From 140938ff4d80f96f6f1d75f9f4f4411ac878d6f2 Mon Sep 17 00:00:00 2001 From: jtimko16 Date: Wed, 28 Feb 2024 14:08:26 +0200 Subject: [PATCH 1/5] Mod - first simple example --- index.html | 96 ++++++++++++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 93 insertions(+), 3 deletions(-) diff --git a/index.html b/index.html index 95ab56a..1199fda 100644 --- a/index.html +++ b/index.html @@ -1,11 +1,101 @@ + - Hello, World! + Workshop on Automated Machine Learning (AutoML) + -

Hello, World!

-

AutoML workshop 2024 site.

+ +
+

Workshop on Automated Machine Learning (AutoML)

+
+ +
+
+

Welcome to the Workshop!

+

+ Lorem ipsum dolor sit amet, consectetur adipiscing elit. Proin feugiat, libero vel + consectetur euismod, quam velit ultricies felis, eget dapibus velit odio vel ipsum. +

+

+ In this workshop, we will explore the exciting world of Automated Machine Learning (AutoML) + and learn how it can simplify and streamline the machine learning model development process. +

+
+ + +
+ \ No newline at end of file From 07deb2c9e99effc264e45c93c7b21b7e95a81574 Mon Sep 17 00:00:00 2001 From: jtimko16 Date: Wed, 28 Feb 2024 14:16:14 +0200 Subject: [PATCH 2/5] Add - topics, format, attendance --- index.html | 136 ++++++++++++++++++++++++++++++----------------------- 1 file changed, 76 insertions(+), 60 deletions(-) diff --git a/index.html b/index.html index 1199fda..6d5bced 100644 --- a/index.html +++ b/index.html @@ -5,65 +5,18 @@ Workshop on Automated Machine Learning (AutoML) @@ -75,15 +28,65 @@

Workshop on Automated Machine Learning (AutoML)

-

Welcome to the Workshop!

+ + + +
+

Workshop Description:

+

+ As the demand for machine learning applications surges, it becomes evident that the available pool of knowledgeable data scientists cannot + scale proportionally with the increasing data volumes and diverse application requirements in our digital world. To address this challenge, + various automated machine learning (AutoML) frameworks have emerged, aiming to bridge the gap in human expertise by automating the construction + of machine learning pipelines. AutoML research aims to automate the machine learning process progressively, with the objective of making effective + methods accessible to everyone. Therefore, the workshop is designed for a diverse audience, including core machine learning researchers involved + in various ML domains related to AutoML, such as neural architecture search, hyperparameter optimization, meta-learning, and explainability + within the AutoML context. It also caters to domain experts seeking to apply machine learning to novel problem domains. + + +

+ +

+ We invite submissions on the topics of: +
    +
  • Model selection, hyper-parameter optimization, and model search
    • +
  • Neural architecture search
    • +
  • Internet of things (IoT) and automation
    • +
  • Automation bias and misuse
    • +
  • Meta-learning and transfer learning
    • +
  • Bayesian optimization for AutoML
    • +
  • Evolutionary algorithms for AutoML
    • +
  • Multi-fidelity optimization
    • +
  • Predictive models of performance
    • +
  • Automatic feature extraction / construction
    • +
  • Automatic data cleaning
    • +
  • Automatic feature engineering
    • +
  • Automation of semi-supervised and unsupervised machine learning
    • +
  • Robustness of AutoML systems
    • +
  • Human-in-the-loop approaches for AutoML
    • +
  • Trust in AutoML
    • +
  • Learning to learn new algorithms and strategies
    • +
  • Hyperparameter agnostic algorithms
    • +
  • AutoML for neuro-fuzzy systems
    • + + +

    Format:

    - Lorem ipsum dolor sit amet, consectetur adipiscing elit. Proin feugiat, libero vel - consectetur euismod, quam velit ultricies felis, eget dapibus velit odio vel ipsum. + The workshop will follow the classical format of presentations of peer-reviewed papers followed by + discussion. The typical duration for the workshop is a full day. We will arrange invited talks + from experts in Automated Machine Learning (AutoML) to enrich the workshop experience and provide + valuable insights from leaders in the field.

