From dafe2ece633002546ae18edc7b16344c9836874e Mon Sep 17 00:00:00 2001
From: Pieter Gijsbers <p.gijsbers@tue.nl>
Date: Mon, 25 Jul 2022 17:30:27 -0400
Subject: [PATCH] New site (#479)

* First draft of new website

* Add framework descriptions, papers and logos

* Update footer with Github link

* Remove under construction banner

* Add redirect from old page to new one

* Update page title

* Add text links to new paper to be added later

* Move static site to /docs

* Whitelist documentation images

* Remove temporary work directory

* Add documentation images

* Place holder for mobile

* Move old notebooks and visualizations

To make sure they are not confusing for new users, as these will no longer work out-of-the-box.
New notebooks will be added soon but I don't have the files available right now.

* Tell github this is not Jekyll
---
 .gitignore                                    |   1 +
 docs/.nojekyll                                |   0
 docs/Gemfile                                  |   3 -
 docs/_config.yml                              |  26 -
 docs/assets/css/main.scss                     |  16 -
 docs/automl_overview.html                     |  17 +
 docs/frameworks.html                          | 892 ++++++++++++++++++
 docs/img/logos/GAMA-icon.png                  | Bin 0 -> 117679 bytes
 docs/img/logos/GAMA.png                       | Bin 0 -> 161401 bytes
 docs/img/logos/GitHub-Mark-32px.png           | Bin 0 -> 1714 bytes
 docs/img/logos/GitHub-Mark-64px.png           | Bin 0 -> 2625 bytes
 docs/img/logos/GitHub-Mark-Light-64px.png     | Bin 0 -> 2330 bytes
 docs/img/logos/LightAutoML_logo_small.png     | Bin 0 -> 214460 bytes
 docs/img/logos/auto-sklearn.png               | Bin 0 -> 9083 bytes
 docs/img/logos/autogluon.png                  | Bin 0 -> 54649 bytes
 docs/img/logos/flaml.svg                      |   1 +
 docs/img/logos/h2o-automl-logo.jpeg           | Bin 0 -> 80863 bytes
 docs/img/logos/mljar.png                      | Bin 0 -> 8395 bytes
 docs/img/logos/tpot.jpeg                      | Bin 0 -> 181208 bytes
 docs/img/shiny.png                            | Bin 0 -> 211925 bytes
 docs/index.html                               | 357 +++++++
 docs/papers.html                              | 209 ++++
 docs/results.html                             | 184 ++++
 docs/style.css                                | 484 ++++++++++
 reports/{ => old}/datasets/1h/metadata.csv    |   0
 reports/{ => old}/datasets/4h/metadata.csv    |   0
 reports/{ => old}/duration_comparison.ipynb   |   0
 .../graphics/1h/binary_norm_score_heat.png    | Bin
 .../1h/binary_norm_score_parallel_ccord.png   | Bin
 .../1h/binary_norm_score_stripplot.png        | Bin
 .../graphics/1h/binary_results_stripplot.png  | Bin
 .../graphics/1h/binary_score_heat.png         | Bin
 .../1h/binary_score_parallel_ccord.png        | Bin
 .../1h/multiclass_norm_score_heat.png         | Bin
 .../multiclass_norm_score_parallel_ccord.png  | Bin
 .../1h/multiclass_norm_score_stripplot.png    | Bin
 .../1h/multiclass_results_stripplot.png       | Bin
 .../graphics/1h/multiclass_score_heat.png     | Bin
 .../1h/multiclass_score_parallel_ccord.png    | Bin
 .../graphics/4h/binary_norm_score_heat.png    | Bin
 .../4h/binary_norm_score_parallel_ccord.png   | Bin
 .../4h/binary_norm_score_stripplot.png        | Bin
 .../graphics/4h/binary_results_stripplot.png  | Bin
 .../graphics/4h/binary_score_heat.png         | Bin
 .../4h/binary_score_parallel_ccord.png        | Bin
 .../4h/multiclass_norm_score_heat.png         | Bin
 .../multiclass_norm_score_parallel_ccord.png  | Bin
 .../4h/multiclass_norm_score_stripplot.png    | Bin
 .../4h/multiclass_results_stripplot.png       | Bin
 .../graphics/4h/multiclass_score_heat.png     | Bin
 .../4h/multiclass_score_parallel_ccord.png    | Bin
 .../graphics/8h/binary_norm_score_heat.png    | Bin
 .../8h/binary_norm_score_parallel_ccord.png   | Bin
 .../8h/binary_norm_score_stripplot.png        | Bin
 .../graphics/8h/binary_results_stripplot.png  | Bin
 .../graphics/8h/binary_score_heat.png         | Bin
 .../8h/binary_score_parallel_ccord.png        | Bin
 .../8h/multiclass_norm_score_heat.png         | Bin
 .../multiclass_norm_score_parallel_ccord.png  | Bin
 .../8h/multiclass_norm_score_stripplot.png    | Bin
 .../8h/multiclass_results_stripplot.png       | Bin
 .../graphics/8h/multiclass_score_heat.png     | Bin
 .../8h/multiclass_score_parallel_ccord.png    | Bin
 .../{ => old}/input/results_large-8c4h.csv    |   0
 .../input/results_large-8c4h_ref.csv          |   0
 .../{ => old}/input/results_large-8c8h.csv    |   0
 .../input/results_large-8c8h_ref.csv          |   0
 .../{ => old}/input/results_medium-4c1h.csv   |   0
 .../input/results_medium-4c1h_ref.csv         |   0
 .../{ => old}/input/results_medium-4c4h.csv   |   0
 .../input/results_medium-4c4h_ref.csv         |   0
 .../{ => old}/input/results_medium-8c1h.csv   |   0
 .../input/results_medium-8c1h_ref.csv         |   0
 .../{ => old}/input/results_medium-8c4h.csv   |   0
 .../input/results_medium-8c4h_ref.csv         |   0
 reports/{ => old}/input/results_old.csv       |   0
 .../{ => old}/input/results_small-2c1h.csv    |   0
 .../input/results_small-2c1h_ref.csv          |   0
 .../{ => old}/input/results_small-8c1h.csv    |   0
 .../input/results_small-8c1h_ref.csv          |   0
 .../{ => old}/input/results_small-8c4h.csv    |   0
 .../input/results_small-8c4h_ref.csv          |   0
 reports/{ => old}/input/results_valid.csv     |   0
 reports/{ => old}/input/results_valid_ref.csv |   0
 reports/old/readme.md                         |   3 +
 reports/{ => old}/reports.ipynb               |   0
 reports/{ => old}/tables/1h/all_results.csv   |   0
 .../{ => old}/tables/1h/folds_leaderboard.csv |   0
 .../{ => old}/tables/1h/models_summary.csv    |   0
 .../tables/1h/norm_score_summary.csv          |   0
 .../{ => old}/tables/1h/result_summary.csv    |   0
 reports/{ => old}/tables/1h/score_summary.csv |   0
 .../{ => old}/tables/1h/tasks_leaderboard.csv |   0
 reports/{ => old}/tables/4h/all_results.csv   |   0
 .../{ => old}/tables/4h/folds_leaderboard.csv |   0
 .../{ => old}/tables/4h/models_summary.csv    |   0
 .../tables/4h/norm_score_summary.csv          |   0
 .../{ => old}/tables/4h/result_summary.csv    |   0
 reports/{ => old}/tables/4h/score_summary.csv |   0
 .../{ => old}/tables/4h/tasks_leaderboard.csv |   0
 reports/{ => old}/tables/8h/all_results.csv   |   0
 .../{ => old}/tables/8h/folds_leaderboard.csv |   0
 .../{ => old}/tables/8h/models_summary.csv    |   0
 .../tables/8h/norm_score_summary.csv          |   0
 .../{ => old}/tables/8h/result_summary.csv    |   0
 reports/{ => old}/tables/8h/score_summary.csv |   0
 .../{ => old}/tables/8h/tasks_leaderboard.csv |   0
 reports/readme.md                             |   4 +
