AutoML
237 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
OutRank: Speeding up AutoML-based Model Search for Large Sparse Data sets with Cardinality-aware Feature Ranking
The proposed approach's feasibility is demonstrated by speeding up the state-of-the-art AutoML system on a synthetic data set with no performance loss.
DiffPrep: Differentiable Data Preprocessing Pipeline Search for Learning over Tabular Data
Data preprocessing is a crucial step in the machine learning process that transforms raw data into a more usable format for downstream ML models.
AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting
We introduce AutoGluon-TimeSeries - an open-source AutoML library for probabilistic time series forecasting.
Predicting delays in Indian lower courts using AutoML and Decision Forests
This paper presents a classification model that predicts delays in Indian lower courts based on case information available at filing.
Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering
Automated Machine Learning (AutoML) techniques have recently been introduced to design Collaborative Filtering (CF) models in a data-specific manner.
Pricing European Options with Google AutoML, TensorFlow, and XGBoost
juan-esteban-berger/options_pricing_automl_tensorflow_xgboost
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Researchers have been using Neural Networks and other related machine-learning techniques to price options since the early 1990s.
Assembled-OpenML: Creating Efficient Benchmarks for Ensembles in AutoML with OpenML
Moreover, we present an example of using Assembled-OpenML to compare a set of ensemble techniques.
AutoML in Heavily Constrained Applications
In this paper, we propose CAML, which uses meta-learning to automatically adapt its own AutoML parameters, such as the search strategy, the validation strategy, and the search space, for a task at hand.
Fix Fairness, Don't Ruin Accuracy: Performance Aware Fairness Repair using AutoML
In order to demonstrate the effectiveness of our approach, we evaluated our approach on four fairness problems and 16 different ML models, and our results show a significant improvement over the baseline and existing bias mitigation techniques.
Hyperparameters in Reinforcement Learning and How To Tune Them
In order to improve reproducibility, deep reinforcement learning (RL) has been adopting better scientific practices such as standardized evaluation metrics and reporting.