    + + +

    Attendance:

    - In this workshop, we will explore the exciting world of Automated Machine Learning (AutoML) - and learn how it can simplify and streamline the machine learning model development process. + The workshop is timely and relevant for the data management and machine learning research + communities due to the rapid growth in machine learning applications in almost every application + domain. The workshop is intended also to benefit designers and machine learning users in the broad + area of machine learning. We anticipate receiving approximately 35 submissions.

    + +
From 73240aadcceccf8df6c11cdce913e7b0c1bbdb7d Mon Sep 17 00:00:00 2001 From: jtimko16 Date: Wed, 28 Feb 2024 14:24:20 +0200 Subject: [PATCH 3/5] Add - create main.html as a separate file --- main.html | 57 +++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 57 insertions(+) create mode 100644 main.html diff --git a/main.html b/main.html new file mode 100644 index 0000000..9c5b98b --- /dev/null +++ b/main.html @@ -0,0 +1,57 @@ + +
+

Workshop Description:

+

+ As the demand for machine learning applications surges, it becomes evident that the available pool of knowledgeable data scientists cannot + scale proportionally with the increasing data volumes and diverse application requirements in our digital world. To address this challenge, + various automated machine learning (AutoML) frameworks have emerged, aiming to bridge the gap in human expertise by automating the construction + of machine learning pipelines. AutoML research aims to automate the machine learning process progressively, with the objective of making effective + methods accessible to everyone. Therefore, the workshop is designed for a diverse audience, including core machine learning researchers involved + in various ML domains related to AutoML, such as neural architecture search, hyperparameter optimization, meta-learning, and explainability + within the AutoML context. It also caters to domain experts seeking to apply machine learning to novel problem domains. + + +

+ +

+We invite submissions on the topics of: +
    +
  • Model selection, hyper-parameter optimization, and model search
    • +
  • Neural architecture search
    • +
  • Internet of things (IoT) and automation
    • +
  • Automation bias and misuse
    • +
  • Meta-learning and transfer learning
    • +
  • Bayesian optimization for AutoML
    • +
  • Evolutionary algorithms for AutoML
    • +
  • Multi-fidelity optimization
    • +
  • Predictive models of performance
    • +
  • Automatic feature extraction / construction
    • +
  • Automatic data cleaning
    • +
  • Automatic feature engineering
    • +
  • Automation of semi-supervised and unsupervised machine learning
    • +
  • Robustness of AutoML systems
    • +
  • Human-in-the-loop approaches for AutoML
    • +
  • Trust in AutoML
    • +
  • Learning to learn new algorithms and strategies
    • +
  • Hyperparameter agnostic algorithms
    • +
  • AutoML for neuro-fuzzy systems
    • + + +

    Format:

    +

    + The workshop will follow the classical format of presentations of peer-reviewed papers followed by + discussion. The typical duration for the workshop is a full day. We will arrange invited talks + from experts in Automated Machine Learning (AutoML) to enrich the workshop experience and provide + valuable insights from leaders in the field. +

    + + +

    Attendance:

    +

    + The workshop is timely and relevant for the data management and machine learning research + communities due to the rapid growth in machine learning applications in almost every application + domain. The workshop is intended also to benefit designers and machine learning users in the broad + area of machine learning. We anticipate receiving approximately 35 submissions. +

    + +
\ No newline at end of file From 2454a41aab7211c6744d4fc206a885c6739ff529 Mon Sep 17 00:00:00 2001 From: jtimko16 Date: Wed, 28 Feb 2024 14:31:05 +0200 Subject: [PATCH 4/5] Fix - fixed the main file and remove other --- index.html | 27 +++++++++++++------------- main.html | 57 ------------------------------------------------------ 2 files changed, 14 insertions(+), 70 deletions(-) delete mode 100644 main.html diff --git a/index.html b/index.html index 6d5bced..287422d 100644 --- a/index.html +++ b/index.html @@ -85,7 +85,19 @@

Attendance:

domain. The workshop is intended also to benefit designers and machine learning users in the broad area of machine learning. We anticipate receiving approximately 35 submissions.

