AutoML
233 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
Libraries
Use these libraries to find AutoML models and implementationsMost implemented papers
Benchmarking Automatic Machine Learning Frameworks
AutoML serves as the bridge between varying levels of expertise when designing machine learning systems and expedites the data science process.
SAFE ML: Surrogate Assisted Feature Extraction for Model Learning
Complex black-box predictive models may have high accuracy, but opacity causes problems like lack of trust, lack of stability, sensitivity to concept drift.
Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning
Automated Machine Learning (AutoML) supports practitioners and researchers with the tedious task of designing machine learning pipelines and has recently achieved substantial success.
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science
As the field of data science continues to grow, there will be an ever-increasing demand for tools that make machine learning accessible to non-experts.
Automatic Gradient Boosting
Automatic machine learning performs predictive modeling with high performing machine learning tools without human interference.
AutoSF: Searching Scoring Functions for Knowledge Graph Embedding
The algorithm is further sped up by a filter and a predictor, which can avoid repeatedly training SFs with same expressive ability and help removing bad candidates during the search before model training.
Searching to Exploit Memorization Effect in Learning from Corrupted Labels
Sample selection approaches are popular in robust learning from noisy labels.
Automatically Optimized Gradient Boosting Trees for Classifying Large Volume High Cardinality Data Streams Under Concept Drift
Data abundance along with scarcity of machine learning experts and domain specialists necessitates progressive automation of end-to-end machine learning workflows.
Model-based Asynchronous Hyperparameter and Neural Architecture Search
We introduce a model-based asynchronous multi-fidelity method for hyperparameter and neural architecture search that combines the strengths of asynchronous Hyperband and Gaussian process-based Bayesian optimization.
AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks
Inspired by the recent success of AutoML in deep compression, we introduce AutoML to GAN compression and develop an AutoGAN-Distiller (AGD) framework.