*ECML/PKDD Workshop on neuro-symbolic metalearning and AutoML* 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 Confirmed invited speakers include: - Artur d’Avila Garcez <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.city.ac.uk%2Fabout%2Fpeople%2Facademics%2Fartur-davila-garcez&data=05%7C01%7Cuai%40engr.orst.edu%7C7676901ecab3497bb60608db6c6a6980%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C638222976509615172%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=SBmPyyPROYu1g%2F08CqFSdtYBi%2BWJkB5I%2FGbmQX1ARNk%3D&reserved=0>, City University of London, UK - Bernhard Pfahringer <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fprofiles.waikato.ac.nz%2Fbernhard.pfahringer&data=05%7C01%7Cuai%40engr.orst.edu%7C7676901ecab3497bb60608db6c6a6980%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C638222976509615172%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=O1MFT7gJzTmn7mMSF097Jcv8%2BzHg%2BZhjlfltqI7K43A%3D&reserved=0>, University of Waikato, New Zealand Deadline: 26 June Website: https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fjanvanrijn.github.io%2Fmetalearning%2F2023ECMLPKDDworkshop&data=05%7C01%7Cuai%40engr.orst.edu%7C7676901ecab3497bb60608db6c6a6980%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C638222976509615172%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=h2kcmASy2FjP3NEi%2FVB7PD2qcX1ke6DW%2FKF4PbHoCS8%3D&reserved=0 Best, Workshop Chairs
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