AutoML
235 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
Towards Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks
Experimental results demonstrate the effectiveness of our approach, resulting in models with over 80\% in accuracy and low computational cost.
Budget-aware Query Tuning: An AutoML Perspective
We further extend our study from tuning a single query to tuning a workload with multiple queries, and we call this generalized problem budget-aware workload tuning (WT), which aims for minimizing the execution time of the entire workload.
Deciphering AutoML Ensembles: cattleia's Assistance in Decision-Making
In many applications, model ensembling proves to be better than a single predictive model.
Automated Contrastive Learning Strategy Search for Time Series
In recent years, Contrastive Learning (CL) has become a predominant representation learning paradigm for time series.
Automated data processing and feature engineering for deep learning and big data applications: a survey
In addition to automating specific data processing tasks, we discuss the use of AutoML methods and tools to simultaneously optimize all stages of the machine learning pipeline.
LLM Guided Evolution - The Automation of Models Advancing Models
GE leverages LLMs for a more intelligent, supervised evolutionary process, guiding mutations and crossovers.
Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methods
Finally, we carried out an extensive comparison and analysis of the performance of automated data augmentation techniques and state-of-the-art methods based on classical augmentation approaches.
Evolving machine learning workflows through interactive AutoML
In this paper we present \ourmethod, an interactive G3P algorithm that allows users to dynamically modify the grammar to prune the search space and focus on their regions of interest.
Automated Machine Learning for Multi-Label Classification
Automated machine learning (AutoML) aims to select and configure machine learning algorithms and combine them into machine learning pipelines tailored to a dataset at hand.
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.
