content: # Please note that order matters: https://toml.io/en/v1.0.0#array-of-tables
  
  [[frameworks]]
  name = "GAMA"
  icon = "img/logos/GAMA-icon.png"  # URL or relative path
  repository = "https://github.com/openml-labs/gama"
  documentation="https://openml-labs.github.io/gama/master/"
  summary = """
  GAMA is developed for AutoML research and features a flexible AutoML
  pipeline, which makes it easy to develop and evaluate new AutoML
  components. GAMA's benchmarking configuration features evolutionary
  optimization and ensemble construction.
  """
  
  [[frameworks.papers]]
  title = "GAMA: A General Automated Machine Learning Assistant"
  abstract = """
  The General Automated Machine learning Assistant (GAMA) is a
  modular AutoML system developed to empower users to track and
  control how AutoML algorithms search for optimal machine
  learning pipelines, and facilitate AutoML research itself. In
  contrast to current, often black-box systems, GAMA allows users
  to plug in different AutoML and post-processing techniques, logs
  and visualizes the search process, and supports easy
  benchmarking. It currently features three AutoML search
  algorithms, two model post-processing steps, and is designed to
  allow for more components to be added.
  """
  year = 2021
  venue = "Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, Cham, 2020."
  pdf ="https://arxiv.org/pdf/2007.04911.pdf"
  arxiv = "https://link.springer.com/chapter/10.1007/978-3-030-67670-4_39"
  authors = "Pieter Gijsbers, Joaquin Vanschoren"
  
  [[frameworks]]
  name = "TPOT"
  icon = "img/logos/tpot.jpeg"
  repository ="https://github.com/EpistasisLab/tpot"
  documentation="http://epistasislab.github.io/tpot/"
  summary = "TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. It has a focus on optimizing models for biomedical data."
  
  [[frameworks.papers]]
  title="Automating biomedical data science through tree-based pipeline optimization"
  authors="Randal S. Olson, Ryan J. Urbanowicz, Peter C. Andrews, Nicole A. Lavender, La Creis Kidd, and Jason H. Moore"
  abstract="""
  Automated machine learning (AutoML) systems are helpful data science assistants designed to scan data for novel features, select appropriate supervised learning models and optimize their parameters. For this purpose, Tree-based Pipeline Optimization Tool (TPOT) was developed using strongly typed genetic programing (GP) to recommend an optimized analysis pipeline for the data scientist’s prediction problem. However, like other AutoML systems, TPOT may reach computational resource limits when working on big data such as whole-genome expression data.
  """
  year=2016
  venue="Applications of Evolutionary Computation, pages 123-137"
  pdf="https://proceedings.mlsys.org/paper/2021/file/92cc227532d17e56e07902b254dfad10-Paper.pdf"
  arxiv="https://arxiv.org/pdf/1601.07925"
  
  [[frameworks]]
  name = "AutoGluon"
  repository = "https://github.com/awslabs/autogluon"
  documentation = "https://auto.gluon.ai/stable/index.html"
  icon = "img/logos/autogluon.png"
  summary = """
  AutoGluon enables easy-to-use and easy-to-extend AutoML with a focus on automated
  stack ensembling, deep learning, and real-world applications spanning image, text,
  and tabular data.
  """
  
  [[frameworks.papers]]
  title="AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data"
  authors="Nick Erickson, Jonas Mueller, Alexander Shirkov, Hang Zhang, Pedro Larroy, Mu Li, Alexander Smola"
  abstract="""
  We introduce AutoGluon-Tabular, an open-source AutoML framework that requires only
  a single line of Python to train highly accurate machine learning models on an
  unprocessed tabular dataset such as a CSV file. Unlike existing AutoML frameworks
  that primarily focus on model/hyperparameter selection, AutoGluon-Tabular succeeds
  by ensembling multiple models and stacking them in multiple layers. Experiments
  reveal that our multi-layer combination of many models offers better use of
  allocated training time than seeking out the best. A second contribution is an
  extensive evaluation of public and commercial AutoML platforms including TPOT, H2O,
  AutoWEKA, auto-sklearn, AutoGluon, and Google AutoML Tables. Tests on a suite of 50
  classification and regression tasks from Kaggle and the OpenML AutoML Benchmark
  reveal that AutoGluon is faster, more robust, and much more accurate. We find that
  AutoGluon often even outperforms the best-in-hindsight combination of all of its
  competitors. In two popular Kaggle competitions, AutoGluon beat 99% of the
  participating data scientists after merely 4h of training on the raw data.
  """
  year=2020
  venue="ICML 2020 AutoML Workshop"
  pdf="https://arxiv.org/pdf/2003.06505.pdf"
  arxiv="https://arxiv.org/pdf/2003.06505"
  
