This paper introduces a structural theorem for the sparsifiability of real-valued codes, which generalizes both combinatorial and continuous notions of sparsification.

This paper proposes methods to optimally permute the rows and columns of a sparse matrix to minimize the number of cyclic diagonals required for homomorphic sparse-matrix vector multiplication, signif…

This paper presents the 2026 Algorithmic Information Theory Data Compression Challenge, evaluating lossless compressors under realistic constraints and revealing performance dependencies.

This paper establishes recovery guarantees for low-rank Hermitian matrices from quadratic sampling matrices under the assumption of finite 4+δ moments of the entries.

This paper presents a local computation algorithm to approximate the top eigenvector of a symmetric matrix with entries between -1 and 1, building on Swartworth and Woodruff's work.

This paper compares PCA and LPC for dimensionality reduction in cyberattack classification, demonstrating that both techniques can achieve substantial feature compression with minimal loss of classifi…

This paper introduces and studies the block tensor rank of sum-rank metric codes, showing its additive decomposition and deriving lower bounds.

The paper introduces $g$MAGNUS, a new algorithm for sparse matrix-matrix multiplication on GPUs that addresses heavy rows by reordering intermediate products and achieves significant speedups.

This paper proposes a compression scheme for transmitting sparse local updates in distributed learning systems, and provides a converse based on f-divergence to characterize the communication-accuracy…

The paper analyzes the structured CVP distance on the log-unit lattice of cyclotomic fields, significantly reducing the conjectured CDPR factor for the ML-KEM cryptosystem from exponential to sub-poly…

The paper describes how to compute singular value soft-thresholding using matrix polar decomposition for faster GPU processing.

This paper shows that sub-dictionaries sampled from a coherent dictionary using a coherence rejective Poisson sampling model are well-conditioned with high probability, as long as their expected size…

This paper generalizes the connection between graph theory, association schemes, and coding theory to the space of tensors over a finite field, and derives the spectrum of the corresponding graph.

This paper systematically analyzes combining dimensionality reduction and quantization to compress text embeddings, showing that this combined approach achieves substantial compression (e.g., 0.1% siz…

This paper presents a method for compressing matrices using a RePair straight-line program (SLP), allowing matrix-vector products with time and space proportional to the compressed size, and demonstra…

A new solver-free parallel spectral sparsification algorithm for weighted graphs is presented, relying on low-diameter decompositions and independent sampling, eliminating dependence on target approxi…

This paper models the changes in graph adjacency matrices over time as a low-rank matrix and develops a method for jointly interpolating signals and estimating graph adjacency matrices using this assu…