Fast and Efficient Local-Search for Genetic Programming Based Loss Function Learning
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{raymond:2023:GECCO,
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author = "Christian Raymond and Qi Chen and Bing Xue and
Mengjie Zhang",
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title = "Fast and Efficient {Local-Search} for Genetic
Programming Based Loss Function Learning",
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booktitle = "Proceedings of the 2023 Genetic and Evolutionary
Computation Conference",
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year = "2023",
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editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and
Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and
Arnaud Liefooghe and Bing Xue and Ying Bi and
Nelishia Pillay and Irene Moser and Arthur Guijt and
Jessica Catarino and Pablo Garcia-Sanchez and
Leonardo Trujillo and Carla Silva and Nadarajen Veerapen",
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pages = "1184--1193",
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address = "Lisbon, Portugal",
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series = "GECCO '23",
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month = "15-19 " # jul,
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organisation = "SIGEVO",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, loss function
learning, meta-learning",
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isbn13 = "9798400701191",
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DOI = "doi:10.1145/3583131.3590361",
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size = "10 pages",
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abstract = "In this paper, we develop upon the topic of loss
function learning, an emergent meta-learning paradigm
that aims to learn loss functions that significantly
improve the performance of the models trained under
them. Specifically, we propose a new meta-learning
framework for task and model-agnostic loss function
learning via a hybrid search approach. The framework
first uses genetic programming to find a set of
symbolic loss functions. Second, the set of learned
loss functions is subsequently parameterized and
optimized via unrolled differentiation. The versatility
and performance of the proposed framework are
empirically validated on a diverse set of supervised
learning tasks. Results show that the learned loss
functions bring improved convergence, sample
efficiency, and inference performance on tabulated,
computer vision, and natural language processing
problems, using a variety of task-specific neural
network architectures.",
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notes = "GECCO-2023 A Recombination of the 32nd International
Conference on Genetic Algorithms (ICGA) and the 28th
Annual Genetic Programming Conference (GP)",
- }
Genetic Programming entries for
Christian Raymond
Qi Chen
Bing Xue
Mengjie Zhang
Citations