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Electronic Colloquium on Computational Complexity

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All reports by Author Mikito Nanashima:

TR21-161 | 16th November 2021
Shuichi Hirahara, Mikito Nanashima

On Worst-Case Learning in Relativized Heuristica

A PAC learning model involves two worst-case requirements: a learner must learn all functions in a class on all example distributions. However, basing the hardness of learning on NP-hardness has remained a key challenge for decades. In fact, recent progress in computational complexity suggests the possibility that a weaker assumption ... more >>>

TR20-095 | 24th June 2020
Mikito Nanashima

On Basing Auxiliary-Input Cryptography on NP-hardness via Nonadaptive Black-Box Reductions

Revisions: 1

A black-box (BB) reduction is a central proof technique in theoretical computer science. However, the limitations on BB reductions have been revealed for several decades, and the series of previous work gives strong evidence that we should avoid a nonadaptive BB reduction to base cryptography on NP-hardness (e.g., Akavia et ... more >>>

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