Entropy Coding of Unordered Data Structures
Julius Kunze, Daniel Severo, Giulio Zani, Jan-Willem van de Meent, James Townsend
Abstract
We present shuffle coding, a general method for optimal compression of sequences of unordered objects using bits-back coding. Data structures that can be compressed using shuffle coding include multisets, graphs, hypergraphs, and others. We release an implementation that can easily be adapted to different data types and statistical models, and demonstrate that our implementation achieves state-of-the-art compression rates on a range of graph datasets including molecular data.
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Install the CLIlune papers fulltext 3f8ec723-177f-4742-b69d-c90bf1cb5427Cited by top-tier papers3
- Practical Shuffle CodingJulius Kunze, Daniel Severo, Jan-Willem van de Meent, James TownsendNeurIPS 2024 · 2 citations
- Learning Distributions over Permutations and Rankings with Factorized RepresentationsDaniel Severo, Brian Karrer, Niklas NolteICLR 2026 · 1 citation
- Decouple Distortion from Perception: Region Adaptive Diffusion for Extreme-low Bitrate Perception Image CompressionJinchang Xu, Shaokang Wang, Jintao Chen, Zhe Li et al.CVPR 2025
Builds on2
- Order Matters: Probabilistic Modeling of Node Sequence for Graph GenerationXiaohui Chen, Xu Han, Jiajing Hu, Francisco J. R. Ruiz et al.ICML 2021 · 40 citations
- Partition and Code: learning how to compress graphsGiorgos Bouritsas, Andreas Loukas, Nikolaos Karalias, Michael M. BronsteinNeurIPS 2021 · 23 citations
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