 108 files changed, 2152 insertions(+), 45 deletions(-)
 create mode 100644 docs/.nojekyll
 delete mode 100644 docs/Gemfile
 delete mode 100644 docs/_config.yml
 delete mode 100644 docs/assets/css/main.scss
 create mode 100644 docs/automl_overview.html
 create mode 100644 docs/frameworks.html
 create mode 100644 docs/img/logos/GAMA-icon.png
 create mode 100644 docs/img/logos/GAMA.png
 create mode 100644 docs/img/logos/GitHub-Mark-32px.png
 create mode 100644 docs/img/logos/GitHub-Mark-64px.png
 create mode 100644 docs/img/logos/GitHub-Mark-Light-64px.png
 create mode 100644 docs/img/logos/LightAutoML_logo_small.png
 create mode 100644 docs/img/logos/auto-sklearn.png
 create mode 100644 docs/img/logos/autogluon.png
 create mode 100644 docs/img/logos/flaml.svg
 create mode 100644 docs/img/logos/h2o-automl-logo.jpeg
 create mode 100644 docs/img/logos/mljar.png
 create mode 100644 docs/img/logos/tpot.jpeg
 create mode 100644 docs/img/shiny.png
 create mode 100644 docs/index.html
 create mode 100644 docs/papers.html
 create mode 100644 docs/results.html
 create mode 100644 docs/style.css
 rename reports/{ => old}/datasets/1h/metadata.csv (100%)
 rename reports/{ => old}/datasets/4h/metadata.csv (100%)
 rename reports/{ => old}/duration_comparison.ipynb (100%)
 rename reports/{ => old}/graphics/1h/binary_norm_score_heat.png (100%)
 rename reports/{ => old}/graphics/1h/binary_norm_score_parallel_ccord.png (100%)
 rename reports/{ => old}/graphics/1h/binary_norm_score_stripplot.png (100%)
 rename reports/{ => old}/graphics/1h/binary_results_stripplot.png (100%)
 rename reports/{ => old}/graphics/1h/binary_score_heat.png (100%)
 rename reports/{ => old}/graphics/1h/binary_score_parallel_ccord.png (100%)
 rename reports/{ => old}/graphics/1h/multiclass_norm_score_heat.png (100%)
 rename reports/{ => old}/graphics/1h/multiclass_norm_score_parallel_ccord.png (100%)
 rename reports/{ => old}/graphics/1h/multiclass_norm_score_stripplot.png (100%)
 rename reports/{ => old}/graphics/1h/multiclass_results_stripplot.png (100%)
 rename reports/{ => old}/graphics/1h/multiclass_score_heat.png (100%)
 rename reports/{ => old}/graphics/1h/multiclass_score_parallel_ccord.png (100%)
 rename reports/{ => old}/graphics/4h/binary_norm_score_heat.png (100%)
 rename reports/{ => old}/graphics/4h/binary_norm_score_parallel_ccord.png (100%)
 rename reports/{ => old}/graphics/4h/binary_norm_score_stripplot.png (100%)
 rename reports/{ => old}/graphics/4h/binary_results_stripplot.png (100%)
 rename reports/{ => old}/graphics/4h/binary_score_heat.png (100%)
 rename reports/{ => old}/graphics/4h/binary_score_parallel_ccord.png (100%)
 rename reports/{ => old}/graphics/4h/multiclass_norm_score_heat.png (100%)
 rename reports/{ => old}/graphics/4h/multiclass_norm_score_parallel_ccord.png (100%)
 rename reports/{ => old}/graphics/4h/multiclass_norm_score_stripplot.png (100%)
 rename reports/{ => old}/graphics/4h/multiclass_results_stripplot.png (100%)
 rename reports/{ => old}/graphics/4h/multiclass_score_heat.png (100%)
 rename reports/{ => old}/graphics/4h/multiclass_score_parallel_ccord.png (100%)
 rename reports/{ => old}/graphics/8h/binary_norm_score_heat.png (100%)
 rename reports/{ => old}/graphics/8h/binary_norm_score_parallel_ccord.png (100%)
 rename reports/{ => old}/graphics/8h/binary_norm_score_stripplot.png (100%)
 rename reports/{ => old}/graphics/8h/binary_results_stripplot.png (100%)
 rename reports/{ => old}/graphics/8h/binary_score_heat.png (100%)
 rename reports/{ => old}/graphics/8h/binary_score_parallel_ccord.png (100%)
 rename reports/{ => old}/graphics/8h/multiclass_norm_score_heat.png (100%)
 rename reports/{ => old}/graphics/8h/multiclass_norm_score_parallel_ccord.png (100%)
 rename reports/{ => old}/graphics/8h/multiclass_norm_score_stripplot.png (100%)
 rename reports/{ => old}/graphics/8h/multiclass_results_stripplot.png (100%)
 rename reports/{ => old}/graphics/8h/multiclass_score_heat.png (100%)
 rename reports/{ => old}/graphics/8h/multiclass_score_parallel_ccord.png (100%)
 rename reports/{ => old}/input/results_large-8c4h.csv (100%)
 rename reports/{ => old}/input/results_large-8c4h_ref.csv (100%)
 rename reports/{ => old}/input/results_large-8c8h.csv (100%)
 rename reports/{ => old}/input/results_large-8c8h_ref.csv (100%)
 rename reports/{ => old}/input/results_medium-4c1h.csv (100%)
 rename reports/{ => old}/input/results_medium-4c1h_ref.csv (100%)
 rename reports/{ => old}/input/results_medium-4c4h.csv (100%)
 rename reports/{ => old}/input/results_medium-4c4h_ref.csv (100%)
 rename reports/{ => old}/input/results_medium-8c1h.csv (100%)
 rename reports/{ => old}/input/results_medium-8c1h_ref.csv (100%)
 rename reports/{ => old}/input/results_medium-8c4h.csv (100%)
 rename reports/{ => old}/input/results_medium-8c4h_ref.csv (100%)
 rename reports/{ => old}/input/results_old.csv (100%)
 rename reports/{ => old}/input/results_small-2c1h.csv (100%)
 rename reports/{ => old}/input/results_small-2c1h_ref.csv (100%)
 rename reports/{ => old}/input/results_small-8c1h.csv (100%)
 rename reports/{ => old}/input/results_small-8c1h_ref.csv (100%)
 rename reports/{ => old}/input/results_small-8c4h.csv (100%)
 rename reports/{ => old}/input/results_small-8c4h_ref.csv (100%)
 rename reports/{ => old}/input/results_valid.csv (100%)
 rename reports/{ => old}/input/results_valid_ref.csv (100%)
 create mode 100644 reports/old/readme.md
 rename reports/{ => old}/reports.ipynb (100%)
 rename reports/{ => old}/tables/1h/all_results.csv (100%)
 rename reports/{ => old}/tables/1h/folds_leaderboard.csv (100%)
 rename reports/{ => old}/tables/1h/models_summary.csv (100%)
 rename reports/{ => old}/tables/1h/norm_score_summary.csv (100%)
 rename reports/{ => old}/tables/1h/result_summary.csv (100%)
 rename reports/{ => old}/tables/1h/score_summary.csv (100%)
 rename reports/{ => old}/tables/1h/tasks_leaderboard.csv (100%)
 rename reports/{ => old}/tables/4h/all_results.csv (100%)
 rename reports/{ => old}/tables/4h/folds_leaderboard.csv (100%)
 rename reports/{ => old}/tables/4h/models_summary.csv (100%)
 rename reports/{ => old}/tables/4h/norm_score_summary.csv (100%)
 rename reports/{ => old}/tables/4h/result_summary.csv (100%)
 rename reports/{ => old}/tables/4h/score_summary.csv (100%)
 rename reports/{ => old}/tables/4h/tasks_leaderboard.csv (100%)
 rename reports/{ => old}/tables/8h/all_results.csv (100%)
 rename reports/{ => old}/tables/8h/folds_leaderboard.csv (100%)
 rename reports/{ => old}/tables/8h/models_summary.csv (100%)
 rename reports/{ => old}/tables/8h/norm_score_summary.csv (100%)
 rename reports/{ => old}/tables/8h/result_summary.csv (100%)
 rename reports/{ => old}/tables/8h/score_summary.csv (100%)
 rename reports/{ => old}/tables/8h/tasks_leaderboard.csv (100%)
 create mode 100644 reports/readme.md

