Search Results for author:
Found 5 papers, 1 papers with code
Interpretable Prediction and Feature Selection for Survival Analysis
While DyS works well for all survival analysis problems, it is particularly useful for large (in $n$ and $p$) survival datasets such as those commonly found in observational healthcare studies.
Interpretable Survival Analysis for Heart Failure Risk Prediction
Specifically, we use an improved version of survival stacking to transform a survival analysis problem to a classification problem, ControlBurn to perform feature selection, and Explainable Boosting Machines to generate interpretable predictions.
Cross-Frequency Time Series Meta-Forecasting
Meta-forecasting is a newly emerging field which combines meta-learning and time series forecasting.
The Missing Indicator Method: From Low to High Dimensions
In this paper, we show empirically and theoretically that MIM improves performance for informative missing values, and we prove that MIM does not hurt linear models asymptotically for uninformative missing values.
CDF Normalization for Controlling the Distribution of Hidden Nodes
Batch Normalizaiton (BN) is a normalization method for deep neural networks that has been shown to accelerate training.
