mlsanityData to 14 Oct 2026

Compressing Multisets with Large Alphabets using Bits-Back Coding

arXiv 2107.09202 ↗First posted 15 Jul 2021cs.IT cs.LG eess.SPcited by 4

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Abstract (arXiv)

Current methods which compress multisets at an optimal rate have computational complexity that scales linearly with alphabet size, making them too slow to be practical in many real-world settings. We show how to convert a compression algorithm for sequences into one for multisets, in exchange for an additional complexity term that is quasi-linear in sequence length. This allows us to compress multisets of exchangeable symbols at an optimal rate, with computational complexity decoupled from the alphabet size. The key insight is to avoid encoding the multiset directly, and instead compress a proxy sequence, using a technique called `bits-back coding'. We demonstrate the method experimentally on tasks which are intractable with previous optimal-rate methods: compression of multisets of images and JavaScript Object Notation (JSON) files. Code for our experiments is available at https://github.com/facebookresearch/multiset-compression.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 6 of its 30 references that are in the map. First posted on arXiv 15 Jul 2021.

Builds on

6 of 30 references.

References · 6

  1. 2019Practical Lossless Compression with Latent Variables using Bits Back Codingcited by 86
  2. 2019Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variablescited by 52
  3. 2019HiLLoC: Lossless Image Compression with Hierarchical Latent Variable Modelscited by 37
  4. 2021Improving Lossless Compression Rates via Monte Carlo Bits-Back Codingcited by 19
  5. 2021Lossless compression with state space models using bits back codingcited by 6
  6. 2021Lossless Compression with Latent Variable Modelscited by 4

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Citing papers per year: 2023: 1; 2024: 2; 2025: 1.

4 papers.