ATM: A distributed, collaborative, scalable system for automated machine learning

By Thomas Swearingen, Will Drevo, Bennett Cyphers, Alfredo Cuesta-Infante, Arun A. Ross, Kalyan Veeramachaneni. 2017

109 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, methodology): “Figure 7 illustrates the architectures of the Auto-Tuned Models (ATM) framework(17) that has been introduced as a parallel framework for fast optimization of machine learning modeling pipelines [119].”
Benchmark and Survey of Automated Machine Learning Frameworks (2019, methodology): “ATM is evaluated in version 0.2.2.”
Survey on Automated Machine Learning (2019, methodology): “Various open-source designs for AutoML services have been proposed, e.g., (Sparks et al., 2015; Chan, 2017; Swearingen et al., 2017; Koch et al., 2018), but also several commercial solutions exist, e.g., (Golovin et al., 2017; Clouder, 2018; H2O.ai, 2018).”
Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar (2019, methodology): “Existing AutoML systems use methods such as diﬀerentiable programming (Milutinovic et al., 2017), tree search (Swearingen et al., 2017), evolutionary algorithm (Olson and Moore, 2016; Chen et al., 2018; Gijsbers et al., 2017), Bayesian optimization (Rasmussen, 2003; Bergstra and Bengio, 2012;…”
Working with Beliefs: AI Transparency in the Enterprise (2018, background): “In [22], researchers present a new system that automates the model selection step, even improving on human performance.”
Neural Architecture Search over Decentralized Data (2020, background): “A few efforts have proposed distributed systems [12, 26, 34] for automated machine learning tasks.”
AutoML to Date and Beyond: Challenges and Opportunities (2020, methodology): “Swearingen et al. [59] introduced a machine learning service named ATM that can be hosted on any distributed computing infrastructure.”
Automated Machine Learning in Practice: State of the Art and Recent Results (2019, methodology): “The ATM framework [42] finally uses multi-armed bandit optimization in combination with hybrid Bayesian optimization to find the best models.”
Federated Neural Architecture Search (2020, background): “A few efforts have proposed distributed systems [14, 32, 44] for automated machine learning tasks.”
Bayesian tuning and bandits : an extensible, open source library for AutoML (2018, methodology): “The selector API was established in the ATM paper, and was not modified during this thesis [29].”
AlphaD3M: Machine Learning Pipeline Synthesis (2021, background): “DARPA’s Data Driven Discovery of Models (D3M) program pushes this vision further and proposes to develop infrastructure to automate model discovery, i.e., solve any task on any dataset specified by the user.”
Rafiki: Machine Learning as an Analytics Service System (2018, background): “Study with BO is actually the same as ATM [31].”