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 $n^*_{\mathrm{Hub}}(\varepsilon)$, $n^*_{\mathrm{Sub}}(\varepsilon)$, and $n^*_{\mathrm{TV}}(\varepsilon)$, respectively. For subtractive contamination, we show that least favourable distributions exist and provide explicit formulas for the same, bringing this model in line with the classical Huber and TV models. Next we show th
This research builds on fundamental statistical robust hypothesis testing, a critical area for improving the reliability and safety of AI/ML systems, with ongoing advancements in theoretical foundations.
Improving robust binary hypothesis testing is crucial for the development of more reliable and trustworthy AI systems, particularly in critical applications where data contamination is a concern.
This theoretical work provides a deeper understanding of sample complexity under different contamination models, offering foundational insights for designing more robust algorithms.
- · AI/ML researchers
- · Developers of safety-critical AI
- · Sectors reliant on AI reliability
- · Systems vulnerable to data contamination
Improved theoretical understanding of robust statistical decision-making under uncertainty.
Potential for more resilient AI models capable of handling imperfect real-world data effectively.
Increased trust and adoption of AI in sensitive applications due to enhanced reliability and verifiable robustness.
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