20 results for “information theory”
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This paper introduces a novel algorithm for generating k Hamming weight binary words in linear time while minimizing random bit consumption.
The authors introduce a quantum empirical operator and use it to prove a universal achievability result for composite quantum hypothesis testing.
This paper proves space lower bounds for entropy-efficient random sampling using i.i.d. uniform bits.
This paper establishes a Coding Theorem in the context of symmetry groups and develops a connection between subgroups of a group and subsets of binary strings.
André Ribeiro, Rúben Garrido, Violeta Ramos, António Alberto +27 more
This paper presents the 2026 Algorithmic Information Theory Data Compression Challenge, evaluating lossless compressors under realistic constraints and revealing performance dependencies.
This paper derives bounds on the probability of incorrect clustering of noisy short sequences using statistically optimal rules, focusing on DNA storage decoders.
The paper derives new informational inequalities using Stam-like, moment-entropy-like inequalities, and a recently established Rényi entropy-based inequality. It obtains a Stam-like inequality connect…
The paper proves a stochastic comparison for Gaussian maxima, resolving the Weak Simplex Conjecture and proving the Simplex Mean Width Conjecture.
This paper studies the existence of polynomial measures of dependence between two random variables that satisfy the data processing inequality and vanish on independence. It proves that no such polyno…
The paper proves an information-theoretically optimal gap-majority lemma in the two-player randomized communication model, achieving the correct linear scaling and constant-constant tradeoff.
This paper measures the lower bound for the shortest program generating a sequence, proving a conservation law and providing a deterministic engine to recover generating programs for certain sequences…
This paper proves conditions for efficiently approximating expectations of certain functions with respect to standard Gaussian or symmetric exponential probability measures.
The paper improves Banaszczyk's inequality, providing a significantly better tail estimate for the discrete Gaussian measure on a lattice, which has applications in analyzing dual attacks against the…
The paper establishes information-theoretic lower bounds for stochastic optimization using low-bit gradients by reducing the problem to compressed Gaussian mean estimation, yielding sharp bounds on co…