ML-Plan: Automated machine learning via hierarchical planning

By Felix Mohr, Marcel Wever, Eyke Hüllermeier. 2018

182 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Automated Machine Learning: State-of-The-Art and Open Challenges (2019, background): “ML-Plan(12) has been proposed to tackle the composability challenge on building machine learning pipelines [89].”
Benchmark and Survey of Automated Machine Learning Frameworks (2019, background): “Using this abstraction, the problem of finding an ML pipeline structure is reduced to finding the best leaf node in the graph (Mohr et al., 2018).”
Survey on Automated Machine Learning (2019, methodology): “Using this abstraction, the problem of finding an ML pipeline structure is reduced to finding the best leaf node in the graph (Mohr et al., 2018).”
An Open Source AutoML Benchmark (2019, methodology): “Notable omissions from this list include autoxgboost (Thomas et al., 2018), which leverages Bayesian optimization to optimize gradient boosting models, OBOE (Yang et al., 2018), which uses low-rank approximation to predict the best pipelines, ML-Plan (Mohr et al., 2018), which optimizes WEKA-based p”
Automated Machine Learning: Methods, Systems, Challenges (2019, unknown): “, in FMS or CASH), where good configurations can be very diverse, which increases the potential gains from ensembling [4, 19, 31, 34].”
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data (2020, background): “Other AutoML Platforms worth mentioning include: autoxgboost (Thomas et al., 2018), GAMA (Gijsbers & Vanschoren, 2019), hyperopt-sklearn (Bergstra et al., 2015), TransmogrifAI (Moore et al., 2018), ML-Plan (Mohr et al., 2018), OBOE (Yang et al., 2018), Auto-Keras (Jin et al., 2019), as well as recen”
Auto-sklearn: Efficient and Robust Automated Machine Learning (2019, methodology): “Since the publication of the original NIPS paper [14] Auto-sklearn has become a standard baseline for new approaches to automated machine learning such as FLASH [39], RECIPE [32], Hyperband [25], Auto-Prognosis [2], ML-PLAN [28], Auto-Stacker [10] and AlphaD3M [11].”
ML-Plan for Unlimited-Length Machine Learning Pipelines (2018, methodology): “As briefly sketched above, ML-Plan is a hierarchical planner designed for AutoML problems (Mohr et al., 2018).”
Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges (2021, methodology): “Combining a graph with an efficient tuner is the key principle of AutoML (Feurer, Klein, et al., 2015a; Mohr et al., 2018; Olson et al., 2016).”
OpenML Benchmarking Suites (2017, methodology): “This selection of tasks was still used in, e.g., Mohr et al. [2018] and consisted of 21 problems, a third of which are image classiﬁcation tasks which are typically not the intended use-case for the AutoML tools.”