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Found 7 papers, 5 papers with code
Improving COVID-19 Forecasting using eXogenous Variables
We demonstrate significant enhancement in the forecasting accuracy for a COVID-19 dataset, with a maximum improvement in forecasting accuracy by 64. 58% and 59. 18% (on average) over the GCN-LSTM model in the national level data, and 58. 79% and 52. 40% (on average) over the GCN-LSTM model in the state level data.
Large-scale optimal transport map estimation using projection pursuit
We theoretically show the proposed dimension reduction method can consistently estimate the most ``informative'' projection direction in each iteration.
Model-free Feature Screening and FDR Control with Knockoff Features
It can be shown that the proposed two-step approach enjoys both sure screening and FDR control if the pre-specified FDR level $\alpha$ is greater or equal to $1/s$, where $s$ is the number of active features.
Mathematics Content Understanding for Cyberlearning via Formula Evolution Map
Although the scientific digital library is growing at a rapid pace, scholars/students often find reading Science, Technology, Engineering, and Mathematics (STEM) literature daunting, especially for the math-content/formula.
User-Friendly Covariance Estimation for Heavy-Tailed Distributions
We offer a survey of recent results on covariance estimation for heavy-tailed distributions.
Methodology Statistics Theory Statistics Theory
Preliminary Exploration of Formula Embedding for Mathematical Information Retrieval: can mathematical formulae be embedded like a natural language?
While neural network approaches are achieving breakthrough performance in the natural language related fields, there have been few similar attempts at mathematical language related tasks.
Factor-Adjusted Regularized Model Selection
This paper studies model selection consistency for high dimensional sparse regression when data exhibits both cross-sectional and serial dependency.
Methodology
