12 papers
Scalable Differentially Private Data Compression via Diffusion and Stochastic Codes
Gergely Flamich, Oykü Sıla Güner, Yanxiao Liu +1
The ever-increasing collection of personal data has created mounting pressure to develop technologies that protect sensitive aspects of individual identity. Differential privacy (D…
Data Compression with Stochastic Codes
Gergely Flamich, Deniz Gündüz
Machine learning has had a major impact on data compression over the last decade and opened up many new theoretical and applied fields of inquiry. This paper describes one such dir…
Multi-Marginal Couplings for Metropolis-Hastings
Buu Phan, Gergely Flamich, Ashish Khisti +1
Convergence diagnosis for Markov chain Monte Carlo is a matter of fundamental importance in computational statistics: it determines the resources allocated to a particular sampling…
Rejection Sampling is Optimal for Relative Entropy Coding
Spencer Hill, Fady Alajaji, Tamás Linder +1
In relative entropy coding, a sender aims to design a stochastic code such that, on input , the receiver can generate a sample . It is a standard r…
Singular Relative Entropy Coding with Bits-Back Rejection Sampling
Gergely Flamich, Spencer Hill
A relative entropy code for a source is a stochastic code that encodes random samples from a prescribed using as few bits as possible. A generalisation…
Data Compression with Relative Entropy Coding
Gergely Flamich
Over the last few years, machine learning unlocked previously infeasible features for compression, such as providing guarantees for users' privacy or tailoring compression to speci…