ML-Plan for Unlimited-Length Machine Learning Pipelines

By Marcel Wever, Felix Mohr, Eyke Hüllermeier. 2018

23 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Marc-André Zöller
2. Marco F. Huber
3. Maryam Ashoori
4. Yufan Jiang
5. Li Yang
6. Xin He
7. Tien-Dung Nguyen

Citations in the corpus

• Benchmark and Survey of Automated Machine Learning Frameworks (2019, background): “A benchmarking of other frameworks capable of building flexible pipelines, e.g., ML-Plan (Mohr et al., 2018; Wever et al., 2018) or P4ML (Gil et al., 2018), in combination with longer optimization periods is desirable to understand the capabilities of creating adaptable pipelines better.”
• Survey on Automated Machine Learning (2019, methodology): “1 ML-Plan ML-Plan (Mohr et al., 2018; Wever et al., 2018) is a framework for building arbitrary shaped ML pipelines based on HTNs.”
• Meta-Learning: A Survey (2018, neutral): “, 2018), and can be leveraged in larger pipeline construction approaches, such as planning (Nguyen et al., 2014; Kietz et al., 2012; Gil et al., 2018; Wever et al., 2018) or evolutionary techniques (Olson et al.”