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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.17932 |
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| _version_ | 1866909533236887552 |
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| author | Bazinet, Mathieu Zantedeschi, Valentina Germain, Pascal |
| author_facet | Bazinet, Mathieu Zantedeschi, Valentina Germain, Pascal |
| contents | The sample compression theory provides generalization guarantees for predictors that can be fully defined using a subset of the training dataset and a (short) message string, generally defined as a binary sequence. Previous works provided generalization bounds for the zero-one loss, which is restrictive notably when applied to deep learning approaches. In this paper, we present a general framework for deriving new sample compression bounds that hold for real-valued unbounded losses. Using the Pick-To-Learn (P2L) meta-algorithm, which transforms the training method of any machine-learning predictor to yield sample-compressed predictors, we empirically demonstrate the tightness of the bounds and their versatility by evaluating them on random forests and multiple types of neural networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_17932 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Sample Compression Unleashed: New Generalization Bounds for Real Valued Losses Bazinet, Mathieu Zantedeschi, Valentina Germain, Pascal Machine Learning The sample compression theory provides generalization guarantees for predictors that can be fully defined using a subset of the training dataset and a (short) message string, generally defined as a binary sequence. Previous works provided generalization bounds for the zero-one loss, which is restrictive notably when applied to deep learning approaches. In this paper, we present a general framework for deriving new sample compression bounds that hold for real-valued unbounded losses. Using the Pick-To-Learn (P2L) meta-algorithm, which transforms the training method of any machine-learning predictor to yield sample-compressed predictors, we empirically demonstrate the tightness of the bounds and their versatility by evaluating them on random forests and multiple types of neural networks. |
| title | Sample Compression Unleashed: New Generalization Bounds for Real Valued Losses |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2409.17932 |