Researchers have developed a unified pseudoentropy characterization that strengthens the relationship between computational hardness and randomness. This new formulation applies to both uniform and nonuniform computational models and encompasses various entropy notions, including Shannon and min-entropy. A key technical advancement involves using weight-restricted calibration and computational indistinguishability, which leads to an exponential improvement in alphabet size dependence compared to previous methods. AI
IMPACT Establishes theoretical foundations for understanding computational randomness and hardness, potentially impacting future algorithm design.
RANK_REASON This is a research paper published on arXiv detailing theoretical computer science concepts. [lever_c_demoted from research: ic=1 ai=0.4]
- arXiv
- Casacuberta
- Dwork
- information entropy
- Jetchev
- Lunjia Hu
- min entropy
- Pietrzak
- Trevisan
- Tulsiani
- Vadhana Isarabhakdi
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