diff --git a/.gitignore b/.gitignore
index e3cad6b3e..4dba33db1 100644
--- a/.gitignore
+++ b/.gitignore
@@ -47,3 +47,4 @@ Singularityfile
 #whitelist
 !reports/**/*.csv
 !reports/**/*.png
+!docs/img/**/*.png
diff --git a/docs/.nojekyll b/docs/.nojekyll
new file mode 100644
index 000000000..e69de29bb
diff --git a/docs/Gemfile b/docs/Gemfile
deleted file mode 100644
index 24c316eba..000000000
--- a/docs/Gemfile
+++ /dev/null
@@ -1,3 +0,0 @@
-source 'https://rubygems.org'
-gem 'github-pages', group: :jekyll_plugins
-gem "jekyll-theme-hydeout", "~> 4"
diff --git a/docs/_config.yml b/docs/_config.yml
deleted file mode 100644
index f5bc67e41..000000000
--- a/docs/_config.yml
+++ /dev/null
@@ -1,26 +0,0 @@
-repository: openml/automlbenchmark
-#theme: jekyll-theme-slate
-remote_theme: fongandrew/hydeout
-markdown: kramdown
-
-title: AutoML Benchmark
-#description: config description
-
-#sidebar_home_link: true
-version: 0.9
-
-hydeout:
-  repo: https://github.com/openml/automlbenchmark
-  version: 0.9
-
-defaults:
-  -
-    scope:
-      path: README.md
-    values:
-      title: ''
-  -
-    scope:
-      path: HOWTO.md
-    values:
-      title: ''
diff --git a/docs/assets/css/main.scss b/docs/assets/css/main.scss
deleted file mode 100644
index 61a51042f..000000000
--- a/docs/assets/css/main.scss
+++ /dev/null
@@ -1,16 +0,0 @@
----
-# Jekyll needs front matter for SCSS files
-# $sidebar-bg-color: #ac4142;
-# $sidebar-sticky: false;
----
-
-$sidebar-width: 20rem !default;
-$large-font-size: 1.1rem;
-$link-color: #195cc6;
-@import "hydeout";
-
-#sidebar {
-    .site-title {
-		font-family: Sans-Serif;
-    }
-}
\ No newline at end of file
diff --git a/docs/automl_overview.html b/docs/automl_overview.html
new file mode 100644
index 000000000..e72f07c04
--- /dev/null
+++ b/docs/automl_overview.html
@@ -0,0 +1,17 @@
+<html>
+  <head>
+    <title>AMLB</title>
+    <meta charset="UTF-8" />
+    <meta http-equiv="refresh" content="3; URL=frameworks.html" />
+  </head>
+  <body>
+    <p>
+      The old AutoML framework overview page no longer exists, you will be
+      redirected to the new page. If you are not redirected within 3 seconds,
+      you can find it at
+      <a href="https://openml.github.io/automlbenchmark/frameworks.html"
+        >https://openml.github.io/automlbenchmark/frameworks.html</a
+      >.
+    </p>
+  </body>
+</html>
diff --git a/docs/frameworks.html b/docs/frameworks.html
new file mode 100644
index 000000000..23d258e8b
--- /dev/null
+++ b/docs/frameworks.html
@@ -0,0 +1,892 @@
+<html>
+  <head>
+    <meta charset="utf-8" />
+    <title>AMLB: Frameworks</title>
+    <link rel="preconnect" href="https://fonts.googleapis.com" />
+    <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin />
+    <link
+      href="https://fonts.googleapis.com/css2?family=Inconsolata&family=Lato:ital,wght@0,400;0,700;1,400&display=swap"
+      rel="stylesheet"
+    />
+    <link href="style.css" rel="stylesheet" />
+  </head>
+  <body>
+    <nav class="navigation-bar">
+      <a href="index.html">
+        <svg
+          xmlns="http://www.w3.org/2000/svg"
+          class="nav-icon"
+          viewBox="0 0 20 20"
+          fill="currentColor"
+        >
+          <path
+            d="M10.707 2.293a1 1 0 00-1.414 0l-7 7a1 1 0 001.414 1.414L4 10.414V17a1 1 0 001 1h2a1 1 0 001-1v-2a1 1 0 011-1h2a1 1 0 011 1v2a1 1 0 001 1h2a1 1 0 001-1v-6.586l.293.293a1 1 0 001.414-1.414l-7-7z"
+          />
+        </svg>
+      </a>
+      <a href="results.html" class="nav-link">Results</a>
+      <a href="frameworks.html" class="nav-link">Frameworks</a>
+      <a href="papers.html" class="nav-link">papers</a>
+      <a
+        href="https://github.com/openml/automlbenchmark#readme"
+        target="_blank"
+        class="nav-link"
+        >user guide
+        <svg
+          xmlns="http://www.w3.org/2000/svg"
+          class="nav-icon-with-text"
+          fill="none"
+          viewBox="0 0 24 24"
+          stroke="currentColor"
+          stroke-width="2"
+        >
+          <path
+            stroke-linecap="round"
+            stroke-linejoin="round"
+            d="M10 6H6a2 2 0 00-2 2v10a2 2 0 002 2h10a2 2 0 002-2v-4M14 4h6m0 0v6m0-6L10 14"
+          />
+        </svg>
+      </a>
+      <a
+        href="https://github.com/openml/automlbenchmark/discussions/216"
+        target="_blank"
+        class="nav-link"
+        >Discussions
+        <svg
+          xmlns="http://www.w3.org/2000/svg"
+          class="nav-icon-with-text"
+          fill="none"
+          viewBox="0 0 24 24"
+          stroke="currentColor"
+          stroke-width="2"
+        >
+          <path
+            stroke-linecap="round"
+            stroke-linejoin="round"
+            d="M10 6H6a2 2 0 00-2 2v10a2 2 0 002 2h10a2 2 0 002-2v-4M14 4h6m0 0v6m0-6L10 14"
+          />
+        </svg>
+      </a>
+      <a
+        href="https://github.com/openml/automlbenchmark/"
+        target="_blank"
+        class="nav-icon"
+      >
+        <img src="img/logos/GitHub-Mark-Light-64px.png" height="100%" />
+      </a>
+    </nav>
+    <!-- <div class="construction-banner">
+      🚧 This website is under construction 🚧
+    </div> -->
+    <div class="page-content">
+      <section>
+        <h1 class="page-title">AutoML Frameworks</h1>
+        There is more to an AutoML system than just its performance. This page
+        contains more information about the integrated AutoML frameworks,
+        including links to their papers, repositories
+        <img src="img/logos/GitHub-Mark-64px.png" height="16px" />, and
+        documentation 📖. Summaries taken directly from the respective
+        documentation pages. Want to integrate your own framework?
+        <a
+          href="https://github.com/openml/automlbenchmark/blob/master/docs/HOWTO.md#add-an-automl-framework"
+          target="_blank"
+          >Adding your own framework</a
+        >
+        is relatively simple.
+        <!-- 
+        <br />
+        <br />
+        An AutoML framework may only be available through an API for a specific
+        programming language, while others can work stand-alone. Some systems
+        might output models which can be used without further dependency on the
+        AutoML package, in other cases the AutoML system is still required to
+        use the model. Some systems might be developed with a specific domain in
+        mind. When choosing an AutoML system, it is essential to consider things
+        that are important to you. -->
+      </section>
+      <section class="framework-card-list">
+        <div class="accordion acard">
+          <div class="framework-header">
+            <img src="img/logos/autogluon.png" height="32px" />
+            <h3>AutoGluon</h3>
+            <div class="framework-links">
+              <a href="https://github.com/awslabs/autogluon" target="_blank"
+                ><img src="img/logos/GitHub-Mark-64px.png" height="24px"
+              /></a>
+              <a href="https://auto.gluon.ai/stable/index.html" target="_blank"
+                >📖</a
+              >
+            </div>
+          </div>
+          <div>
+            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.
+          </div>
+          <input type="checkbox" id="more-autogluon" class="accordion-input" />
+          <div class="accordion-content">
+            <div class="paper">
+              <h3 class="paper-title">
+                AutoGluon-Tabular: Robust and Accurate AutoML for Structured
+                Data
+              </h3>
+              <div class="paper-authors">
+                Nick Erickson, Jonas Mueller, Alexander Shirkov, Hang Zhang,
+                Pedro Larroy, Mu Li, Alexander Smola
+              </div>
+              <div class="paper-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.
+              </div>
+              <div class="paper-links">
+                <div class="hover-expand">
+                  <strong>2020</strong>
+                  <div>ICML 2020 AutoML Workshop</div>
+                </div>
+                <a href="https://arxiv.org/pdf/2003.06505.pdf" target="_blank"
+                  >PDF</a
+                >
+                <a href="https://arxiv.org/abs/2003.06505" target="_blank"
+                  >arxiv</a
+                >
+              </div>
+            </div>
+          </div>
+          <label for="more-autogluon">
+            <svg
+              xmlns="http://www.w3.org/2000/svg"
+              class="accordion-chevron-down accordion-icon"
+              fill="none"
+              viewBox="0 0 24 24"
+              stroke="currentColor"
+              stroke-width="2"
+            >
+              <path
+                stroke-linecap="round"
+                stroke-linejoin="round"
+                d="M19 9l-7 7-7-7"
+              />
+            </svg>
+            <svg
+              xmlns="http://www.w3.org/2000/svg"
+              class="accordion-chevron-up accordion-icon"
+              fill="none"
+              viewBox="0 0 24 24"
+              stroke="currentColor"
+              stroke-width="2"
+            >
+              <path
+                stroke-linecap="round"
+                stroke-linejoin="round"
+                d="M5 15l7-7 7 7"
+              />
+            </svg>
+          </label>
+        </div>
+        <div class="accordion acard">
+          <div class="framework-header">
+            <img src="img/logos/auto-sklearn.png" height="32px" />
+            <h3>Auto-sklearn</h3>
+            <div class="framework-links">
+              <a href="https://github.com/automl/auto-sklearn" target="_blank"
+                ><img src="img/logos/GitHub-Mark-64px.png" height="24px"
+              /></a>
+              <a
+                href="https://automl.github.io/auto-sklearn/master/#"
+                target="_blank"
+                >📖</a
+              >
+            </div>
+          </div>
+          <div>
+            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.
+          </div>
+          <input type="checkbox" id="more-askl" class="accordion-input" />
+          <div class="accordion-content">
+            <div class="paper">
+              <h3 class="paper-title">
+                Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning
+              </h3>
+              <div class="paper-authors">
+                Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius
+                Lindauer, Frank Hutter
+              </div>
+              <div class="paper-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.
+              </div>
+              <div class="paper-links">
+                <div class="hover-expand">
+                  <strong>2021</strong>
+                  <div>arXiv</div>
+                </div>
+                <a href="https://arxiv.org/pdf/2007.04074.pdf" target="_blank"
+                  >PDF</a
+                >
+                <a href="https://arxiv.org/abs/2007.04074" target="_blank"
+                  >arxiv</a
+                >
+              </div>
+            </div>
+            <div class="paper">
+              <h3 class="paper-title">
+                Efficient and Robust Automated Machine Learning
+              </h3>
+              <div class="paper-authors">
+                Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost
+                Springenberg, Manuel Blum, Frank Hutter
+              </div>
+              <div class="paper-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.
