance issues when dealing with larger datasets.
In this paper, we present an alternative approach leveraging a hierarchical planning to configure machine learning pipelines that are unlimited in length.
We evaluate our approach and find its performance to be competitive with other AutoML tools, including TPOT.}},
  author       = {{Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}},
  booktitle    = {{ICML 2018 AutoML Workshop}},
  keywords     = {{automated machine learning, complex pipelines, hierarchical planning}},
  location     = {{Stockholm, Sweden}},
  title        = {{{ML-Plan for Unlimited-Length Machine Learning Pipelines}}},
  year         = {{2018}},
}

@inproceedings{2109,
  abstract     = {{In multinomial classification, reduction techniques are commonly used to decompose the original learning problem into several simpler problems. For example, by recursively bisecting the original set of classes, so-called nested dichotomies define a set of binary classification problems that are organized in the structure of a binary tree. In contrast to the existing one-shot heuristics for constructing nested dichotomies and motivated by recent work on algorithm configuration, we propose a genetic algorithm for optimizing the structure of such dichotomies. A key component of this approach is the proposed genetic representation that facilitates the application of standard genetic operators, while still supporting the exchange of partial solutions under recombination. We evaluate the approach in an extensive experimental study, showing that it yields classifiers with superior generalization performance.}},
  author       = {{Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}},
  booktitle    = {{Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018}},
  keywords     = {{Classification, Hierarchical Decomposition, Indirect Encoding}},
  location     = {{Kyoto, Japan}},
  publisher    = {{ACM}},
  title        = {{{Ensembles of Evolved Nested Dichotomies for Classification}}},
  doi          = {{10.1145/3205455.3205562}},
  year         = {{2018}},
}

@inproceedings{11806,
  abstract     = {{Microphone arrays represent the basis for many challenging acoustic sensing tasks. The accuracy of techniques like beamforming directly depends on a precise knowledge of the relative positions of the sensors used. Unfortunately, for certain use cases manually measuring the geometry of an array is not feasible due to practical constraints. In this paper we present an approach to unsupervised shape calibration of microphone array networks. We developed a hierarchical procedure that first performs local shape calibration based on coherence analysis and then employs SRP-PHAT in a network calibration method. Practical experiments demonstrate the effectiveness of our approach especially for highly reverberant acoustic environments.}},
  author       = {{Hennecke, Marius and Ploetz, Thomas and Fink, Gernot A. and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}},
  booktitle    = {{IEEE/SP 15th Workshop on Statistical Signal Processing (SSP 2009)}},
  keywords     = {{acoustic sensing tasks, array geometry, calibration, coherence analysis, hierarchical procedure, local shape calibration, microphone array networks, microphone arrays, network calibration method, sensor arrays, SRP-PHAT, unsupervised shape calibration}},
  pages        = {{257--260}},
  title        = {{{A hierarchical approach to unsupervised shape calibration of microphone array networks}}},
  doi          = {{10.1109/SSP.2009.5278589}},
  year         = {{2009}},
}