A Family of Distributions of Random Subsets for Controlling Positive and Negative Dependence
Not on the map. Why?
Abstract (arXiv)
Positive and negative dependence are fundamental concepts that characterize the attractive and repulsive behavior of random subsets. Although some probabilistic models are known to exhibit positive or negative dependence, it is challenging to seamlessly bridge them with a practicable probabilistic model. In this study, we introduce a new family of distributions, named the discrete kernel point process (DKPP), which includes determinantal point processes and parts of Boltzmann machines. We also develop some computational methods for probabilistic operations and inference with DKPPs, such as calculating marginal and conditional probabilities and learning the parameters. Our numerical experiments demonstrate the controllability of positive and negative dependence and the effectiveness of the computational methods for DKPPs.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 17 of its 61 references that are in the map. First posted on arXiv 2 Aug 2024.
Builds on
17 of 61 references.
References · 17
- 2019Explaining Machine Learning Classifiers through Diverse Counterfactual Explanationscited by 614
- 2013Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networkscited by 47
- 2022A Langevin-like Sampler for Discrete Distributionscited by 44
- 2020Determinantal Point Processes in Randomized Numerical Linear Algebracited by 41
- 2014Expectation-Maximization for Learning Determinantal Point Processescited by 20
- 2019Bayesian experimental design using regularized determinantal point processescited by 17
- 2015Fixed-point algorithms for learning determinantal point processescited by 14
- 2016Low-Rank Factorization of Determinantal Point Processes for Recommendationcited by 14
- 2020Precise expressions for random projections: Low-rank approximation and randomized Newtoncited by 13
- 2014Provable Submodular Minimization using Wolfe's Algorithmcited by 10
- 2016Learning Determinantal Point Processes in Sublinear Timecited by 8
- 2013Fast Semidifferential-based Submodular Function Optimizationcited by 7
5 more
- 2016Kronecker Determinantal Point Processescited by 6
- 2017Lost Relatives of the Gumbel Trickcited by 6
- 2019Unbiased estimators for random design regressioncited by 6
- 2022Gradient-Guided Importance Sampling for Learning Binary Energy-Based Modelscited by 6
- 2015Fast Mixing for Discrete Point Processescited by 3
Built on by
Citing papers per year: 2026: 2.
2 papers.