32 citations · 32 across the 3 of their papers we have counts for
5 papers
Random Cycle Coding: Lossless Compression of Cluster Assignments via Bits-Back Coding
Daniel Severo, Ashish Khisti, Alireza Makhzani
We present an optimal method for encoding cluster assignments of arbitrary data sets. Our method, Random Cycle Coding (RCC), encodes data sequentially and sends assignment informat…
Random Edge Coding: One-Shot Bits-Back Coding of Large Labeled Graphs
Daniel Severo, James Townsend, Ashish Khisti +1
We present a one-shot method for compressing large labeled graphs called Random Edge Coding. When paired with a parameter-free model based on Pólya's Urn, the worst-case computatio…
Variational Model Inversion Attacks
Kuan-Chieh Wang, Yan Fu, Ke Li +3
Given the ubiquity of deep neural networks, it is important that these models do not reveal information about sensitive data that they have been trained on. In model inversion atta…
Compressing Multisets with Large Alphabets using Bits-Back Coding
Daniel Severo, James Townsend, Ashish Khisti +2
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…
Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding
Yangjun Ruan, Karen Ullrich, Daniel Severo +5
Latent variable models have been successfully applied in lossless compression with the bits-back coding algorithm. However, bits-back suffers from an increase in the bitrate equal…