+              </div>
+              <div class="paper-links">
+                <div class="hover-expand">
+                  <strong>2015</strong>
+                  <div>
+                    Advances in Neural Information Processing Systems 28 (NIPS
+                    2015)
+                  </div>
+                </div>
+                <a
+                  href="https://papers.neurips.cc/paper/2015/file/11d0e6287202fced83f79975ec59a3a6-Paper.pdf"
+                  target="_blank"
+                  >PDF</a
+                >
+                <a
+                  href="https://papers.neurips.cc/paper/2015/hash/11d0e6287202fced83f79975ec59a3a6-Abstract.html"
+                  target="_blank"
+                  >neurips</a
+                >
+              </div>
+            </div>
+          </div>
+          <label for="more-askl">
+            <svg
+              xmlns="http://www.w3.org/2000/svg"
+              class="accordion-chevron-down accordion-icon"
+              fill="none"
+              viewBox="0 0 24 24"
+              stroke="currentColor"
+              stroke-width="2"
+            >
+              <path
+                stroke-linecap="round"
+                stroke-linejoin="round"
+                d="M19 9l-7 7-7-7"
+              />
+            </svg>
+            <svg
+              xmlns="http://www.w3.org/2000/svg"
+              class="accordion-chevron-up accordion-icon"
+              fill="none"
+              viewBox="0 0 24 24"
+              stroke="currentColor"
+              stroke-width="2"
+            >
+              <path
+                stroke-linecap="round"
+                stroke-linejoin="round"
+                d="M5 15l7-7 7 7"
+              />
+            </svg>
+          </label>
+        </div>
+        <div class="accordion acard">
+          <div class="framework-header">
+            <img src="img/logos/flaml.svg" height="32px" />
+            <h3>FLAML</h3>
+            <div class="framework-links">
+              <a href="https://github.com/microsoft/FLAML" target="_blank"
+                ><img src="img/logos/GitHub-Mark-64px.png" height="24px"
+              /></a>
+              <a href="https://microsoft.github.io/FLAML/" target="_blank"
+                >📖</a
+              >
+            </div>
+          </div>
+          <div>
+            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.
+          </div>
+          <input type="checkbox" id="more-flaml" class="accordion-input" />
+          <div class="accordion-content">
+            <div class="paper">
+              <h3 class="paper-title">
+                FLAML: A Fast and Lightweight AutoML Library
+              </h3>
+              <div class="paper-authors">
+                Chi Wang, Qingyun Wu, Markus Weimer, Erkang Zhu
+              </div>
+              <div class="paper-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.
+              </div>
+              <div class="paper-links">
+                <div class="hover-expand">
+                  <strong>2021</strong>
+                  <div>MLSys Conference 2021</div>
+                </div>
+                <a
+                  href="https://proceedings.mlsys.org/paper/2021/file/92cc227532d17e56e07902b254dfad10-Paper.pdf"
+                  target="_blank"
+                  >PDF</a
+                >
+              </div>
+            </div>
+          </div>
+          <label for="more-flaml">
+            <svg
+              xmlns="http://www.w3.org/2000/svg"
+              class="accordion-chevron-down accordion-icon"
+              fill="none"
+              viewBox="0 0 24 24"
+              stroke="currentColor"
+              stroke-width="2"
+            >
+              <path
+                stroke-linecap="round"
+                stroke-linejoin="round"
+                d="M19 9l-7 7-7-7"
+              />
+            </svg>
+            <svg
+              xmlns="http://www.w3.org/2000/svg"
+              class="accordion-chevron-up accordion-icon"
+              fill="none"
+              viewBox="0 0 24 24"
+              stroke="currentColor"
+              stroke-width="2"
+            >
+              <path
+                stroke-linecap="round"
+                stroke-linejoin="round"
+                d="M5 15l7-7 7 7"
+              />
+            </svg>
+          </label>
+        </div>
+        <div class="accordion acard">
+          <div class="framework-header">
+            <img src="img/logos/GAMA-icon.png" height="32px" />
+            <h3>GAMA</h3>
+            <div class="framework-links">
+              <a href="https://github.com/openml-labs/gama" target="_blank"
+                ><img src="img/logos/GitHub-Mark-64px.png" height="24px"
+              /></a>
+              <a
+                href="https://openml-labs.github.io/gama/master/"
+                target="_blank"
+                >📖</a
+              >
+            </div>
+          </div>
+          <div>
+            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.
+          </div>
+          <input type="checkbox" id="more-gama" class="accordion-input" />
+          <div class="accordion-content">
+            <div class="paper">
+              <h3 class="paper-title">
+                GAMA: A General Automated Machine Learning Assistant
+              </h3>
+              <div class="paper-authors">
+                Pieter Gijsbers, Joaquin Vanschoren
+              </div>
+
+              <!-- <div class="paper-year">2019</div>
+            <div class="paper-venue">ICML 2019 AutoML Workshop</div>
+            <div class="paper-venue">cite</div> -->
+
+              <div class="paper-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.
+              </div>
+              <div class="paper-links">
+                <div class="hover-expand">
+                  <strong>2021</strong>
+                  <div>
+                    Joint European Conference on Machine Learning and Knowledge
+                    Discovery in Databases. Springer, Cham, 2020.
+                  </div>
+                </div>
+                <a href="https://arxiv.org/pdf/2007.04911.pdf" target="_blank"
+                  >PDF</a
+                >
+                <a
+                  href="https://link.springer.com/chapter/10.1007/978-3-030-67670-4_39"
+                  target="_blank"
+                  >Springer</a
+                >
+              </div>
+            </div>
+          </div>
+          <label for="more-gama">
+            <svg
+              xmlns="http://www.w3.org/2000/svg"
+              class="accordion-chevron-down accordion-icon"
+              fill="none"
+              viewBox="0 0 24 24"
+              stroke="currentColor"
+              stroke-width="2"
+            >
+              <path
+                stroke-linecap="round"
+                stroke-linejoin="round"
+                d="M19 9l-7 7-7-7"
+              />
+            </svg>
+            <svg
+              xmlns="http://www.w3.org/2000/svg"
+              class="accordion-chevron-up accordion-icon"
+              fill="none"
+              viewBox="0 0 24 24"
+              stroke="currentColor"
+              stroke-width="2"
+            >
+              <path
+                stroke-linecap="round"
+                stroke-linejoin="round"
+                d="M5 15l7-7 7 7"
+              />
+            </svg>
+          </label>
+        </div>
+        <div class="accordion acard">
+          <div class="framework-header">
+            <img src="img/logos/h2o-automl-logo.jpeg" height="32px" />
+            <h3>H2O AutoML</h3>
+            <div class="framework-links">
+              <a href="https://github.com/h2oai/h2o-3" target="_blank"
+                ><img src="img/logos/GitHub-Mark-64px.png" height="24px"
+              /></a>
+              <a
+                href="https://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html"
+                target="_blank"
+                >📖</a
+              >
+            </div>
+          </div>
+          <div>
+            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.
+          </div>
+          <input type="checkbox" id="more-h2o" class="accordion-input" />
+          <div class="accordion-content">
+            <div class="paper">
+              <h3 class="paper-title">
+                H2O AutoML: Scalable Automatic Machine Learning
+              </h3>
+              <div class="paper-authors">Erin LeDell and Sébastien Poirier</div>
+              <div class="paper-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 pro- cess 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 al- gorithms 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 va- riety 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.
+              </div>
+              <div class="paper-links">
+                <div class="hover-expand">
+                  <strong>2020</strong>
+                  <div>ICML 2020 AutoML Workshop</div>
+                </div>
+                <a
+                  href="https://www.automl.org/wp-content/uploads/2020/07/AutoML_2020_paper_61.pdf"
+                  target="_blank"
+                  >PDF</a
+                >
+              </div>
+            </div>
+          </div>
+          <label for="more-h2o">
+            <svg
+              xmlns="http://www.w3.org/2000/svg"
+              class="accordion-chevron-down accordion-icon"
+              fill="none"
+              viewBox="0 0 24 24"
+              stroke="currentColor"
+              stroke-width="2"
+            >
+              <path
+                stroke-linecap="round"
+                stroke-linejoin="round"
+                d="M19 9l-7 7-7-7"
+              />
+            </svg>
+            <svg
+              xmlns="http://www.w3.org/2000/svg"
+              class="accordion-chevron-up accordion-icon"
+              fill="none"
+              viewBox="0 0 24 24"
+              stroke="currentColor"
+              stroke-width="2"
+            >
+              <path
+                stroke-linecap="round"
+                stroke-linejoin="round"
+                d="M5 15l7-7 7 7"
+              />
+            </svg>
+          </label>
+        </div>
+        <div class="accordion acard">
+          <div class="framework-header">
+            <img src="img/logos/LightAutoML_logo_small.png" height="32px" />
+            <h3>LightAutoML</h3>
+            <div class="framework-links">
+              <a href="https://github.com/sb-ai-lab/LightAutoML" target="_blank"
+                ><img src="img/logos/GitHub-Mark-64px.png" height="24px"
+              /></a>
+              <a
+                href="https://lightautoml.readthedocs.io/en/latest/"
+                target="_blank"
+                >📖</a
+              >
+            </div>
+          </div>
+          <div>
+            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.
+          </div>
+          <input type="checkbox" id="more-lama" class="accordion-input" />
+          <div class="accordion-content">
+            <div>Paper to be added.</div>
+            <div class="paper">
+              <!-- <h3 class="paper-title">
+                Automating biomedical data science through tree-based pipeline
+                optimization
+              </h3> -->
+              <div class="paper-authors">
+                Alexander Ryzhkov, Anton Vakhrushev, Dmitry Simakov, Vasilii
+                Bunakov, Rinchin Damdinov, Alexander Kirilin, Pavel Shvets
