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Found 5 papers, 1 papers with code
Fair and Diverse DPP-based Data Summarization
Sampling methods that choose a subset of the data proportional to its diversity in the feature space are popular for data summarization.
Batched Gaussian Process Bandit Optimization via Determinantal Point Processes
Gaussian Process bandit optimization has emerged as a powerful tool for optimizing noisy black box functions.
How to be Fair and Diverse?
However, in doing so, a question that seems to be overlooked is whether it is possible to produce fair subsamples that are also adequately representative of the feature space of the data set - an important and classic requirement in machine learning.
On the Complexity of Constrained Determinantal Point Processes
Consequently, we obtain a few algorithms of independent interest: 1) to count over the base polytope of regular matroids when there are additional (succinct) budget constraints and, 2) to evaluate and compute the mixed characteristic polynomials, that played a central role in the resolution of the Kadison-Singer problem, for certain special cases.
On Sampling and Greedy MAP Inference of Constrained Determinantal Point Processes
When pairwise similarities are captured by a kernel, the determinants of submatrices provide a measure of diversity or independence of items within a subset.
