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Doubly High-Dimensional Contextual Bandits: An Interpretable Model for Joint Assortment-Pricing
In each case, we show at least three-fold gains in revenue or profit by our bandit method, as well as the interpretability of the latent factor models that are learned.
Network regression and supervised centrality estimation
The centrality in a network is a popular metric for agents' network positions and is often used in regression models to model the network effect on an outcome variable of interest.
Nonparametric Empirical Bayes Estimation and Testing for Sparse and Heteroscedastic Signals
In contrast to the state-of-the-art methods, the proposed methods solve the estimation and testing problem at once with several merits: 1) an accurate sparsity estimation; 2) point estimates with shrinkage/soft-thresholding property; 3) credible intervals for uncertainty quantification; 4) an optimal multiple testing procedure that controls false discovery rate.
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