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 implementations
14 papers
137
5 papers
6,999
4 papers
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Most implemented papers

Benchmarking Automatic Machine Learning Frameworks

EpistasisLab/tpot 17 Aug 2018

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

olagacek/SAFE 28 Feb 2019

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

automl/ASKL2.0_experiments 8 Jul 2020

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

rhiever/tpot 20 Mar 2016

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

ja-thomas/autoxgboost 10 Jul 2018

Automatic machine learning performs predictive modeling with high performing machine learning tools without human interference.

AutoSF: Searching Scoring Functions for Knowledge Graph Embedding

AutoML-4Paradigm/ERAS 26 Apr 2019

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

bhanML/Co-teaching 6 Nov 2019

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

jim-schwoebel/allie 30 Nov 2019

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

awslabs/syne-tune 24 Mar 2020

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

TAMU-VITA/AGD ICML 2020

Inspired by the recent success of AutoML in deep compression, we introduce AutoML to GAN compression and develop an AutoGAN-Distiller (AGD) framework.