Statistically Valid Information Bottleneck via Multiple Hypothesis Testing
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909343955288064 |
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| author | Farzaneh, Amirmohammad Simeone, Osvaldo |
| author_facet | Farzaneh, Amirmohammad Simeone, Osvaldo |
| contents | The information bottleneck (IB) problem is a widely studied framework in machine learning for extracting compressed features that are informative for downstream tasks. However, current approaches to solving the IB problem rely on a heuristic tuning of hyperparameters, offering no guarantees that the learned features satisfy information-theoretic constraints. In this work, we introduce a statistically valid solution to this problem, referred to as IB via multiple hypothesis testing (IB-MHT), which ensures that the learned features meet the IB constraints with high probability, regardless of the size of the available dataset. The proposed methodology builds on Pareto testing and learn-then-test (LTT), and it wraps around existing IB solvers to provide statistical guarantees on the IB constraints. We demonstrate the performance of IB-MHT on classical and deterministic IB formulations, including experiments on distillation of language models. The results validate the effectiveness of IB-MHT in outperforming conventional methods in terms of statistical robustness and reliability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_07325 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Statistically Valid Information Bottleneck via Multiple Hypothesis Testing Farzaneh, Amirmohammad Simeone, Osvaldo Information Theory Machine Learning The information bottleneck (IB) problem is a widely studied framework in machine learning for extracting compressed features that are informative for downstream tasks. However, current approaches to solving the IB problem rely on a heuristic tuning of hyperparameters, offering no guarantees that the learned features satisfy information-theoretic constraints. In this work, we introduce a statistically valid solution to this problem, referred to as IB via multiple hypothesis testing (IB-MHT), which ensures that the learned features meet the IB constraints with high probability, regardless of the size of the available dataset. The proposed methodology builds on Pareto testing and learn-then-test (LTT), and it wraps around existing IB solvers to provide statistical guarantees on the IB constraints. We demonstrate the performance of IB-MHT on classical and deterministic IB formulations, including experiments on distillation of language models. The results validate the effectiveness of IB-MHT in outperforming conventional methods in terms of statistical robustness and reliability. |
| title | Statistically Valid Information Bottleneck via Multiple Hypothesis Testing |
| topic | Information Theory Machine Learning |
| url | https://arxiv.org/abs/2409.07325 |