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Found 6 papers, 1 papers with code
Nonparametric learning from Bayesian models with randomized objective functions
Bayesian learning is built on an assumption that the model space contains a true reflection of the data generating mechanism.
TensOrMachine: Probabilistic Boolean Tensor Decomposition
Boolean tensor decomposition approximates data of multi-way binary relationships as product of interpretable low-rank binary factors, following the rules of Boolean algebra.
Encrypted accelerated least squares regression
Information that is stored in an encrypted format is, by definition, usually not amenable to statistical analysis or machine learning methods.
Bayesian Boolean Matrix Factorisation
Boolean matrix factorisation aims to decompose a binary data matrix into an approximate Boolean product of two low rank, binary matrices: one containing meaningful patterns, the other quantifying how the observations can be expressed as a combination of these patterns.
Encrypted statistical machine learning: new privacy preserving methods
We present two new statistical machine learning methods designed to learn on fully homomorphic encrypted (FHE) data.
A review of homomorphic encryption and software tools for encrypted statistical machine learning
Recent advances in cryptography promise to enable secure statistical computation on encrypted data, whereby a limited set of operations can be carried out without the need to first decrypt.
