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Incremental Search Space Construction for Machine Learning Pipeline Synthesis [article]

Marc-André Zöller, Tien-Dung Nguyen, Marco F. Huber
2021 arXiv   pre-print
Automated machine learning (AutoML) aims for constructing machine learning (ML) pipelines automatically.  ...  However, methods for ML pipeline synthesis and optimization considering the impact of complex pipeline structures containing multiple preprocessing and classification algorithms have not been studied thoroughly  ...  Section 3 describes how we model the pipeline synthesis and the creation of the meta-learning base.  ... 
arXiv:2101.10951v1 fatcat:2jtf7ljkgfcfhojqzwmrnrplty

PipelineProfiler: A Visual Analytics Tool for the Exploration of AutoML Pipelines [article]

Jorge Piazentin Ono, Sonia Castelo, Roque Lopez, Enrico Bertini, Juliana Freire, Claudio Silva
2020 arXiv   pre-print
In recent years, a wide variety of automated machine learning (AutoML) methods have been proposed to search and generate end-to-end learning pipelines.  ...  In this paper, we present the PipelineProfiler, an interactive visualization tool that allows the exploration and comparison of the solution space of machine learning (ML) pipelines produced by AutoML  ...  AlphaD3M uses deep learning to learn how to incrementally construct ML pipelines, framing the problem of pipeline synthesis for model discovery as a single-player game with a neural network sequence model  ... 
arXiv:2005.00160v2 fatcat:36fp5pu6tjeudoj7m6puateme4

Self-Service Data Science in Healthcare with Automated Machine Learning

Richard Ooms, Marco Spruit
2020 Applied Sciences  
(1) Background: This work investigates whether and how researcher-physicians can be supported in their knowledge discovery process by employing Automated Machine Learning (AutoML). (2) Methods: We take  ...  a design science research approach and select the Tree-based Pipeline Optimization Tool (TPOT) as the AutoML method based on a benchmark test and requirements from researcher-physicians.  ...  The AutoML community aims to automate all steps in the process of creating a machine learning pipeline.  ... 
doi:10.3390/app10092992 fatcat:blp4a576hnemtn3to7bf4aa2me

The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development [article]

Micah J. Smith, Carles Sala, James Max Kanter, Kalyan Veeramachaneni
2019 arXiv   pre-print
To address these problems, we introduce the Machine Learning Bazaar, a new approach to developing machine learning and automated machine learning software systems.  ...  As machine learning is applied more widely, data scientists often struggle to find or create end-to-end machine learning systems for specific tasks.  ...  AlphaD3M [16] formulates a pipeline synthesis problem and uses reinforcement learning to construct pipelines.  ... 
arXiv:1905.08942v3 fatcat:7m7urx74hrfvdjgrgo6ttyjn6y

Ensemble Squared: A Meta AutoML System [article]

Jason Yoo, Tony Joseph, Dylan Yung, S. Ali Nasseri, Frank Wood
2021 arXiv   pre-print
in machine learning.  ...  There are currently many barriers that prevent non-experts from exploiting machine learning solutions ranging from the lack of intuition on statistical learning techniques to the trickiness of hyperparameter  ...  FA8750-19-2-0222) and Learning with Less Labels (LwLL) program (Contract No.FA8750-19-C-0515).  ... 
arXiv:2012.05390v3 fatcat:3kffo3t4tzf3zm26zbcjq6khte

Solving Constrained CASH Problems with ADMM [article]

Parikshit Ram, Sijia Liu, Deepak Vijaykeerthi, Dakuo Wang, Djallel Bouneffouf, Greg Bramble, Horst Samulowitz, Alexander G. Gray
2020 arXiv   pre-print
The CASH problem has been widely studied in the context of automated configurations of machine learning (ML) pipelines and various solvers and toolkits are available.  ...  Alphad3m: Machine learning pipeline synthesis. In AutoML Workshop at ICML, 2018.A. Durand and C. Gagné. Thompson sampling for combinatorial bandits and its application to online feature selection.  ...  Automated ML Pipeline Configuration Hyper-parameter optimization (HPO) for a single machine learning (ML) algorithm is widely studied in AutoML (Snoek et al., 2012; Shahriari et al., 2016) .  ... 
arXiv:2006.09635v2 fatcat:ldiqvg57bjah7lvu56rbz5u5j4

Meta-Learning [chapter]

Joaquin Vanschoren
2019 Automated Machine Learning  
Not only does this dramatically speed up and improve the design of machine learning pipelines or neural architectures, it also allows us to replace hand-engineered algorithms with novel approaches learned  ...  Likewise, when building machine learning models for a specific task, we often build on experience with related tasks, or use our (often implicit) understanding of the behavior of machine learning techniques  ...  Pipeline Synthesis When creating entire machine learning pipelines [153] , the number of configuration options grows dramatically, making it even more important to leverage prior experience.  ... 
doi:10.1007/978-3-030-05318-5_2 fatcat:hkfdkzimpzhvpcj6fwmdpnfpbm

Meta-Learning: A Survey [article]

Joaquin Vanschoren
2018 arXiv   pre-print
Not only does this dramatically speed up and improve the design of machine learning pipelines or neural architectures, it also allows us to replace hand-engineered algorithms with novel approaches learned  ...  Meta-learning, or learning to learn, is the science of systematically observing how different machine learning approaches perform on a wide range of learning tasks, and then learning from this experience  ...  Pipeline Synthesis When creating entire machine learning pipelines (Serban et al., 2013) , the number of configuration options grows dramatically, making it even more important to leverage prior experience  ... 
arXiv:1810.03548v1 fatcat:b64vk5jcn5dzrnshzkth4m7zqu

AutoML to Date and Beyond: Challenges and Opportunities [article]

Shubhra Kanti Karmaker Santu, Md. Mahadi Hassan, Micah J. Smith, Lei Xu, ChengXiang Zhai, Kalyan Veeramachaneni
2021 arXiv   pre-print
We begin by describing what an end-to-end machine learning pipeline actually looks like, and which subtasks of the machine learning pipeline have been automated so far.  ...  AutoML tools aim to make machine learning accessible for non-machine learning experts (domain experts), to improve the efficiency of machine learning, and to accelerate machine learning research.  ...  It has inspired several other tools, such as AlphaD3M, an AutoML system based on edit operations performed over machine learning pipeline primitives (Drori et al.  ... 
arXiv:2010.10777v4 fatcat:arixmky6erdvhnmboe2sfgbb7a

The Roles and Modes of Human Interactions with Automated Machine Learning Systems [article]

Thanh Tung Khuat, David Jacob Kedziora, Bogdan Gabrys
2022 arXiv   pre-print
As automated machine learning (AutoML) systems continue to progress in both sophistication and performance, it becomes important to understand the 'how' and 'why' of human-computer interaction (HCI) within  ...  (iv) As AutoML systems become more autonomous and capable of learning from complex open-ended environments, will the fundamental nature of HCI evolve?  ...  In some cases, new learning pipelines will be constructed.  ... 
arXiv:2205.04139v1 fatcat:kfo3oybsjvfs5gjyt2j3dbaali