+              </div>
+              <!-- <div class="paper-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.
+              </div>
+              <div class="paper-links">
+                <div class="hover-expand">
+                  <strong>2016</strong>
+                  <div>
+                    Applications of Evolutionary Computation, pages 123-137
+                  </div>
+                </div>
+                <a href="https://arxiv.org/pdf/1601.07925" target="_blank"
+                  >PDF</a
+                >
+                <a
+                  href="https://link.springer.com/chapter/10.1007/978-3-319-31204-0_9"
+                  target="_blank"
+                  >springer</a
+                >
+              </div> -->
+            </div>
+          </div>
+          <label for="more-lama">
+            <svg
+              xmlns="http://www.w3.org/2000/svg"
+              class="accordion-chevron-down accordion-icon"
+              fill="none"
+              viewBox="0 0 24 24"
+              stroke="currentColor"
+              stroke-width="2"
+            >
+              <path
+                stroke-linecap="round"
+                stroke-linejoin="round"
+                d="M19 9l-7 7-7-7"
+              />
+            </svg>
+            <svg
+              xmlns="http://www.w3.org/2000/svg"
+              class="accordion-chevron-up accordion-icon"
+              fill="none"
+              viewBox="0 0 24 24"
+              stroke="currentColor"
+              stroke-width="2"
+            >
+              <path
+                stroke-linecap="round"
+                stroke-linejoin="round"
+                d="M5 15l7-7 7 7"
+              />
+            </svg>
+          </label>
+        </div>
+        <div class="accordion acard">
+          <div class="framework-header">
+            <img src="img/logos/mljar.png" height="32px" />
+            <h3>mljar-supervised</h3>
+            <div class="framework-links">
+              <a
+                href="https://github.com/mljar/mljar-supervised/"
+                target="_blank"
+                ><img src="img/logos/GitHub-Mark-64px.png" height="24px"
+              /></a>
+              <a href="https://supervised.mljar.com" target="_blank">📖</a>
+            </div>
+          </div>
+          <div>
+            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).
+          </div>
+          <input type="checkbox" id="more-mljar" class="accordion-input" />
+          <div class="accordion-content">
+            <div>No paper available.</div>
+          </div>
+          <label for="more-mljar">
+            <svg
+              xmlns="http://www.w3.org/2000/svg"
+              class="accordion-chevron-down accordion-icon"
+              fill="none"
+              viewBox="0 0 24 24"
+              stroke="currentColor"
+              stroke-width="2"
+            >
+              <path
+                stroke-linecap="round"
+                stroke-linejoin="round"
+                d="M19 9l-7 7-7-7"
+              />
+            </svg>
+            <svg
+              xmlns="http://www.w3.org/2000/svg"
+              class="accordion-chevron-up accordion-icon"
+              fill="none"
+              viewBox="0 0 24 24"
+              stroke="currentColor"
+              stroke-width="2"
+            >
+              <path
+                stroke-linecap="round"
+                stroke-linejoin="round"
+                d="M5 15l7-7 7 7"
+              />
+            </svg>
+          </label>
+        </div>
+        <div class="accordion acard">
+          <div class="framework-header">
+            <img src="img/logos/tpot.jpeg" height="32px" />
+            <h3>TPOT</h3>
+            <div class="framework-links">
+              <a href="https://github.com/EpistasisLab/tpot" target="_blank"
+                ><img src="img/logos/GitHub-Mark-64px.png" height="24px"
+              /></a>
+              <a href="http://epistasislab.github.io/tpot/" target="_blank"
+                >📖</a
+              >
+            </div>
+          </div>
+          <div>
+            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.
+          </div>
+          <input type="checkbox" id="more-tpot" class="accordion-input" />
+          <div class="accordion-content">
+            <div class="paper">
+              <h3 class="paper-title">
+                Automating biomedical data science through tree-based pipeline
+                optimization
+              </h3>
+              <div class="paper-authors">
+                Randal S. Olson, Ryan J. Urbanowicz, Peter C. Andrews, Nicole A.
+                Lavender, La Creis Kidd, and Jason H. Moore
+              </div>
+              <div class="paper-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.
+              </div>
+              <div class="paper-links">
+                <div class="hover-expand">
+                  <strong>2016</strong>
+                  <div>
+                    Applications of Evolutionary Computation, pages 123-137
+                  </div>
+                </div>
+                <a href="https://arxiv.org/pdf/1601.07925" target="_blank"
+                  >PDF</a
+                >
+                <a
+                  href="https://link.springer.com/chapter/10.1007/978-3-319-31204-0_9"
+                  target="_blank"
+                  >springer</a
+                >
+              </div>
+            </div>
+          </div>
+          <label for="more-tpot">
+            <svg
+              xmlns="http://www.w3.org/2000/svg"
+              class="accordion-chevron-down accordion-icon"
+              fill="none"
+              viewBox="0 0 24 24"
+              stroke="currentColor"
+              stroke-width="2"
+            >
+              <path
+                stroke-linecap="round"
+                stroke-linejoin="round"
+                d="M19 9l-7 7-7-7"
+              />
+            </svg>
+            <svg
+              xmlns="http://www.w3.org/2000/svg"
+              class="accordion-chevron-up accordion-icon"
+              fill="none"
+              viewBox="0 0 24 24"
+              stroke="currentColor"
+              stroke-width="2"
+            >
+              <path
+                stroke-linecap="round"
+                stroke-linejoin="round"
+                d="M5 15l7-7 7 7"
+              />
+            </svg>
+          </label>
+        </div>
+      </section>
+    </div>
+  </body>
+  <footer>
+    © 2022 Copyright, the AutoML Benchmark team.<br />
+    Icons of AutoML frameworks and OpenML by their respective owners. <br />
+    Spotted an issue?
+    <a href="https://github.com/openml/automlbenchmark" target="_blank"
+      >Tell us on Github or open a pull request</a
+    >!
+  </footer>
+</html>
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diff --git a/docs/img/logos/autogluon.png b/docs/img/logos/autogluon.png
new file mode 100644
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diff --git a/docs/img/logos/mljar.png b/docs/img/logos/mljar.png
new file mode 100644
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diff --git a/docs/index.html b/docs/index.html
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+<html>
+  <head>
+    <meta charset="utf-8" />
+    <title>AMLB: an AutoML Benchmark</title>
+    <meta name="viewport" content="width=device-width, initial-scale=1" />
+    <link rel="preconnect" href="https://fonts.googleapis.com" />
+    <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin />
+    <link
+      href="https://fonts.googleapis.com/css2?family=Inconsolata&family=Lato:ital,wght@0,400;0,700;1,400&display=swap"
+      rel="stylesheet"
+    />
+    <link href="style.css" rel="stylesheet" />
+  </head>
+  <body>
+    <div class="mobile">
+      <div class="title">AMLB</div>
+      <div class="summary">
+        We are working on mobile-friendly site. In the mean time, have a look at
+        our <a href="">paper</a> or visit the page from a larger screen. Sorry
+        for the inconvenience.
+      </div>
+    </div>
+    <div class="desktop">
+      <nav class="navigation-bar">
+        <a href="index.html">
+          <svg
+            xmlns="http://www.w3.org/2000/svg"
+            class="nav-icon"
+            viewBox="0 0 20 20"
+            fill="currentColor"
+          >
+            <path
+              d="M10.707 2.293a1 1 0 00-1.414 0l-7 7a1 1 0 001.414 1.414L4 10.414V17a1 1 0 001 1h2a1 1 0 001-1v-2a1 1 0 011-1h2a1 1 0 011 1v2a1 1 0 001 1h2a1 1 0 001-1v-6.586l.293.293a1 1 0 001.414-1.414l-7-7z"
+            />
+          </svg>
+        </a>
+        <a href="results.html" class="nav-link">Results</a>
+        <a href="frameworks.html" class="nav-link">Frameworks</a>
+        <a href="papers.html" class="nav-link">papers</a>
+        <a
+          href="https://github.com/openml/automlbenchmark#readme"
+          target="_blank"
+          class="nav-link"
+          >user guide
+          <svg
+            xmlns="http://www.w3.org/2000/svg"
+            class="nav-icon-with-text"
+            fill="none"
+            viewBox="0 0 24 24"
+            stroke="currentColor"
+            stroke-width="2"
+          >
+            <path
+              stroke-linecap="round"
+              stroke-linejoin="round"
+              d="M10 6H6a2 2 0 00-2 2v10a2 2 0 002 2h10a2 2 0 002-2v-4M14 4h6m0 0v6m0-6L10 14"
+            />
+          </svg>
+        </a>
+        <a
+          href="https://github.com/openml/automlbenchmark/discussions/216"
+          target="_blank"
+          class="nav-link"
+          >Discussions
+          <svg
+            xmlns="http://www.w3.org/2000/svg"
+            class="nav-icon-with-text"
+            fill="none"
+            viewBox="0 0 24 24"
+            stroke="currentColor"
+            stroke-width="2"
+          >
+            <path
+              stroke-linecap="round"
+              stroke-linejoin="round"
+              d="M10 6H6a2 2 0 00-2 2v10a2 2 0 002 2h10a2 2 0 002-2v-4M14 4h6m0 0v6m0-6L10 14"
+            />
+          </svg>
+        </a>
+        <a
+          href="https://github.com/openml/automlbenchmark/"
+          target="_blank"
+          class="nav-icon"
+        >
+          <img src="img/logos/GitHub-Mark-Light-64px.png" height="100%" />
+        </a>
+      </nav>
+      <!-- <div class="construction-banner">
+      🚧 This website is under construction 🚧
+    </div> -->
+      <div class="page-content">
+        <section>
+          <div class="title">AMLB</div>
+          <div class="subtitle">An AutoML Benchmark</div>
+          <div class="summary">
+            Comparing different AutoML frameworks is notoriously challenging.
+            AMLB is an open and extensible benchmark that follows best practices
+            and avoids common mistakes when comparing AutoML frameworks.
+          </div>
+        </section>
+        <section>
+          <div class="three-cols">
+            <div class="card">
+              <h3>Easy to Use</h3>
+              <div class="grey">
+                You can run an entire benchmark with a single command! The
+                AutoML benchmark tool automates the installation of the AutoML
+                framework, the experimental setup, and executing the experiment.
+              </div>
+              <div class="terminal">
+                > python runbenchmark.py autosklearn openml/s/269 1h8c
+              </div>
+              <div class="flex-grow"></div>
