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CFP Neuro-symbolic Metalearning and AutoML Workshop co-hosted at ECML/PKDD 2023
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mdkamruzz...@gmail.com
May 18, 2023, 7:47:34 AM5/18/23
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CFP Neuro-symbolic Metalearning and AutoML
Workshop co-hosted at ECML/PKDD 2023.
Details: https://janvanrijn.github.io/metalearning/2023ECMLPKDDworkshop
******************************************
Date: September 18, 2023 (afternoon)
Location: Pending the ECML/PKDD room allocation
Invited Speakers
* Artur d’Avila Garcez, City University of London, UK
Organization
General organizers / Program Chairs (ordered by last name)
Pavel Brazdil, University of Porto, Portugal
Henry Gouk, University of Edinburgh, Scotland
Jan N. van Rijn, Leiden University, The Netherlands
Md Kamruzzaman Sarker, University of Hartford, USA
Overview
This workshop explores different types of meta-knowledge, such as performance summary statistics or pre-trained model weights. One way of acquiring meta-knowledge is by observing learning processes and representing it in such a way that it can be used later to improve future learning processes. AutoML systems typically explore meta-knowledge acquired from a single task, e.g., by modelling the relationship between hyperparameters and model performance. Metalearning systems, on the other hand, normally explore metaknowledge acquired on a collection of machine learning tasks. This can be used not only for selection of the best workflow(s) for the current task, but also for adaptation and fine-tuning of a prior model to the new task. Many current AutoML and metalearning systems exploit both types of meta-knowledge. Neuro-symbolic systems explore the interplay between neural network-based learning and symbol-based learning to get the best of those two types of learning. While doing so, it tries to use the existing knowledge as a concrete symbolic representation or as a transformed version of the symbolic representation suited for the learning algorithm. The goal of this workshop is to explore ways in which ideas can be cross-pollinated between the AutoML/Metalearning and neuro-symbolic learning research communities. This could lead to, e.g., systems with interpretable meta-knowledge, and tighter integration between machine learning workflows and automated reasoning systems.
Main research areas:
Controlling the learning processes
Definitions of configuration spaces
Few-shot learning
Elaboration of feature hierarchies
Exploiting hierarchy of features in learning
Meta-learning
Conditional meta-learning
Meta-knowledge transfer
Transfer learning
Transfer of prior models
Transfer of meta-knowledge between systems
Symbolic vs subsymbolic meta-knowledge
Neuro-symbolic learning
Explainable and interpretable meta-learning
Explainable artificial intelligence
Important Dates
Workshop Paper Submission Deadline: 12 June 2023
Workshop Paper Author Notification: 17 July 2023
Camera Ready Deadline: End of July 2023
Workshop: September 18, 2023 (afternoon)
Submission
This workshop hosts two tracks:
* Original paper track: Authors can submit novel papers, that have not been accepted elsewhere, maximum 12 pages.
* Poster of already published work: Authors can apply for a poster spot for a paper that has recently (less than 2 years) been published elsewhere. During submission, you send a link to the already published version of the work, and the peer-review will determine whether it is a good match based on the topic.
Submissions go through the Conference Management Tool.
Format of the Workshop
The workshop will last a half a day. It will include:
* Invited talks
* Short oral presentations
* Poster session
* Panel discussions on “Neuro-symbolic Metalearning and AutoML”
Proceedings
Accepted papers can decide to opt-in to the formal workshop proceedings of ECML/PKDD 2023. The authors of accepted papers can decide whether they wish to have their full paper included or not. In the latter case, publication of a short abstract would be possible.
Regards,
~ Zaman/Sarker
Md Kamruzzaman Sarker
Assistant Professor
Department of Computing Sciences, University of Hartford
https://mdkzaman.com/
https://www.hartford.edu/directory/arts-science/sarker-md-kamruzzaman.aspx
CFP Neuro-symbolic Metalearning and AutoML
Workshop co-hosted at ECML/PKDD 2023.
Details: https://janvanrijn.github.io/metalearning/2023ECMLPKDDworkshop
******************************************
Date: September 18, 2023 (afternoon)
Location: Pending the ECML/PKDD room allocation
Invited Speakers
* Artur d’Avila Garcez, City University of London, UK
Organization
General organizers / Program Chairs (ordered by last name)
Pavel Brazdil, University of Porto, Portugal
Henry Gouk, University of Edinburgh, Scotland
Jan N. van Rijn, Leiden University, The Netherlands
Md Kamruzzaman Sarker, University of Hartford, USA
Overview
This workshop explores different types of meta-knowledge, such as performance summary statistics or pre-trained model weights. One way of acquiring meta-knowledge is by observing learning processes and representing it in such a way that it can be used later to improve future learning processes. AutoML systems typically explore meta-knowledge acquired from a single task, e.g., by modelling the relationship between hyperparameters and model performance. Metalearning systems, on the other hand, normally explore metaknowledge acquired on a collection of machine learning tasks. This can be used not only for selection of the best workflow(s) for the current task, but also for adaptation and fine-tuning of a prior model to the new task. Many current AutoML and metalearning systems exploit both types of meta-knowledge. Neuro-symbolic systems explore the interplay between neural network-based learning and symbol-based learning to get the best of those two types of learning. While doing so, it tries to use the existing knowledge as a concrete symbolic representation or as a transformed version of the symbolic representation suited for the learning algorithm. The goal of this workshop is to explore ways in which ideas can be cross-pollinated between the AutoML/Metalearning and neuro-symbolic learning research communities. This could lead to, e.g., systems with interpretable meta-knowledge, and tighter integration between machine learning workflows and automated reasoning systems.
Main research areas:
Controlling the learning processes
Definitions of configuration spaces
Few-shot learning
Elaboration of feature hierarchies
Exploiting hierarchy of features in learning
Meta-learning
Conditional meta-learning
Meta-knowledge transfer
Transfer learning
Transfer of prior models
Transfer of meta-knowledge between systems
Symbolic vs subsymbolic meta-knowledge
Neuro-symbolic learning
Explainable and interpretable meta-learning
Explainable artificial intelligence
Important Dates
Workshop Paper Submission Deadline: 12 June 2023
Workshop Paper Author Notification: 17 July 2023
Camera Ready Deadline: End of July 2023
Workshop: September 18, 2023 (afternoon)
Submission
This workshop hosts two tracks:
* Original paper track: Authors can submit novel papers, that have not been accepted elsewhere, maximum 12 pages.
* Poster of already published work: Authors can apply for a poster spot for a paper that has recently (less than 2 years) been published elsewhere. During submission, you send a link to the already published version of the work, and the peer-review will determine whether it is a good match based on the topic.
Submissions go through the Conference Management Tool.
Format of the Workshop
The workshop will last a half a day. It will include:
* Invited talks
* Short oral presentations
* Poster session
* Panel discussions on “Neuro-symbolic Metalearning and AutoML”
Proceedings
Accepted papers can decide to opt-in to the formal workshop proceedings of ECML/PKDD 2023. The authors of accepted papers can decide whether they wish to have their full paper included or not. In the latter case, publication of a short abstract would be possible.
Regards,
~ Zaman/Sarker
Md Kamruzzaman Sarker
Assistant Professor
Department of Computing Sciences, University of Hartford
https://mdkzaman.com/
https://www.hartford.edu/directory/arts-science/sarker-md-kamruzzaman.aspx
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