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Found 5 papers, 2 papers with code
Naturally Private Recommendations with Determinantal Point Processes
Often we consider machine learning models or statistical analysis methods which we endeavour to alter, by introducing a randomized mechanism, to make the model conform to a differential privacy constraint.
Intersectionality: Multiple Group Fairness in Expectation Constraints
Group fairness is an important concern for machine learning researchers, developers, and regulators.
A General Framework for Fair Regression
Fairness, through its many forms and definitions, has become an important issue facing the machine learning community.
Entropic Trace Estimates for Log Determinants
The scalable calculation of matrix determinants has been a bottleneck to the widespread application of many machine learning methods such as determinantal point processes, Gaussian processes, generalised Markov random fields, graph models and many others.
Bayesian Inference of Log Determinants
The log-determinant of a kernel matrix appears in a variety of machine learning problems, ranging from determinantal point processes and generalized Markov random fields, through to the training of Gaussian processes.