+              <a
+                class="card-nav"
+                href="https://github.com/openml/automlbenchmark#installation"
+                target="_blank"
+              >
+                <svg
+                  xmlns="http://www.w3.org/2000/svg"
+                  class="h-6 w-6"
+                  fill="none"
+                  viewBox="0 0 24 24"
+                  stroke="currentColor"
+                  stroke-width="2"
+                >
+                  <path
+                    stroke-linecap="round"
+                    stroke-linejoin="round"
+                    d="M17 8l4 4m0 0l-4 4m4-4H3"
+                  />
+                </svg>
+                Installation guide
+              </a>
+            </div>
+            <div class="card">
+              <h3>Visualize Results</h3>
+              <div class="grey">
+                The results can be visualized with our
+                <a
+                  href="#https://compstat-lmu.shinyapps.io/AutoML-Benchmark-Analysis/"
+                  target="_blank"
+                  >interactive visualization tool</a
+                >
+                or one of our
+                <a
+                  href="https://github.com/openml/automlbenchmark/tree/master/reports"
+                  >notebooks</a
+                >. This includes stripplots, critical difference diagrams,
+                Bradley-Terry trees, and more!
+              </div>
+              <img src="img/shiny.png" width="100%" />
+              <div class="flex-grow"></div>
+              <a class="card-nav" href="results.html" target="_blank">
+                <svg
+                  xmlns="http://www.w3.org/2000/svg"
+                  class="h-6 w-6"
+                  fill="none"
+                  viewBox="0 0 24 24"
+                  stroke="currentColor"
+                  stroke-width="2"
+                >
+                  <path
+                    stroke-linecap="round"
+                    stroke-linejoin="round"
+                    d="M17 8l4 4m0 0l-4 4m4-4H3"
+                  />
+                </svg>
+                Results
+              </a>
+            </div>
+            <div class="card">
+              <h3>Easy to Extend</h3>
+              <div class="grey">
+                <a
+                  href="https://github.com/openml/automlbenchmark/blob/master/docs/HOWTO.md#add-a-benchmark"
+                  >Adding a framework</a
+                >
+                and
+                <a
+                  href="https://github.com/openml/automlbenchmark/blob/master/docs/HOWTO.md#add-a-benchmark"
+                  >adding a dataset</a
+                >
+                is easy. These extensions can be kept completely private, or
+                shared with the community. For datasets, it is even possible to
+                work with
+                <a href="https://www.openml.org" target="_blank">OpenML</a>
+                tasks and suites directly!
+              </div>
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227.396 314.975,226.920 315.182,226.616 315.874 C 226.405 316.355,225.820 316.936,225.316 317.165 C 224.558 317.511,224.400 317.894,224.400 319.391 C 224.400 321.058,224.321 321.200,223.392 321.200 L 222.384 321.200 222.492 329.500 C 222.599 337.706,222.610 337.801,223.479 337.925 C 224.221 338.031,224.377 338.344,224.479 339.925 C 224.582 341.521,224.734 341.819,225.500 341.928 C 226.083 342.011,226.400 342.331,226.400 342.838 C 226.400 343.715,227.803 344.400,229.598 344.400 C 230.482 344.400,230.774 344.606,230.872 345.300 C 231.000 346.199,231.008 346.200,239.000 346.200 C 246.992 346.200,247.000 346.199,247.128 345.300 C 247.226 344.606,247.518 344.400,248.402 344.400 C 250.197 344.400,251.600 343.715,251.600 342.838 C 251.600 342.331,251.917 342.011,252.500 341.928 C 253.266 341.819,253.418 341.521,253.521 339.925 C 253.623 338.344,253.779 338.031,254.521 337.925 C 255.390 337.801,255.401 337.706,255.508 329.500 L 255.616 321.200 254.608 321.200 C 253.668 321.200,253.600 321.068,253.600 319.243 C 253.600 317.548,253.480 317.256,252.700 317.060 C 252.205 316.935,251.620 316.421,251.400 315.917 C 251.077 315.177,250.634 314.977,249.100 314.878 C 247.385 314.768,247.200 314.661,247.200 313.778 L 247.200 312.800 239.000 312.800 L 230.800 312.800 230.800 313.778 M178.500 317.065 C 178.225 317.176,178.000 317.702,178.000 318.233 C 178.000 319.002,177.792 319.200,176.982 319.200 C 175.671 319.200,174.000 320.871,174.000 322.182 C 174.000 322.896,173.785 323.200,173.280 323.200 C 171.745 323.200,171.600 323.910,171.600 331.447 C 171.600 339.546,171.677 340.000,173.043 340.000 C 173.743 340.000,174.000 340.221,174.000 340.826 C 174.000 342.613,175.525 344.365,177.100 344.387 C 177.712 344.396,178.000 344.642,178.000 345.157 C 178.000 346.300,178.674 346.400,186.400 346.400 C 194.126 346.400,194.800 346.300,194.800 345.157 C 194.800 344.660,195.082 344.400,195.623 344.400 C 196.691 344.400,198.800 342.084,198.800 340.911 C 198.800 340.217,199.028 340.000,199.757 340.000 C 201.123 340.000,201.200 339.546,201.200 331.447 C 201.200 323.910,201.055 323.200,199.520 323.200 C 199.045 323.200,198.800 322.901,198.800 322.322 C 198.800 321.149,196.881 319.200,195.725 319.200 C 195.016 319.200,194.800 318.976,194.800 318.243 C 194.800 317.716,194.515 317.176,194.167 317.043 C 193.414 316.754,179.221 316.774,178.500 317.065 "
+                    stroke="none"
+                    fill="#0464fc"
+                    fill-rule="evenodd"
+                  ></path>
+                </g>
+              </svg>
+              <div class="flex-grow"></div>
+              <a
+                class="card-nav"
+                href="https://github.com/openml/automlbenchmark/blob/master/docs/HOWTO.md#how-to"
+                target="_blank"
+              >
+                <svg
+                  xmlns="http://www.w3.org/2000/svg"
+                  class="h-6 w-6"
+                  fill="none"
+                  viewBox="0 0 24 24"
+                  stroke="currentColor"
+                  stroke-width="2"
+                >
+                  <path
+                    stroke-linecap="round"
+                    stroke-linejoin="round"
+                    d="M17 8l4 4m0 0l-4 4m4-4H3"
+                  />
+                </svg>
+                Extending the benchmark
+              </a>
+            </div>
+          </div>
+        </section>
+        <section>
+          <div class="two-col">
+            <div class="flat-card">
+              <h3>📄 Paper</h3>
+              <div>
+                A preprint of our most recent paper is available on
+                <a href="#">ArXiv</a>. It includes an in-depth discussion of the
+                different design decisions and its limiations, as well as a
+                multi-faceted analysis of results from large scale comparison
+                across 9 frameworks on more than 100 tasks.
+                <!-- https://arxiv.org/abs/1907.00909 -->
+              </div>
+            </div>
+            <div class="flat-card">
+              <h3>🧑‍💻 Code</h3>
+              <div>
+                The entire benchmark tool is open source and developed on
+                <a
+                  href="https://github.com/openml/automlbenchmark"
+                  target="_blank"
+                  >Github</a
+                >. The Github discussion board and issue trackers are the main
+                way for us to interact with the community.
+              </div>
+            </div>
+          </div>
+          <!-- <h2>Paper <img src=/></h2> -->
+        </section>
+        <section>
+          <h2>AutoML Frameworks</h2>
+          <div>
+            <div class="grey">
+              Many AutoML frameworks are already integrated with the AutoML
+              benchmark tool and
+              <a href="#ADDFRAMEWORK">adding more is easy</a>. We have more
+              information about the different frameworks on our
+              <a href="#FRAMEWORKS">framework overview page</a>. The icons below
+              link directly to their respective Github repositories.
+            </div>
+            <div class="icon-flex">
+              <a
+                href="https://github.com/openml-labs/gama"
+                target="_blank"
+                class="framework-logo"
+                ><img src="img/logos/GAMA-icon.png" title="GAMA"
+              /></a>
+              <a href="https://github.com/EpistasisLab/tpot" target="_blank"
+                ><img src="img/logos/tpot.jpeg" title="TPOT"
+              /></a>
+              <a href="https://github.com/awslabs/autogluon" target="_blank"
+                ><img src="img/logos/autogluon.png" title="AutoGluon"
+              /></a>
+              <a
+                href="https://github.com/automl/auto-sklearn"
+                target="_blank"
+                class="framework-logo"
+                ><img src="img/logos/auto-sklearn.png" title="auto-sklearn"
+              /></a>
+              <a
+                href="https://github.com/mljar/mljar-supervised"
+                target="_blank"
+                ><img src="img/logos/mljar.png" title="mljar-supervised"
+              /></a>
+              <a href="https://github.com/h2oai/h2o-3" target="_blank"
+                ><img src="img/logos/h2o-automl-logo.jpeg" title="H2O AutoML"
+              /></a>
+              <a
+                href="https://github.com/sb-ai-lab/LightAutoML"
+                target="_blank"
+                class="framework-logo"
+                ><img
+                  src="img/logos/LightAutoML_logo_small.png"
+                  title="Light AutoML"
+              /></a>
+              <a href="https://github.com/microsoft/FLAML" target="_blank"
+                ><img src="img/logos/flaml.svg" title="FLAML"
+              /></a>
+            </div>
+            <div></div>
+          </div>
+        </section>
+        <section>
+          <h2>Community</h2>
+          <div class="grey">
+            We welcome any contributions to the AutoML benchmark. Our goal is to
+            provide the best benchmark tools for AutoML research and we can't do
+            that on our own. Contributions are appreciated in many forms,
+            including feedback on the benchmark design, feature requests, bug
+            reports, code and documentation contributions, and more. Why not
+            stop by on our
+            <a
+              href="https://github.com/openml/automlbenchmark/discussions/216"
+              target="_blank"
+              >welcome board</a
+            >
+            and let us know what got you interested in the benchmark?
+          </div>
+        </section>
+      </div>
+    </div>
+  </body>
+  <footer>
+    © 2022 Copyright, the AutoML Benchmark team.<br />
+    Icons of AutoML frameworks and OpenML by their respective owners. <br />
+    Spotted an issue?
+    <a href="https://github.com/openml/automlbenchmark" target="_blank"
+      >Tell us on Github or open a pull request</a
+    >!
+  </footer>
+</html>
diff --git a/docs/papers.html b/docs/papers.html
new file mode 100644
index 000000000..0b02b5930
--- /dev/null
+++ b/docs/papers.html
@@ -0,0 +1,209 @@
+<html>
+  <head>
+    <meta charset="utf-8" />
+    <title>AMLB: Papers</title>
+    <link rel="preconnect" href="https://fonts.googleapis.com" />
+    <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin />
+    <link
+      href="https://fonts.googleapis.com/css2?family=Inconsolata&family=Lato:ital,wght@0,400;0,700;1,400&display=swap"
