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New Research Analyzes Sample Complexity in Robust Hypothesis Testing

By PulseAugur Editorial · [2 sources] ·

A new research paper explores the sample complexity of robust binary hypothesis testing across three contamination models: Huber, subtractive, and total variation. The study provides explicit formulas for subtractive contamination and demonstrates that sample complexity can be highly unstable with respect to the contamination parameter $\varepsilon$. The paper also shows that sample complexities across the different models are comparable when $\varepsilon$ is rescaled by constant factors. AI

IMPACT This research contributes to the theoretical understanding of hypothesis testing, which can underpin future advancements in AI model evaluation and robustness.

RANK_REASON The cluster contains a research paper published on arXiv detailing theoretical findings in statistics.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New Research Analyzes Sample Complexity in Robust Hypothesis Testing

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Shankar Vallinayagam, Ankit Pensia, Varun Jog ·

    On the Sample Complexity of Robust Binary Hypothesis Testing

    arXiv:2605.24741v1 Announce Type: cross Abstract: We study the sample complexity of robust binary hypothesis testing under three standard contamination models: $\varepsilon$-additive (Huber), $\varepsilon$-subtractive, and $\varepsilon$-total variation (TV), denoted by $n^*_{\mat…

  2. arXiv stat.ML TIER_1 English(EN) · Varun Jog ·

    On the Sample Complexity of Robust Binary Hypothesis Testing

    We study the sample complexity of robust binary hypothesis testing under three standard contamination models: $\varepsilon$-additive (Huber), $\varepsilon$-subtractive, and $\varepsilon$-total variation (TV), denoted by $n^*_{\mathrm{Hub}}(\varepsilon)$, $n^*_{\mathrm{Sub}}(\vare…

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