411 citations · 1.7k across the 27 of their papers we have counts for
4 papers · 1 filter
Optimal Compression of Locally Differentially Private Mechanisms
Abhin Shah, Wei-Ning Chen, Johannes Balle +2
Compressing the output of ε-locally differentially private (LDP) randomizers naively leads to suboptimal utility. In this work, we demonstrate the benefits of using schemes that jo…
Algorithms for the Communication of Samples
Lucas Theis, Noureldin Yosri
The efficient communication of noisy data has applications in several areas of machine learning, such as neural compression or differential privacy, and is also known as reverse ch…
A coding theorem for the rate-distortion-perception function
Lucas Theis, Aaron B. Wagner
The rate-distortion-perception function (RDPF; Blau and Michaeli, 2019) has emerged as a useful tool for thinking about realism and distortion of reconstructions in lossy compressi…
On the advantages of stochastic encoders
Lucas Theis, Eirikur Agustsson
Stochastic encoders have been used in rate-distortion theory and neural compression because they can be easier to handle. However, in performance comparisons with deterministic enc…