+      rel="stylesheet"
+    />
+    <link href="style.css" rel="stylesheet" />
+  </head>
+  <body>
+    <nav class="navigation-bar">
+      <a href="index.html">
+        <svg
+          xmlns="http://www.w3.org/2000/svg"
+          class="nav-icon"
+          viewBox="0 0 20 20"
+          fill="currentColor"
+        >
+          <path
+            d="M10.707 2.293a1 1 0 00-1.414 0l-7 7a1 1 0 001.414 1.414L4 10.414V17a1 1 0 001 1h2a1 1 0 001-1v-2a1 1 0 011-1h2a1 1 0 011 1v2a1 1 0 001 1h2a1 1 0 001-1v-6.586l.293.293a1 1 0 001.414-1.414l-7-7z"
+          />
+        </svg>
+      </a>
+      <a href="results.html" class="nav-link">Results</a>
+      <a href="frameworks.html" class="nav-link">Frameworks</a>
+      <a href="papers.html" class="nav-link">papers</a>
+      <a
+        href="https://github.com/openml/automlbenchmark#readme"
+        target="_blank"
+        class="nav-link"
+        >user guide
+        <svg
+          xmlns="http://www.w3.org/2000/svg"
+          class="nav-icon-with-text"
+          fill="none"
+          viewBox="0 0 24 24"
+          stroke="currentColor"
+          stroke-width="2"
+        >
+          <path
+            stroke-linecap="round"
+            stroke-linejoin="round"
+            d="M10 6H6a2 2 0 00-2 2v10a2 2 0 002 2h10a2 2 0 002-2v-4M14 4h6m0 0v6m0-6L10 14"
+          />
+        </svg>
+      </a>
+      <a
+        href="https://github.com/openml/automlbenchmark/discussions/216"
+        target="_blank"
+        class="nav-link"
+        >Discussions
+        <svg
+          xmlns="http://www.w3.org/2000/svg"
+          class="nav-icon-with-text"
+          fill="none"
+          viewBox="0 0 24 24"
+          stroke="currentColor"
+          stroke-width="2"
+        >
+          <path
+            stroke-linecap="round"
+            stroke-linejoin="round"
+            d="M10 6H6a2 2 0 00-2 2v10a2 2 0 002 2h10a2 2 0 002-2v-4M14 4h6m0 0v6m0-6L10 14"
+          />
+        </svg>
+      </a>
+      <a
+        href="https://github.com/openml/automlbenchmark/"
+        target="_blank"
+        class="nav-icon"
+      >
+        <img src="img/logos/GitHub-Mark-Light-64px.png" height="100%" />
+      </a>
+    </nav>
+    <!-- <div class="construction-banner">
+      🚧 This website is under construction 🚧
+    </div> -->
+    <div class="page-content">
+      <h1 class="page-title">Papers</h1>
+      <div class="paper">
+        <h2 class="paper-title">AMLB: an AutoML Benchmark</h2>
+        <div class="paper-authors">
+          Pieter Gijsbers, Marcos L. P. Bueno, Stefan Coors, Erin LeDell,
+          Sébastien Poirier, Janek Thomas, Bernd Bischl and Joaquin Vanschoren
+        </div>
+
+        <!-- <div class="paper-year">2019</div>
+      <div class="paper-venue">ICML 2019 AutoML Workshop</div>
+      <div class="paper-venue">cite</div> -->
+
+        <div class="paper-abstract">
+          Comparing different AutoML frameworks is notoriously challenging and
+          often done incorrectly. We introduce an open and extensible benchmark
+          that follows best practices and avoids common mistakes when comparing
+          AutoML frameworks. We conduct a thorough comparison of 9 well-known
+          AutoML frameworks across 71 classification and 33 regression tasks.
+          The differences between the AutoML frameworks are explored with a
+          multi-faceted analysis, evaluating model accuracy, its trade-offs with
+          inference time, and framework failures. We also use Bradley-Terry
+          trees to discover subsets of tasks where the relative AutoML framework
+          rankings differ. The benchmark comes with an open-source tool that
+          integrates with many AutoML frameworks and automates the empirical
+          evaluation process end-to-end: from framework installation and
+          resource allocation to in-depth evaluation. The benchmark uses public
+          data sets, can be easily extended with other AutoML frameworks and
+          tasks, and has a website with up-to-date results.
+        </div>
+        <div class="paper-links">
+          <div class="hover-expand">
+            <strong>2022</strong>
+            <div>Under Review</div>
+          </div>
+          <p>Links to be added after preprint publication.</p>
+          <!-- <a class="flat-button">
+            <label for="cite-2022">CITE</label>
+          </a>
+          <a
+            href="https://www.automl.org/wp-content/uploads/2019/06/automlws2019_Paper45.pdf"
+            target="_blank"
+            >PDF</a
+          >
+          <a href="https://arxiv.org/abs/1907.00909" target="_blank">ArXiv</a>
+        </div>
+        <div>
+          <input
+            type="checkbox"
+            id="cite-2022"
+            class="hidden-content-checkbox"
+          />
+          <div class="hidden-content bibtex">
+            @article{amlb2019, <br />
+            &emsp; title={An Open Source AutoML Benchmark},<br />
+            &emsp; author={Gijsbers, P. and LeDell, E. and Poirier, S. and
+            Thomas, J. and Bischl, B. and Vanschoren, J.}, <br />
+            &emsp; journal={arXiv preprint arXiv:1907.00909 [cs.LG]}, <br />
+            &emsp; url={https://arxiv.org/abs/1907.00909}, <br />
+            &emsp; note={Accepted at AutoML Workshop at ICML 2019}, <br />
+            &emsp; year={2019} <br />}
+          </div> -->
+        </div>
+      </div>
+      <div class="paper">
+        <h2 class="paper-title">An Open Source AutoML Benchmark</h2>
+        <div class="paper-authors">
+          Pieter Gijsbers, Erin LeDell, Janek Thomas, Sébastien Poirier, Bernd
+          Bischl, Joaquin Vanschoren
+        </div>
+
+        <!-- <div class="paper-year">2019</div>
+      <div class="paper-venue">ICML 2019 AutoML Workshop</div>
+      <div class="paper-venue">cite</div> -->
+
+        <div class="paper-abstract">
+          In recent years, an active field of research has developed around
+          automated machine learning (AutoML). Unfortunately, comparing
+          different AutoML systems is hard and often done incorrectly. We
+          introduce an open, ongoing, and extensible benchmark framework which
+          follows best practices and avoids common mistakes. The framework is
+          open-source, uses public datasets and has a website with up-to-date
+          results. We use the framework to conduct a thorough comparison of 4
+          AutoML systems across 39 datasets and analyze the results.
+        </div>
+        <div class="paper-links">
+          <div class="hover-expand">
+            <strong>2019</strong>
+            <div>ICML 2019 AutoML Workshop</div>
+          </div>
+          <a class="flat-button">
+            <label for="cite-2019">CITE</label>
+          </a>
+          <a
+            href="https://www.automl.org/wp-content/uploads/2019/06/automlws2019_Paper45.pdf"
+            target="_blank"
+            >PDF</a
+          >
+          <a href="https://arxiv.org/abs/1907.00909" target="_blank">ArXiv</a>
+        </div>
+        <div>
+          <input
+            type="checkbox"
+            id="cite-2019"
+            class="hidden-content-checkbox"
+          />
+          <div class="hidden-content bibtex">
+            @article{amlb2019, <br />
+            &emsp; title={An Open Source AutoML Benchmark},<br />
+            &emsp; author={Gijsbers, P. and LeDell, E. and Poirier, S. and
+            Thomas, J. and Bischl, B. and Vanschoren, J.}, <br />
+            &emsp; journal={arXiv preprint arXiv:1907.00909 [cs.LG]}, <br />
+            &emsp; url={https://arxiv.org/abs/1907.00909}, <br />
+            &emsp; note={Accepted at AutoML Workshop at ICML 2019}, <br />
+            &emsp; year={2019} <br />}
+          </div>
+        </div>
+      </div>
+    </div>
+  </body>
+  <footer>
+    © 2022 Copyright, the AutoML Benchmark team.<br />
+    Icons of AutoML frameworks and OpenML by their respective owners. <br />
+    Spotted an issue?
+    <a href="https://github.com/openml/automlbenchmark" target="_blank"
+      >Tell us on Github or open a pull request</a
+    >!
+  </footer>
+</html>
diff --git a/docs/results.html b/docs/results.html
new file mode 100644
index 000000000..243fd8fd3
--- /dev/null
+++ b/docs/results.html
@@ -0,0 +1,184 @@
+<html>
+  <head>
+    <meta charset="utf-8" />
+    <title>AMLB: Results</title>
+    <link rel="preconnect" href="https://fonts.googleapis.com" />
+    <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin />
+    <link
+      href="https://fonts.googleapis.com/css2?family=Inconsolata&family=Lato:ital,wght@0,400;0,700;1,400&display=swap"
+      rel="stylesheet"
+    />
+    <link href="style.css" rel="stylesheet" />
+  </head>
+  <body>
+    <nav class="navigation-bar">
+      <a href="index.html">
+        <svg
+          xmlns="http://www.w3.org/2000/svg"
+          class="nav-icon"
+          viewBox="0 0 20 20"
+          fill="currentColor"
+        >
+          <path
+            d="M10.707 2.293a1 1 0 00-1.414 0l-7 7a1 1 0 001.414 1.414L4 10.414V17a1 1 0 001 1h2a1 1 0 001-1v-2a1 1 0 011-1h2a1 1 0 011 1v2a1 1 0 001 1h2a1 1 0 001-1v-6.586l.293.293a1 1 0 001.414-1.414l-7-7z"
+          />
+        </svg>
+      </a>
+      <a href="results.html" class="nav-link">Results</a>
+      <a href="frameworks.html" class="nav-link">Frameworks</a>
+      <a href="papers.html" class="nav-link">papers</a>
+      <a
+        href="https://github.com/openml/automlbenchmark#readme"
+        target="_blank"
+        class="nav-link"
+        >user guide
+        <svg
+          xmlns="http://www.w3.org/2000/svg"
+          class="nav-icon-with-text"
+          fill="none"
+          viewBox="0 0 24 24"
+          stroke="currentColor"
+          stroke-width="2"
+        >
+          <path
+            stroke-linecap="round"
+            stroke-linejoin="round"
+            d="M10 6H6a2 2 0 00-2 2v10a2 2 0 002 2h10a2 2 0 002-2v-4M14 4h6m0 0v6m0-6L10 14"
+          />
+        </svg>
+      </a>
+      <a
+        href="https://github.com/openml/automlbenchmark/discussions/216"
+        target="_blank"
+        class="nav-link"
+        >Discussions
+        <svg
+          xmlns="http://www.w3.org/2000/svg"
+          class="nav-icon-with-text"
+          fill="none"
+          viewBox="0 0 24 24"
+          stroke="currentColor"
+          stroke-width="2"
+        >
+          <path
+            stroke-linecap="round"
+            stroke-linejoin="round"
+            d="M10 6H6a2 2 0 00-2 2v10a2 2 0 002 2h10a2 2 0 002-2v-4M14 4h6m0 0v6m0-6L10 14"
+          />
+        </svg>
+      </a>
+      <a
+        href="https://github.com/openml/automlbenchmark/"
+        target="_blank"
+        class="nav-icon"
+      >
+        <img src="img/logos/GitHub-Mark-Light-64px.png" height="100%" />
+      </a>
+    </nav>
+    <!-- <div class="construction-banner">
+      🚧 This website is under construction 🚧
+    </div> -->
+    <div class="page-content">
+      <section>
+        <h1 class="page-title">Results</h1>
+        <div class="accordion acard">
+          <div>
+            ⚠️ <a href="papers.html">Our paper</a> outlines
+            <strong>important limitations</strong> for the interpretation of
+            results. These limitations include:
+          </div>
+          <input
+            type="checkbox"
+            id="more-limitations"
+            class="accordion-input"
+          />
+          <div class="accordion-content">
