Arrow Research search

Author name cluster

Marcel Wever

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

7 papers
2 author rows

Possible papers

7

AAAI Conference 2026 Conference Paper

HyperSHAP: Shapley Values and Interactions for Explaining Hyperparameter Optimization

  • Marcel Wever
  • Maximilian Muschalik
  • Fabian Fumagalli
  • Marius Lindauer

Hyperparameter optimization (HPO) is a crucial step in achieving strong predictive performance. Yet, the impact of individual hyperparameters on model generalization is highly context-dependent, prohibiting a one-size-fits-all solution and requiring opaque HPO methods to find optimal configurations. However, the black-box nature of most HPO methods undermines user trust and discourages adoption. To address this, we propose a game-theoretic explainability framework for HPO based on Shapley values and interactions. Our approach provides an additive decomposition of a performance measure across hyperparameters, enabling local and global explanations of hyperparameters' contributions and their interactions. The framework, named HyperSHAP, offers insights into ablation studies, the tunability of learning algorithms, and optimizer behavior across different hyperparameter spaces. We demonstrate HyperSHAP's capabilities on various HPO benchmarks to analyze the interaction structure of the corresponding HPO problems, demonstrating its broad applicability and actionable insights for improving HPO.

PDF Details DOI

JMLR Journal 2025 Journal Article

DeepCAVE: A Visualization and Analysis Tool for Automated Machine Learning

  • Sarah Segel
  • Helena Graf
  • Edward Bergman
  • Kristina Thieme
  • Marcel Wever
  • Alexander Tornede
  • Frank Hutter
  • Marius Lindauer

Hyperparameter optimization (HPO), as a central paradigm of AutoML, is crucial for leveraging the full potential of machine learning (ML) models; yet its complexity poses challenges in understanding and debugging the optimization process. We present DeepCAVE, a tool for interactive visualization and analysis, providing insights into HPO. Through an interactive dashboard, researchers, data scientists, and ML engineers can explore various aspects of the HPO process and identify issues, untouched potentials, and new insights about the ML model being tuned. By empowering users with actionable insights, DeepCAVE contributes to the interpretability of HPO and ML on a design level and aims to foster the development of more robust and efficient methodologies in the future. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2025. ( edit, beta )

PDF Details

IJCAI Conference 2024 Conference Paper

Best Arm Identification with Retroactively Increased Sampling Budget for More Resource-Efficient HPO

  • Jasmin Brandt
  • Marcel Wever
  • Viktor Bengs
  • Eyke Hüllermeier

Hyperparameter optimization (HPO) is indispensable for achieving optimal performance in machine learning tasks. A popular class of methods in this regard is based on Successive Halving (SHA), which casts HPO into a pure-exploration multi-armed bandit problem under finite sampling budget constraints. This is accomplished by considering hyperparameter configurations as arms and rewards as the negative validation losses. While enjoying theoretical guarantees as well as working well in practice, SHA comes, however, with several hyperparameters itself, one of which is the maximum budget that can be allocated to evaluate a single arm (hyperparameter configuration). Although there are already solutions to this meta hyperparameter optimization problem, such as the doubling trick or asynchronous extensions of SHA, these are either practically inefficient or lack theoretical guarantees. In this paper, we propose incremental SHA (iSHA), a synchronous extension of SHA, allowing to increase the maximum budget a posteriori while still enjoying theoretical guarantees. Our empirical analysis of HPO problems corroborates our theoretical findings and shows that iSHA is more resource-efficient than existing SHA-based approaches.

PDF Details DOI

ICML Conference 2024 Conference Paper

Position: Why We Must Rethink Empirical Research in Machine Learning

  • Moritz Herrmann
  • F. Julian D. Lange
  • Katharina Eggensperger
  • Giuseppe Casalicchio
  • Marcel Wever
  • Matthias Feurer 0001
  • David Rügamer
  • Eyke Hüllermeier

We warn against a common but incomplete understanding of empirical research in machine learning that leads to non-replicable results, makes findings unreliable, and threatens to undermine progress in the field. To overcome this alarming situation, we call for more awareness of the plurality of ways of gaining knowledge experimentally but also of some epistemic limitations. In particular, we argue most current empirical machine learning research is fashioned as confirmatory research while it should rather be considered exploratory.

Details

IJCAI Conference 2023 Conference Paper

A Survey of Methods for Automated Algorithm Configuration (Extended Abstract)

  • Elias Schede
  • Jasmin Brandt
  • Alexander Tornede
  • Marcel Wever
  • Viktor Bengs
  • Eyke Hüllermeier
  • Kevin Tierney

Algorithm configuration (AC) is concerned with the automated search of the most suitable parameter configuration of a parametrized algorithm. There are currently a wide variety of AC problem variants and methods proposed in the literature. Existing reviews do not take into account all derivatives of the AC problem, nor do they offer a complete classification scheme. To this end, we introduce taxonomies to describe the AC problem and features of configuration methods, respectively. Existing AC literature is classified and characterized by the provided taxonomies.

PDF Details DOI

JAIR Journal 2023 Journal Article

Towards Green Automated Machine Learning: Status Quo and Future Directions

  • Tanja Tornede
  • Alexander Tornede
  • Jonas Hanselle
  • Felix Mohr
  • Marcel Wever
  • Eyke Hüllermeier

Automated machine learning (AutoML) strives for the automatic configuration of machine learning algorithms and their composition into an overall (software) solution — a machine learning pipeline — tailored to the learning task (dataset) at hand. Over the last decade, AutoML has developed into an independent research field with hundreds of contributions. At the same time, AutoML is being criticized for its high resource consumption as many approaches rely on the (costly) evaluation of many machine learning pipelines, as well as the expensive large-scale experiments across many datasets and approaches. In the spirit of recent work on Green AI, this paper proposes Green AutoML, a paradigm to make the whole AutoML process more environmentally friendly. Therefore, we first elaborate on how to quantify the environmental footprint of an AutoML tool. Afterward, different strategies on how to design and benchmark an AutoML tool w.r.t. their “greenness”, i.e., sustainability, are summarized. Finally, we elaborate on how to be transparent about the environmental footprint and what kind of research incentives could direct the community in a more sustainable AutoML research direction. As part of this, we propose a sustainability checklist to be attached to every AutoML paper featuring all core aspects of Green AutoML.

PDF Details DOI

JAIR Journal 2022 Journal Article

A Survey of Methods for Automated Algorithm Configuration

  • Elias Schede
  • Jasmin Brandt
  • Alexander Tornede
  • Marcel Wever
  • Viktor Bengs
  • Eyke Hüllermeier
  • Kevin Tierney

Algorithm configuration (AC) is concerned with the automated search of the most suitable parameter configuration of a parametrized algorithm. There is currently a wide variety of AC problem variants and methods proposed in the literature. Existing reviews do not take into account all derivatives of the AC problem, nor do they offer a complete classification scheme. To this end, we introduce taxonomies to describe the AC problem and features of configuration methods, respectively. We review existing AC literature within the lens of our taxonomies, outline relevant design choices of configuration approaches, contrast methods and problem variants against each other, and describe the state of AC in industry. Finally, our review provides researchers and practitioners with a look at future research directions in the field of AC.

PDF Details DOI
v2026.09.13