  [[frameworks]]
  name = "auto-sklearn"
  repository = "https://github.com/automl/auto-sklearn"
  documentation="https://automl.github.io/auto-sklearn/master/#"
  icon = "img/logos/auto-sklearn.png"
  summary = """
  Auto-sklearn is an automated machine learning toolkit and a drop-in replacement
  for a scikit-learn estimator. Auto-sklearn frees a machine learning user from
  algorithm selection and hyperparameter tuning. It leverages recent advantages in
  Bayesian optimization, meta-learning and ensemble construction.
  """
  
  [[frameworks.papers]]
  title="Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning"
  authors="Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer, Frank Hutter"
  abstract="""
  Automated Machine Learning (AutoML) supports practitioners and
  researchers with the tedious task of designing machine learning
  pipelines and has recently achieved substantial success. In this
  paper we introduce new AutoML approaches motivated by our
  winning submission to the second ChaLearn AutoML challenge. We
  develop PoSH Auto-sklearn, which enables AutoML systems to work
  well on large datasets under rigid time limits using a new,
  simple and meta-feature-free meta-learning technique and employs
  a successful bandit strategy for budget allocation. However,
  PoSH Auto-sklearn introduces even more ways of running AutoML
  and might make it harder for users to set it up correctly.
  Therefore, we also go one step further and study the design
  space of AutoML itself, proposing a solution towards truly
  hands-free AutoML. Together, these changes give rise to the next
  generation of our AutoML system, Auto-sklearn 2.0. We verify
  the improvements by these additions in a large experimental
  study on 39 AutoML benchmark datasets and conclude the paper by
  comparing to other popular AutoML frameworks and Auto-sklearn
  1.0, reducing the relative error by up to a factor of 4.5, and
  yielding a performance in 10 minutes that is substantially
  better than what Auto-sklearn 1.0 achieves within an hour.
  """
  year=2021
  venue="arXiv"
  pdf="https://arxiv.org/pdf/2007.04074.pdf"
  arxiv="https://arxiv.org/abs/2007.04074"
  
  [[frameworks.papers]]
  title="Efficient and Robust Automated Machine Learning"
  authors="Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum, Frank Hutter"
  year=2015
  venue="Advances in Neural Information Processing Systems 28 (NIPS 2015)"
  pdf="https://papers.neurips.cc/paper/2015/file/11d0e6287202fced83f79975ec59a3a6-Paper.pdf"
  arxiv="https://papers.neurips.cc/paper/2015/hash/11d0e6287202fced83f79975ec59a3a6-Abstract.html"
  abstract="""
  The success of machine learning in a broad range of applications
  has led to an ever-growing demand for machine learning systems
  that can be used off the shelf by non-experts. To be effective
  in practice, such systems need to automatically choose a good
  algorithm and feature preprocessing steps for a new dataset at
  hand, and also set their respective hyperparameters. Recent work
  has started to tackle this automated machine learning (AutoML)
  problem with the help of efficient Bayesian optimization
  methods. In this work we introduce a robust new AutoML system
  based on scikit-learn (using 15 classifiers, 14 feature
  preprocessing methods, and 4 data preprocessing methods, giving
  rise to a structured hypothesis space with 110 hyperparameters).
  This system, which we dub auto-sklearn, improves on existing
  AutoML methods by automatically taking into account past
  performance on similar datasets, and by constructing ensembles
  from the models evaluated during the optimization. Our system
  won the first phase of the ongoing ChaLearn AutoML challenge,
  and our comprehensive analysis on over 100 diverse datasets
  shows that it substantially outperforms the previous state of
  the art in AutoML. We also demonstrate the performance gains due
  to each of our contributions and derive insights into the
  effectiveness of the individual components of auto-sklearn.
  """
  
  [[frameworks]]
  name = "mljar-supervised"
  repository = "https://github.com/mljar/mljar-supervised"
  documentation = "https://mljar.com/"
  icon ="img/logos/mljar.png"
  summary = "The mljar-supervised is an Automated Machine Learning Python package that works with tabular data. It is designed to save time for a data scientist. It abstracts the common way to preprocess the data, construct the machine learning models, and perform hyper-parameters tuning to find the best model 🏆. It is no black-box as you can see exactly how the ML pipeline is constructed (with a detailed Markdown report for each ML model)."
  
