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Found 10 papers, 3 papers with code
Scalable Sampling for Nonsymmetric Determinantal Point Processes
However, existing work leaves open the question of scalable NDPP sampling.
Combining Public and Private Data
We argue that our mechanism is preferable to techniques that preserve the privacy of individuals by subsampling data proportionally to the privacy needs of users.
Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms
Federated learning is typically approached as an optimization problem, where the goal is to minimize a global loss function by distributing computation across client devices that possess local data and specify different parts of the global objective.
Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes
Determinantal point processes (DPPs) have attracted significant attention in machine learning for their ability to model subsets drawn from a large item collection.
Submodular Hamming Metrics
We show that there is a largely unexplored class of functions (positive polymatroids) that can define proper discrete metrics over pairs of binary vectors and that are fairly tractable to optimize over.
Expectation-Maximization for Learning Determinantal Point Processes
However, log-likelihood is non-convex in the entries of the kernel matrix, and this learning problem is conjectured to be NP-hard.
Near-Optimal MAP Inference for Determinantal Point Processes
Determinantal point processes (DPPs) have recently been proposed as computationally efficient probabilistic models of diverse sets for a variety of applications, including document summarization, image search, and pose estimation.
