Robust Mean Estimation under Quantization
Fuente:
arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
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| _version_ | 1866918283216683008 |
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| author | Abdalla, Pedro Chen, Junren |
| author_facet | Abdalla, Pedro Chen, Junren |
| contents | We consider the problem of mean estimation under quantization and adversarial corruption. We construct multivariate robust estimators that are optimal up to logarithmic factors in two different settings. The first is a one-bit setting, where each bit depends only on a single sample, and the second is a partial quantization setting, in which the estimator may use a small fraction of unquantized data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_07074 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Robust Mean Estimation under Quantization Abdalla, Pedro Chen, Junren Machine Learning Statistics Theory We consider the problem of mean estimation under quantization and adversarial corruption. We construct multivariate robust estimators that are optimal up to logarithmic factors in two different settings. The first is a one-bit setting, where each bit depends only on a single sample, and the second is a partial quantization setting, in which the estimator may use a small fraction of unquantized data. |
| title | Robust Mean Estimation under Quantization |
| topic | Machine Learning Statistics Theory |
| url | https://arxiv.org/abs/2601.07074 |