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...tography, are best solved when viewed as a claw finding problem, hence any algorithmic improvement to solving the claw finding problem provides a better attack on ...um computer]]s are used, Seiichiro Tani showed that a claw can be found in complexity ...

...eration|iterated]]) multiple times inside the algorithm. Splitting a large algorithmic function into rounds simplifies both implementation and [[cryptanalysis]].{ ...of rounds does not ''always'' make weak ciphers into strong ones, as some attacks do not depend on the number of rounds.{{sfn|Biryukov|Wagner|1999}} ...

...ations|regular expression implementations]] have super-linear [[worst-case complexity]]; on certain regex-input pairs, the time taken can grow polynomially or ex |title=Regular Expression Denial of Service Attacks and Defenses ...

...ation, Problems Inform. Transmission, 1:1(1965), 1–7]</ref> The Kolmogorov complexity of a ''single'' finite object is the information in that object; the infor where <math>K(\cdot \mid \cdot)</math> is the [[Kolmogorov complexity]] defined by <ref name="Ko65"/> of the prefix type.<ref>[http://www.mathnet ...

...th> \mathbb F_p </math>, '''Naor and Reingold''' show that for every [[PP (complexity)|probabilistic polynomial time]] algorithm <math> \mathcal{A} </math> and ==Linear complexity== ...

...y''' is a subfield of [[computational complexity theory]] that studies the complexity of computational problems on "most inputs". Generic-case complexity is a way of measuring the complexity of a [[computational problem]] by neglecting a small set of ...

...Systems and Computing|volume=1037|pages=111–125|language=en|chapter=Timing Attacks on Machine Learning: State of the Art|doi=10.1007/978-3-030-29516-5_10|s2ci Most common attacks in adversarial machine learning include [[evasion attack]]s,<ref name="Good ...

...sampling (statistics)|sample]], the resulting model may exhibit unwanted [[algorithmic bias|biases]]. ...AI safety|safe]] text that is both helpful and harmless (such as lacking [[algorithmic bias|bias]], toxicity, or otherwise harmful content). Asking humans to manu ...

== Algorithmic hyper-parameters == ...e model with a regularized version of the gradient. Hence, the computation complexity is linear in local dataset size. Moreover, gradient computation can be para ...

...AIC is the strength of preference for parsimony. BIC's penalty for model complexity is <math>\ln(n) k</math> , while AIC's is <math>2k</math>. Large-sample asy ...l |last3=Schapire |first3=Robert E. |year=1994 |title=Bounds on the sample complexity of Bayesian learning using information theory and the VC dimension |journal ...