- + + + +
+

List of Potential Workshop PC Members:

+
    +
  • Amin Beheshti, Professor, School of Computing, Macquarie University, Sydney, Australia
    • +
  • Riccardo Tommasini, Associate Professor at the Institute National des Sciences Appliquées (INSA)
    • +
  • Dmitri Rozgonjuk, Robert Bosch GmbH
    • +
  • David Camacho, Departamento de Sistemas Informáticos, Technical University of Madrid, Madrid, Spain
    • +
  • Francesco Piccialli, Department of Mathematics and Applications, University of Naples, Italy
    • + +
@@ -98,18 +110,7 @@

Menu

- -
-

List of Potential Workshop PC Members:

-
    -
  • Amin Beheshti, Professor, School of Computing, Macquarie University, Sydney, Australia
    • -
  • Riccardo Tommasini, Associate Professor at the Institute National des Sciences Appliquées (INSA)
    • -
  • Dmitri Rozgonjuk, Robert Bosch GmbH
    • -
  • David Camacho, Departamento de Sistemas Informáticos, Technical University of Madrid, Madrid, Spain
    • -
  • Francesco Piccialli, Department of Mathematics and Applications, University of Naples, Italy
    • - -
-
+ diff --git a/main.html b/main.html deleted file mode 100644 index 9c5b98b..0000000 --- a/main.html +++ /dev/null @@ -1,57 +0,0 @@ - -
-

Workshop Description:

-

- As the demand for machine learning applications surges, it becomes evident that the available pool of knowledgeable data scientists cannot - scale proportionally with the increasing data volumes and diverse application requirements in our digital world. To address this challenge, - various automated machine learning (AutoML) frameworks have emerged, aiming to bridge the gap in human expertise by automating the construction - of machine learning pipelines. AutoML research aims to automate the machine learning process progressively, with the objective of making effective - methods accessible to everyone. Therefore, the workshop is designed for a diverse audience, including core machine learning researchers involved - in various ML domains related to AutoML, such as neural architecture search, hyperparameter optimization, meta-learning, and explainability - within the AutoML context. It also caters to domain experts seeking to apply machine learning to novel problem domains. - - -

- -

-We invite submissions on the topics of: -
    -
  • Model selection, hyper-parameter optimization, and model search
    • -
  • Neural architecture search
    • -
  • Internet of things (IoT) and automation
    • -
  • Automation bias and misuse
    • -
  • Meta-learning and transfer learning
    • -
  • Bayesian optimization for AutoML
    • -
  • Evolutionary algorithms for AutoML
    • -
  • Multi-fidelity optimization
    • -
  • Predictive models of performance
    • -
  • Automatic feature extraction / construction
    • -
  • Automatic data cleaning
    • -
  • Automatic feature engineering
    • -
  • Automation of semi-supervised and unsupervised machine learning
    • -
  • Robustness of AutoML systems
    • -
  • Human-in-the-loop approaches for AutoML
    • -
  • Trust in AutoML
    • -
  • Learning to learn new algorithms and strategies
    • -
  • Hyperparameter agnostic algorithms
    • -
  • AutoML for neuro-fuzzy systems
    • - - -

    Format:

    -

    - The workshop will follow the classical format of presentations of peer-reviewed papers followed by - discussion. The typical duration for the workshop is a full day. We will arrange invited talks - from experts in Automated Machine Learning (AutoML) to enrich the workshop experience and provide - valuable insights from leaders in the field. -

    - - -

    Attendance:

    -

    - The workshop is timely and relevant for the data management and machine learning research - communities due to the rapid growth in machine learning applications in almost every application - domain. The workshop is intended also to benefit designers and machine learning users in the broad - area of machine learning. We anticipate receiving approximately 35 submissions. -

    - -
\ No newline at end of file From d0083ccbbcdcedd596a09f9c9164307300f11c81 Mon Sep 17 00:00:00 2001 From: jtimko16 Date: Wed, 28 Feb 2024 14:39:37 +0200 Subject: [PATCH 5/5] First very simple page --- index.html | 63 ++++++++++++++++++++++++++++++++++++++++-------------- 1 file changed, 47 insertions(+), 16 deletions(-) diff --git a/index.html b/index.html index 287422d..abbae10 100644 --- a/index.html +++ b/index.html @@ -3,13 +3,53 @@ - Workshop on Automated Machine Learning (AutoML) + Workshop on Automated Machine Learning (AutoML) - 2024