A Scalable AutoML Approach Based on Graph Neural Networks

• URL: http://arxiv.org/abs/2111.00083v1
• Date: Fri, 29 Oct 2021 20:55:13 GMT
• Title: A Scalable AutoML Approach Based on Graph Neural Networks
• Authors: Mossad Helali, Essam Mansour, Ibrahim Abdelaziz, Julian Dolby, Kavitha Srinivas
• Abstract summary: KGpip is designed as a sub-component for AutoML systems. We demonstrate this ability via integrating KGpip with two AutoML systems and show that it does significantly enhance the performance of existing state-of-the-art systems.
• Score: 4.723269144709768
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: AutoML systems build machine learning models automatically by performing a search over valid data transformations and learners, along with hyper-parameter optimization for each learner. We present a system called KGpip for the selection of transformations and learners, which (1) builds a database of datasets and corresponding historically used pipelines using effective static analysis instead of the typical use of actual runtime information, (2) uses dataset embeddings to find similar datasets in the database based on its content instead of metadata-based features, (3) models AutoML pipeline creation as a graph generation problem, to succinctly characterize the diverse pipelines seen for a single dataset. KGpip is designed as a sub-component for AutoML systems. We demonstrate this ability via integrating KGpip with two AutoML systems and show that it does significantly enhance the performance of existing state-of-the-art systems.

Related papers

• Data-Juicer 2.0: Cloud-Scale Adaptive Data Processing for and with Foundation Models [64.28420991770382]
  Data-Juicer 2.0 is a data processing system backed by data processing operators spanning text, image, video, and audio modalities.<n>It supports more critical tasks including data analysis, annotation, and foundation model post-training.<n>It has been widely adopted in diverse research fields and real-world products such as Alibaba Cloud PAI.
  arXiv  Detail & Related papers  (2024-12-23T08:29:57Z)
• AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML [56.565200973244146]
  Automated machine learning (AutoML) accelerates AI development by automating tasks in the development pipeline. Recent works have started exploiting large language models (LLM) to lessen such burden. This paper proposes AutoML-Agent, a novel multi-agent framework tailored for full-pipeline AutoML.
  arXiv  Detail & Related papers  (2024-10-03T20:01:09Z)
• Zero-shot Composed Text-Image Retrieval [72.43790281036584]
  We consider the problem of composed image retrieval (CIR) It aims to train a model that can fuse multi-modal information, e.g., text and images, to accurately retrieve images that match the query, extending the user's expression ability.
  arXiv  Detail & Related papers  (2023-06-12T17:56:01Z)
• Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering [52.09178018466104]
  We introduce Context-Aware Automated Feature Engineering (CAAFE) to generate semantically meaningful features for datasets. Despite being methodologically simple, CAAFE improves performance on 11 out of 14 datasets. We highlight the significance of context-aware solutions that can extend the scope of AutoML systems to semantic AutoML.
  arXiv  Detail & Related papers  (2023-05-05T09:58:40Z)
• AutoEn: An AutoML method based on ensembles of predefined Machine Learning pipelines for supervised Traffic Forecasting [1.6242924916178283]
  Traffic Forecasting (TF) is gaining relevance due to its ability to mitigate traffic congestion by forecasting future traffic states. TF poses one big challenge to the Machine Learning paradigm, known as the Model Selection Problem (MSP) We introduce AutoEn, which is a simple and efficient method for automatically generating multi-classifier ensembles from a predefined set of ML pipelines.
  arXiv  Detail & Related papers  (2023-03-19T18:37:18Z)
• SapientML: Synthesizing Machine Learning Pipelines by Learning from Human-Written Solutions [28.718446733713183]
  We propose an AutoML SapientML that can learn from a corpus of existing datasets and their human-written pipelines. We have created a training corpus of 1094 pipelines spanning 170 datasets, and evaluated SapientML on a set of 41 benchmark datasets. Our evaluation shows that SapientML produces the best or comparable accuracy on 27 of the benchmarks while the second best tool fails to even produce a pipeline on 9 of the instances.
  arXiv  Detail & Related papers  (2022-02-18T20:45:47Z)
• SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
  In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines. This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
  arXiv  Detail & Related papers  (2021-12-22T14:45:37Z)
• Benchmarking Multimodal AutoML for Tabular Data with Text Fields [83.43249184357053]
  We assemble 18 multimodal data tables that each contain some text fields. Our benchmark enables researchers to evaluate their own methods for supervised learning with numeric, categorical, and text features.
  arXiv  Detail & Related papers  (2021-11-04T09:29:16Z)
• Data Augmentation for Abstractive Query-Focused Multi-Document Summarization [129.96147867496205]
  We present two QMDS training datasets, which we construct using two data augmentation methods. These two datasets have complementary properties, i.e., QMDSCNN has real summaries but queries are simulated, while QMDSIR has real queries but simulated summaries. We build end-to-end neural network models on the combined datasets that yield new state-of-the-art transfer results on DUC datasets.
  arXiv  Detail & Related papers  (2021-03-02T16:57:01Z)
• Incremental Search Space Construction for Machine Learning Pipeline Synthesis [4.060731229044571]
  Automated machine learning (AutoML) aims for constructing machine learning (ML) pipelines automatically. We propose a data-centric approach based on meta-features for pipeline construction. We prove the effectiveness and competitiveness of our approach on 28 data sets used in well-established AutoML benchmarks.
  arXiv  Detail & Related papers  (2021-01-26T17:17:49Z)
• Testing the Robustness of AutoML Systems [5.942234058526296]
  We investigate the robustness of machine learning pipelines generated with three AutoML systems, TPOT, H2O, and AutoKeras. In particular, we study the influence of dirty data on accuracy, and consider how using dirty training data may help create more robust solutions.
  arXiv  Detail & Related papers  (2020-05-06T08:20:03Z)
• Evolution of Scikit-Learn Pipelines with Dynamic Structured Grammatical Evolution [1.5224436211478214]
  This paper describes a novel grammar-based framework that adapts Dynamic Structured Grammatical Evolution (DSGE) to the evolution of Scikit-Learn classification pipelines. The experimental results include comparing AutoML-DSGE to another grammar-based AutoML framework, Resilient ClassificationPipeline Evolution (RECIPE)
  arXiv  Detail & Related papers  (2020-04-01T09:31:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.