  [[frameworks]]
  name = "h2o"
  repository = "https://github.com/h2oai/h2o-3"
  documentation="https://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html"
  icon = "img/logos/h2o-automl-logo.jpeg"
  summary = """
  H2O's AutoML can be used for automating the machine learning
  workflow, which includes automatic training and tuning of many
  models within a user-specified time-limit. H2O offers a number of
  model explainability methods that apply to AutoML objects (groups of
  models), as well as individual models (e.g. leader model).
  Explanations can be generated automatically with a single function
  call, providing a simple interface to exploring and explaining the
  AutoML models.
  """
  
  [[frameworks.papers]]
  title="H2O AutoML: Scalable Automatic Machine Learning"
  authors="Erin LeDell and Sébastien Poirier"
  abstract="""
  H2O is an open source, distributed machine learning platform
  designed to scale to very large datasets, with APIs in R,
  Python, Java and Scala. We present H2O AutoML, a highly
  scalable, fully-automated, supervised learning algorithm which
  automates the process of training a large selection of
  candidate models and stacked ensembles within a single function.
  The result of the AutoML run is a “leaderboard”: a ranked list
  of models, all of which can be easily exported for use in a
  production environment. Models in the leader- board can be
  ranked by numerous model performance metrics or other model
  attributes such as training time or average per-row prediction
  speed. The H2O AutoML algorithm relies on the efficient training
  of H2O machine learning algorithms to produce a large number
  of models in a short amount of time. H2O AutoML uses a
  combination of fast random search and stacked ensembles to
  achieve results competitive with, and often better than, other
  frameworks which rely on more complex model tuning techniques
  such as Bayesian optimization or genetic algorithms. H2O AutoML
  trains a variety of algorithms (e.g. GBMs, Random Forests,
  Deep Neural Networks, GLMs), yielding a healthy amount of
  diversity across candidate models, which can be exploited by
  stacked ensembles to produce a powerful final model. The
  effectiveness of this technique is reflected in the OpenML
  AutoML Benchmark, which compares the performance of several of
  the most well known, open source AutoML systems across a number
  of datasets.
  """
  year=2020
  venue="ICML 2020 AutoML Workshop"
  pdf="https://www.automl.org/wp-content/uploads/2020/07/AutoML_2020_paper_61.pdf"
  
  
  [[frameworks]]
  name = "Light AutoML"
  repository = "https://github.com/sb-ai-lab/LightAutoML"
  documentation = "https://developers.sber.ru/portal/products/lightautoml"
  icon = "img/logos/LightAutoML_logo_small.png"
  summary = "LightAutoML is open-source Python library aimed at automated machine learning. It is designed to be lightweight and efficient for various tasks with tabular, text data."
  
  [[frameworks]]
  name = "FLAML"
  repository = "https://github.com/microsoft/FLAML"
  documentation="https://microsoft.github.io/FLAML/"
  icon = "img/logos/flaml.svg"
  summary = """
  FLAML is a lightweight Python library that finds accurate machine
  learning models automatically, efficiently and economically. It
  frees users from selecting learners and hyperparameters for each
  learner.
  """
  
  [[frameworks.papers]]
  title="FLAML: A Fast and Lightweight AutoML Librar"
  authors="Chi Wang, Qingyun Wu, Markus Weimer, Erkang Zhu"
  abstract="""
  We study the problem of using low computational cost to automate
  the choices of learners and hyperparameters for an ad-hoc
  training dataset and error metric, by conducting trials of
  different configurations on the given training data. We
  investigate the joint impact of multiple factors on both trial
  cost and model error, and propose several design guidelines.
  Following them, we build a fast and lightweight library FLAML
  which optimizes for low computational resource in finding
  accurate models. FLAML integrates several simple but effective
  search strategies into an adaptive system. It significantly
  outperforms top-ranked AutoML libraries on a large open source
  AutoML benchmark under equal, or sometimes orders of magnitude
  smaller budget constraints.
  """
  year=2021
  venue="MLSys Conference 2021"
  pdf="https://proceedings.mlsys.org/paper/2021/file/92cc227532d17e56e07902b254dfad10-Paper.pdf"
  arxiv=""
  