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Found 3 papers, 2 papers with code
Analysis of Bootstrap and Subsampling in High-dimensional Regularized Regression
We investigate popular resampling methods for estimating the uncertainty of statistical models, such as subsampling, bootstrap and the jackknife, and their performance in high-dimensional supervised regression tasks.
Learning with Combinatorial Optimization Layers: a Probabilistic Approach
We demonstrate its abilities using a pathfinding problem on video game maps as guiding example, as well as three other applications from operations research.
Minimax Estimation of Partially-Observed Vector AutoRegressions
High-dimensional time series are a core ingredient of the statistical modeling toolkit, for which numerous estimation methods are known. But when observations are scarce or corrupted, the learning task becomes much harder. The question is: how much harder?
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