Advances in Metalearning: ECML/PKDD Workshop on Meta-Knowledge Transfer

Pavel Brazdil, Jan N. van Rijn, Henry Gouk, Felix Mohr
ECMLPKDD Workshop on Meta-Knowledge Transfer, PMLR 191:1-7, 2022.

Abstract

Meta-knowledge plays an important role in current machine learning and AutoML systems. One way of acquiring meta-knowledge is by observing learning processes (on the same task, or on different tasks) and representing it in such a way that it can be used later to improve future learning processes. Metalearning systems, on the other hand, normally explore metaknowledge acquired on different problems. The systems may, in addition, use metaknowledge concerning which part of the space should be examined first (i.e., a warm start or dynamic scheduling). Various contributions of this workshop addressed various aspects of metaknowledge, and in particular, how it is exploited in different systems. This workshop included two invited talks, one by Hospedales on “Meta-learning for Knowledge Transfer” and another by Hitzler on “Some advances regarding ontologies and neuro-symbolic artificial intelligence”.

Cite this Paper

BibTeX
@InProceedings{pmlr-v191-brazdil22a, title = {Advances in Metalearning: ECML/PKDD Workshop on Meta-Knowledge Transfer}, author = {Brazdil, Pavel and van Rijn, Jan N. and Gouk, Henry and Mohr, Felix}, booktitle = {ECMLPKDD Workshop on Meta-Knowledge Transfer}, pages = {1--7}, year = {2022}, editor = {Brazdil, Pavel and van Rijn, Jan N. and Gouk, Henry and Mohr, Felix}, volume = {191}, series = {Proceedings of Machine Learning Research}, month = {23 Sep}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v191/brazdil22a/brazdil22a.pdf}, url = {https://proceedings.mlr.press/v191/brazdil22a.html}, abstract = {Meta-knowledge plays an important role in current machine learning and AutoML systems. One way of acquiring meta-knowledge is by observing learning processes (on the same task, or on different tasks) and representing it in such a way that it can be used later to improve future learning processes. Metalearning systems, on the other hand, normally explore metaknowledge acquired on different problems. The systems may, in addition, use metaknowledge concerning which part of the space should be examined first (i.e., a warm start or dynamic scheduling). Various contributions of this workshop addressed various aspects of metaknowledge, and in particular, how it is exploited in different systems. This workshop included two invited talks, one by Hospedales on “Meta-learning for Knowledge Transfer” and another by Hitzler on “Some advances regarding ontologies and neuro-symbolic artificial intelligence”. } }
Endnote
%0 Conference Paper %T Advances in Metalearning: ECML/PKDD Workshop on Meta-Knowledge Transfer %A Pavel Brazdil %A Jan N. van Rijn %A Henry Gouk %A Felix Mohr %B ECMLPKDD Workshop on Meta-Knowledge Transfer %C Proceedings of Machine Learning Research %D 2022 %E Pavel Brazdil %E Jan N. van Rijn %E Henry Gouk %E Felix Mohr %F pmlr-v191-brazdil22a %I PMLR %P 1--7 %U https://proceedings.mlr.press/v191/brazdil22a.html %V 191 %X Meta-knowledge plays an important role in current machine learning and AutoML systems. One way of acquiring meta-knowledge is by observing learning processes (on the same task, or on different tasks) and representing it in such a way that it can be used later to improve future learning processes. Metalearning systems, on the other hand, normally explore metaknowledge acquired on different problems. The systems may, in addition, use metaknowledge concerning which part of the space should be examined first (i.e., a warm start or dynamic scheduling). Various contributions of this workshop addressed various aspects of metaknowledge, and in particular, how it is exploited in different systems. This workshop included two invited talks, one by Hospedales on “Meta-learning for Knowledge Transfer” and another by Hitzler on “Some advances regarding ontologies and neuro-symbolic artificial intelligence”.
APA
Brazdil, P., van Rijn, J.N., Gouk, H. & Mohr, F.. (2022). Advances in Metalearning: ECML/PKDD Workshop on Meta-Knowledge Transfer. ECMLPKDD Workshop on Meta-Knowledge Transfer, in Proceedings of Machine Learning Research 191:1-7 Available from https://proceedings.mlr.press/v191/brazdil22a.html.

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