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Electronic Colloquium on Computational Complexity

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All reports by Author Antonina Kolokolova:

TR16-144 | 15th September 2016
Sam Buss, Valentine Kabanets, Antonina Kolokolova, Michal Koucky

Expander Construction in VNC${}^1$

Revisions: 2

We give a combinatorial analysis (using edge expansion) of a variant of the iterative expander construction due to Reingold, Vadhan, and Wigderson [Annals of Mathematics, 2002], and show that this analysis can be formalized in the bounded-arithmetic system $VNC^1$ (corresponding to the ``$NC^1$ reasoning''). As a corollary, we prove the ... more >>>

TR16-008 | 26th January 2016
Marco L. Carmosino, Russell Impagliazzo, Valentine Kabanets, Antonina Kolokolova

Algorithms from Natural Lower Bounds

Circuit analysis algorithms such as learning, SAT, minimum circuit size, and compression imply circuit lower bounds. We show a generic implication in the opposite direction: natural properties (in the sense of Razborov and Rudich) imply randomized learning and compression algorithms. This is the first such implication outside of the derandomization ... more >>>

TR13-057 | 5th April 2013
Ruiwen Chen, Valentine Kabanets, Antonina Kolokolova, Ronen Shaltiel, David Zuckerman

Mining Circuit Lower Bound Proofs for Meta-Algorithms

We show that circuit lower bound proofs based on the method of random restrictions yield non-trivial compression algorithms for ``easy'' Boolean functions from the corresponding circuit classes. The compression problem is defined as follows: given the truth table of an $n$-variate Boolean function $f$ computable by some unknown small circuit ... more >>>

TR13-024 | 7th February 2013
Valentine Kabanets, Antonina Kolokolova

Compression of Boolean Functions

We consider the problem of compression for ``easy'' Boolean functions: given the truth table of an $n$-variate Boolean function $f$ computable by some \emph{unknown small circuit} from a \emph{known class} of circuits, find in deterministic time $\poly(2^n)$ a circuit $C$ (no restriction on the type of $C$) computing $f$ so ... more >>>

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