AutoML
236 papers with code • 2 benchmarks • 7 datasets
Automated Machine Learning (AutoML) is a general concept which covers diverse techniques for automated model learning including automatic data preprocessing, architecture search, and model selection. Source: Evaluating recommender systems for AI-driven data science (1905.09205)
Source: CHOPT : Automated Hyperparameter Optimization Framework for Cloud-Based Machine Learning Platforms
Benchmarks
These leaderboards are used to track progress in AutoML
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Libraries
Use these libraries to find AutoML models and implementations
NAS-Bench-101
MedMNIST
Chalearn-AutoML-1
Latest papers with no code
AutoMMLab: Automatically Generating Deployable Models from Language Instructions for Computer Vision Tasks
Automated machine learning (AutoML) is a collection of techniques designed to automate the machine learning development process.
Data Pipeline Training: Integrating AutoML to Optimize the Data Flow of Machine Learning Models
With the increasing diversification and complexity of Data sources, as well as the rapid growth of data volumes, building an efficient Data Pipeline has become crucial for improving work efficiency and solving complex problems.
Floralens: a Deep Learning Model for the Portuguese Native Flora
Machine-learning techniques, namely deep convolutional neural networks, are pivotal for image-based identification of biological species in many Citizen Science platforms.
Guided Evolution with Binary Discriminators for ML Program Search
How to automatically design better machine learning programs is an open problem within AutoML.
Hyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Study
First, we assess the importance of data splitting schemes for tuning ML learners within Double Machine Learning.
Explainable Automated Machine Learning for Credit Decisions: Enhancing Human Artificial Intelligence Collaboration in Financial Engineering
This paper explores the integration of Explainable Automated Machine Learning (AutoML) in the realm of financial engineering, specifically focusing on its application in credit decision-making.
Human-Centered AI Product Prototyping with No-Code AutoML: Conceptual Framework, Potentials and Limitations
This paper evaluates No-Code AutoML as a solution for challenges in AI product prototyping, characterized by unpredictability and inaccessibility to non-experts, and proposes a conceptual framework.
Grammar-based evolutionary approach for automated workflow composition with domain-specific operators and ensemble diversity
First, it employs a suite of genetic operators, designed specifically for AWC, to optimise both the structure of workflows and their hyper-parameters.
Large Language Model Agent for Hyper-Parameter Optimization
Hyperparameter optimization is critical in modern machine learning, requiring expert knowledge, numerous trials, and high computational and human resources.
Information Leakage Detection through Approximate Bayes-optimal Prediction
To address these limitations, we establish a theoretical framework using statistical learning theory and information theory to accurately quantify and detect IL.
