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Electronic Colloquium on Computational Complexity

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TR26-308 | 5th November 2026
Marshall Ball, Eshan Chattopadhyay, Mohit Gurumukhani, Ronen Shaltiel, Yunya Zhao

Multiplicative and Additive Extractors for Low Entropy Samplable Sources Under Reduced Assumptions

Trevisan and Vadhan [TV, FOCS'00] constructed (seedless) extractors for distributions samplable by poly-size circuits that have very high min-entropy, under the very strong complexity theoretic hardness assumption that $\text{E}=\text{DTIME}(2^{O(n)})$ requires size $2^{\Omega(n)}$ circuits that have oracle access to $\Sigma_6^{\text{P}}$. In this paper we give three new explicit constructions of extractors ... more >>>


TR26-307 | 4th November 2026
Roberto Bruno, Ugo Vaccaro

The Information Bottleneck and the Privacy Funnel are NP-hard

Given a joint distribution $P_{XY}$ of random variables $X,Y$ on finite alphabets, the information
bottleneck method seeks a representation $T$ of $X$ that is as informative as possible about $Y$ while compressing $X$; the privacy funnel asks for a representation that discloses much about $X$ while leaking little about $Y$. ... more >>>


TR26-306 | 4th November 2026
Gabriel Hart, Kaave Hosseini

Exponential Separation of Sign-Rank and Margin Complexity

We construct an explicit family of total sign matrices whose sign-rank is exponentially larger than their margin complexity. Previously, no superpolynomial separation was known for total matrices. In particular, we give a quantitative answer to a question of Linial, Mendelson, Schechtman, and Shraibman (Combinatorica, 2007), who asked whether the logarithmic ... more >>>



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