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Stefano-V.-Albrechtdeep-learning
2024
Stefano V. Albrecht, Filippos Christianos, Lukas Schäfer
Multi-Agent Reinforcement Learning: Foundations and Modern Approaches
MIT Press (print version scheduled for fall 2024), 2024
Abstract | BibTex | Book website | Book codebase
MITPmulti-agent-rldeep-rldeep-learningsurvey
Abstract:
Textbook published by MIT Press.
@book{ marl-book,
author = {Stefano V. Albrecht and Filippos Christianos and Lukas Sch\"afer},
title = {Multi-Agent Reinforcement Learning: Foundations and Modern Approaches},
publisher = {MIT Press},
year = {2024},
url = {https://www.marl-book.com}
}
Shangmin Guo, Yi Ren, Stefano V. Albrecht, Kenny Smith
lpNTK: Better Generalisation with Less Data via Sample Interaction During Learning
International Conference on Learning Representations, 2024
Abstract | BibTex | arXiv | Code
ICLRdeep-learning
Abstract:
Although much research has been done on proposing new models or loss functions to improve the generalisation of artificial neural networks (ANNs), less attention has been directed to the impact of the training data on generalisation. In this work, we start from approximating the interaction between samples, i.e. how learning one sample would modify the model's prediction on other samples. Through analysing the terms involved in weight updates in supervised learning, we find that labels influence the interaction between samples. Therefore, we propose the labelled pseudo Neural Tangent Kernel (lpNTK) which takes label information into consideration when measuring the interactions between samples. We first prove that lpNTK asymptotically converges to the empirical neural tangent kernel in terms of the Frobenius norm under certain assumptions. Secondly, we illustrate how lpNTK helps to understand learning phenomena identified in previous work, specifically the learning difficulty of samples and forgetting events during learning. Moreover, we also show that using lpNTK to identify and remove poisoning training samples does not hurt the generalisation performance of ANNs.
@inproceedings{guo2024lpntk,
title={Sample Relationship from Learning Dynamics Matters for Generalisation},
author={Shangmin Guo and Yi Ren and Stefano V. Albrecht and Kenny Smith},
booktitle={12th International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=8Ju0VmvMCW}
}
2022
Shangmin Guo, Yi Ren, Stefano V. Albrecht, Kenny Smith
Sample Relationships through the Lens of Learning Dynamics with Label Information
NeurIPS Workshop on Interpolation and Beyond, 2022
Abstract | BibTex | arXiv
NeurIPSiterated-learningdeep-learningtransfer-learning
Abstract:
Although much research has been done on proposing new models or loss functions to improve the generalisation of artificial neural networks (ANNs), less attention has been directed to the data, which is also an important factor for training ANNs. In this work, we start from approximating the interaction between two samples, i.e. how learning one sample would modify the model's prediction on the other sample. Through analysing the terms involved in weight updates in supervised learning, we find that the signs of labels influence the interactions between samples. Therefore, we propose the labelled pseudo Neural Tangent Kernel (lpNTK) which takes label information into consideration when measuring the interactions between samples. We first prove that lpNTK would asymptotically converge to the well-known empirical Neural Tangent Kernel in terms of the Frobenius norm under certain assumptions. Secondly, we illustrate how lpNTK helps to understand learning phenomena identified in previous work, specifically the learning difficulty of samples and forgetting events during learning. Moreover, we also show that lpNTK can help to improve the generalisation performance of ANNs in image classification tasks, compared with the original whole training sets.
@inproceedings{guo2022relationship,
title={Sample Relationships through the Lens of Learning Dynamics with Label Information},
author={Shangmin Guo and Yi Ren and Stefano V. Albrecht and Kenny Smith},
booktitle={NeurIPS 2022 Workshop on Interpolation and Beyond},
year={2022}
}