Variable Selection
126 papers with code • 0 benchmarks • 0 datasets
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DeepLINK-T: deep learning inference for time series data using knockoffs and LSTM
zuowx/deeplink-t
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DeepLINK-T combines deep learning with knockoff inference to control FDR in feature selection for time series models, accommodating a wide variety of feature distributions.
Predictive Analytics of Varieties of Potatoes
We explore the application of machine learning algorithms to predict the suitability of Russet potato clones for advancement in breeding trials.
High-dimensional forecasting with known knowns and known unknowns
Forecasts play a central role in decision making under uncertainty.
A deep learning pipeline for cross-sectional and longitudinal multiview data integration
Biomedical research now commonly integrates diverse data types or views from the same individuals to better understand the pathobiology of complex diseases, but the challenge lies in meaningfully integrating these diverse views.
Co-data Learning for Bayesian Additive Regression Trees
To address these challenges, we propose to incorporate co-data, i. e. external information on the covariates, into Bayesian additive regression trees (BART), a sum-of-trees prediction model that utilizes priors on the tree parameters to prevent overfitting.
Effect of hyperparameters on variable selection in random forests
In conclusion, the default values of the hyperparameters will not always be suitable for identifying important variables.
SharpSAT-TD in Model Counting Competitions 2021-2023
We describe SharpSAT-TD, our submission to the unweighted and weighted tracks of the Model Counting Competition in 2021-2023, which has won in total $6$ first places in different tracks of the competition.
Nonlinear Permuted Granger Causality
Granger causal inference is a contentious but widespread method used in fields ranging from economics to neuroscience.
Scalable variable selection for two-view learning tasks with projection operators
With the projection operators the relationship, correlation, between sets of input and output variables can also be expressed by kernel functions, thus nonlinear correlation models can be exploited as well.
Structured Learning in Time-dependent Cox Models
We propose a flexible framework for variable selection in time-dependent Cox models, accommodating complex selection rules.