+            <ul class="limitations-list">
+              <li>
+                We use AutoML framework versions from
+                <strong>September 2021</strong>, many frameworks have since seen
+                major updates.
+              </li>
+              <li>
+                We use the "benchmark" modes of the frameworks, which generally
+                <em>only</em> optimize for performance. Most AutoML frameworks
+                have multiple modes to support different use cases.
+              </li>
+              <li>
+                Results can not be used to make conclusions about which
+                algorithm is best, as all frameworks differ in multiple ways.
+              </li>
+              <li>
+                Performance statistics are often independent from many
+                qualitative differences, such as ease of use or
+                interpretability.
+              </li>
+            </ul>
+            Please read Section 5.3 in our paper for a more elaborate discussion
+            of these and other limitations.
+          </div>
+          <label for="more-limitations">
+            <svg
+              xmlns="http://www.w3.org/2000/svg"
+              class="accordion-chevron-down accordion-icon"
+              fill="none"
+              viewBox="0 0 24 24"
+              stroke="currentColor"
+              stroke-width="2"
+            >
+              <path
+                stroke-linecap="round"
+                stroke-linejoin="round"
+                d="M19 9l-7 7-7-7"
+              />
+            </svg>
+            <svg
+              xmlns="http://www.w3.org/2000/svg"
+              class="accordion-chevron-up accordion-icon"
+              fill="none"
+              viewBox="0 0 24 24"
+              stroke="currentColor"
+              stroke-width="2"
+            >
+              <path
+                stroke-linecap="round"
+                stroke-linejoin="round"
+                d="M5 15l7-7 7 7"
+              />
+            </svg>
+          </label>
+        </div>
+      </section>
+      <section class="results">
+        <div>
+          All results are available as
+          <a href="https://test.openml.org/amlb/" target="_blank">raw files</a>
+          📂, but we open source the tools we used for generating the figures in
+          our paper. The best way to explore the results is through our
+          interactive
+          <a
+            href="https://compstat-lmu.shinyapps.io/AutoML-Benchmark-Analysis/"
+            target="_blank"
+            >Shiny app</a
+          >. It loads the latest results by default. It is also possible to use
+          our
+          <a
+            href="https://github.com/openml/automlbenchmark/tree/master/reports"
+            target="_blank"
+            >notebooks</a
+          >
+          which contain additional visualizations.
+        </div>
+        <img src="img/shiny.png" height="320px" class="zoomable" />
+      </section>
+    </div>
+  </body>
+  <footer>
+    © 2022 Copyright, the AutoML Benchmark team.<br />
+    Icons of AutoML frameworks and OpenML by their respective owners. <br />
+    Spotted an issue?
+    <a href="https://github.com/openml/automlbenchmark" target="_blank"
+      >Tell us on Github or open a pull request</a
+    >!
+  </footer>
+</html>
diff --git a/docs/style.css b/docs/style.css
new file mode 100644
index 000000000..0bce2c9dc
--- /dev/null
+++ b/docs/style.css
@@ -0,0 +1,484 @@
+* {
+  margin: 0;
+  padding: 0;
+  box-sizing: border-box;
+  font-size: 18px;
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+  color: #495057;
+}
+
+/* main: #1971c2 */
+
+body {
+  font-family: "lato", sans-serif;
+  color: #343a40;
+}
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+section {
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+}
+
+a {
+  color: 1971c2;
+}
+
+.page-content {
+  padding: 0 clamp(15%, 100%, calc((100vw - 1000px) / 2)); /* should maybe set width in px instead */
+}
+
+h1 {
+  font-size: 72px;
+  padding-bottom: 24px;
+}
+
+h2 {
+  font-size: 48px;
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+  font-size: 14px;
+}
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+.terminal {
+  background-color: #343a40;
+  color: #f8f9fa;
+  font-family: "Inconsolata", sans-serif;
+  padding: 8px;
+  margin: 16px 5%;
+}
+
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+  background-color: #1c7ed6;
+  color: white;
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+  color: white;
+  text-transform: uppercase;
+
+  display: flex;
+  gap: 24px;
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+  align-items: center;
+  padding: 16px;
+  height: 60px;
+}
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+nav a:first-child {
+  flex-grow: 1;
+}
+
+.nav-icon {
+  height: 24px;
+  fill: white;
+}
+
+.nav-icon-with-text {
+  height: 18px;
+}
+
+.nav-icon:hover,
+.nav-icon:active {
+  height: 28px;
+}
+
+.nav-link:link,
+.nav-link:visited {
+  text-decoration: none;
+  color: white;
+}
+.nav-link:hover,
+.nav-link:active {
+  color: white;
+  font-weight: bold;
+}
+
+/* TITLE */
+
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+  color: #343a40;
+  text-align: center;
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+  font-weight: bold;
+}
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+  font-weight: bold;
+  padding-bottom: 24px;
+}
+
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+  font-size: 24px;
+  color: #495057;
+  padding: 0 max((100% - 900px) / 2, 0px); /* does not seem to work with just the math expression */
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+  grid-template-columns: repeat(3, 1fr);
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+  padding: 16px;
+
+  display: flex;
+  flex-direction: column;
+}
+
+.card img {
+  padding: 16px 2%;
+}
+
+.card > svg {
+  display: block;
+  margin: 16px auto;
+}
+
+.flex-grow {
+  flex-grow: 1;
+}
+
+.card > .card-nav {
+  color: #1971c2;
+  text-decoration: none;
+
+  display: flex;
+  align-items: center;
+  justify-content: flex-start;
+  gap: 8px;
+}
+
+.card > .card-nav > svg {
+  height: 24px;
+  fill: #1971c2;
+}
+
+h3 {
+  font-size: 24px;
+  padding-bottom: 10px;
+}
+
+/* Frameworks */
+/* .icon-grid {
+  padding: 24px;
+  display: grid;
+  grid-auto-rows: 60px;
+} */
+.icon-flex {
+  margin-top: 24px;
+  display: flex;
+  flex-wrap: wrap;
+  justify-content: center;
+  align-items: center;
+  gap: 40px;
+}
+
+.icon-flex a img {
+  height: 90px;
+}
+/* Paper */
+
+.two-col {
+  display: grid;
+  grid-template-columns: 1fr 1fr;
+  column-gap: 2%;
+
+  background-color: #1971c2;
+  color: #e7f5ff;
+  border-radius: 5px;
+}
+
+.two-col a {
+  color: white;
+}
+
+.two-col h3 {
+  color: white;
+}
+
+.flat-card {
+  padding: 16px;
+}
+
+/* FRAMEWORKS PAGE */
+.page-title {
+  margin-top: 72px;
+}
+
+.framework-card-list {
+  background-color: #e7f5ff;
+  padding: 32px 5%;
+  border-radius: 10px;
+}
+
+.framework-card-list > .accordion {
+  margin-bottom: 32px;
+}
+/* 
+.framework-card {
+  background-color: white;
+  margin-bottom: 64;
+  padding: 32;
+  border-radius: 10px;
+} */
+.framework-card:last-of-type {
+  margin-bottom: 0;
+}
+
+.framework-header {
+  display: flex;
+  align-items: center;
+  gap: 16px;
+}
+
+.framework-header > h3 {
+  padding-bottom: 0px;
+  font-size: 32px;
+}
+
+.framework-links {
+  display: flex;
+  gap: 16px;
+  justify-content: flex-end;
+
+  flex-grow: 1;
+}
+
+.framework-links > a {
+  text-decoration: none;
+  font-size: 24px;
+}
+
+/* RESULTS */
+
+.limitations-list {
+  padding: 0px 32px 16px 32px;
+}
+
+.limitations-list li {
+  padding: 8px 0px;
+}
+
+.results {
+  display: flex;
+  gap: 40px;
+}
+
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+.accordion {
+}
+
+.acard {
+  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+  border-radius: 10px;
+  background-color: white;
+}
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+  padding: 8px 32px;
+}
+
+.accordion > div:first-child {
+  padding-top: 16px;
+}
+
+.accordion > label {
+  width: 100%;
+  display: flex;
+  justify-content: center;
+}
+
+.accordion-input {
+  display: none;
+}
+
+.accordion-icon {
+  height: 24px;
+}
+
+.accordion-input:checked ~ .accordion-content {
+  display: block;
+}
+
+.accordion-input:checked ~ label .accordion-chevron-down {
+  display: none;
+}
+
+.accordion-input:checked ~ label .accordion-chevron-up {
+  display: block;
+}
+
+.accordion-input ~ .accordion-content {
+  display: none;
+}
+
+.accordion-input ~ label .accordion-chevron-up {
+  display: none;
+}
+
+.accordion-input ~ label .accordion-chevron-down {
+  display: block;
+}
+
+.accordion > label {
+  background: linear-gradient(0deg, rgba(0, 0, 0, 0.02), white);
+  border-radius: 10px;
+  padding-bottom: 8px;
+}
+
+.accordion > label:hover {
+  cursor: pointer;
+}
+
+/* Papers */
+.paper {
+  margin-bottom: 48px;
+}
+
+.paper-title {
+  padding-bottom: 0px;
+  margin-bottom: 0px;
+}
+
+.paper-links {
+  display: flex;
+  align-items: center;
+  gap: 12px;
+
+  margin-bottom: 16px;
+}
+
+.paper-links > a {
+  color: white;
+  background-color: #1c7ed6;
+  padding: 4px 12px;
+  border-radius: 4px;
+
+  text-decoration: none;
+  text-transform: uppercase;
+}
+
+.paper-year {
+  color: #343a40;
+  display: inline;
+  font-weight: bold;
+}
+
+.paper-venue {
+  color: #495057;
+  display: inline;
+}
+
+.paper-authors {
+  color: #4263eb;
+  padding-bottom: 8px;
+}
+
+.paper-abstract {
+  color: #495057;
+  padding-bottom: 16px;
+}
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+  display: flex;
+  gap: 20px;
+
+  color: #343a40;
+  background-color: white;
+  border: 2px solid #495057;
+  border-radius: 4px;
+  padding: 2px 8px;
+}
+
+.hover-expand > div {
+  display: none;
+}
+
+.hover-expand:hover > div {
+  display: block;
+}
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+}
+
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+  display: block;
+}
+
+.bibtex {
+  font-family: "Inconsolata", sans-serif;
+  color: #343a40;
+  background-color: #e7f5ff;
+  border-radius: 10px;
+  padding: 16px;
+}
+
+.flat-button {
+  cursor: pointer;
+}
+
+.flat-button > label {
+  cursor: pointer;
+}
+
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+  display: none;
+}
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+@media (max-width: 900px) {
+  .desktop {
+    display: none;
+  }
+
+  .mobile {
+    display: block;
+    padding: 25px;
+  }
+
+  body {
+    background-color: #1971c2;
+  }
+}
+
+.mobile * {
+  color: white;
+}
+
+.mobile > .summary > a {
+  font-size: 24px;
+}
diff --git a/reports/datasets/1h/metadata.csv b/reports/old/datasets/1h/metadata.csv
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diff --git a/reports/old/readme.md b/reports/old/readme.md
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+# Reports for 2019 AutoML Benchmark
+
+These notebooks are built based on a previous iteration of the AutoML benchmark output, so will not work out-of-the-box with the new result files.
diff --git a/reports/reports.ipynb b/reports/old/reports.ipynb
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diff --git a/reports/readme.md b/reports/readme.md
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+# Notebooks for Visualizing AMLB Results
+
+This folder will contain the notebooks used to visualize result files produced by the AMLB tool, including those notebooks used for generating figures and tables in our paper.
+These notebooks will be added soon.