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New method for channel simulation aids compression and privacy

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method for simulating discrete-to-continuous channels, which can be applied to compression and privacy tasks. This approach uses a fixed number of random samples, making its runtime independent of the channel and input, unlike previous methods that could require an infinite number of samples. The scheme offers a flexible trade-off between sample count and compression rate, and has been demonstrated for variable-rate compression with VQ-VAEs and differentially private distributed mean estimation using the Gaussian mechanism. AI

IMPACT This research introduces a novel simulation technique that could improve efficiency in compression and privacy applications within machine learning.

RANK_REASON The cluster contains a single academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New method for channel simulation aids compression and privacy

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The cluster contains a single academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Joseph Rowan, Buu Phan, Ashish J. Khisti ·

    Scalable Discrete-to-Continuous Channel Simulation for Compression and Privacy

    arXiv:2609.12067v1 Announce Type: new Abstract: Channel simulation has recently emerged as a useful component in machine learning systems where samples from a prescribed probability distribution are to be compressed. Yet, general channel simulation algorithms often suffer from hi…

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