6 citations · 11 across the 5 of their papers we have counts for
6 papers · 1 filter
Neural Optimization with Adaptive Heuristics for Intelligent Marketing System
Changshuai Wei, Benjamin Zelditch, Joyce Chen +6
Computational marketing has become increasingly important in today's digital world, facing challenges such as massive heterogeneous data, multi-channel customer journeys, and limit…
Smooth multi-period forecasting with application to prediction of COVID-19 cases
Elena Tuzhilina, Trevor J. Hastie, Daniel J. McDonald +2
Forecasting methodologies have always attracted a lot of attention and have become an especially hot topic since the beginning of the COVID-19 pandemic. In this paper we consider t…
Feature-weighted elastic net: using "features of features" for better prediction
J. Kenneth Tay, Nima Aghaeepour, Trevor Hastie +1
In some supervised learning settings, the practitioner might have additional information on the features used for prediction. We propose a new method which leverages this additiona…
Reluctant generalized additive modeling
J. Kenneth Tay, Robert Tibshirani
Sparse generalized additive models (GAMs) are an extension of sparse generalized linear models which allow a model's prediction to vary non-linearly with an input variable. This en…
Principal component-guided sparse regression
J. Kenneth Tay, Jerome Friedman, Robert Tibshirani
We propose a new method for supervised learning, especially suited to wide data where the number of features is much greater than the number of observations. The method combines th…
A latent factor approach for prediction from multiple assays
J. Kenneth Tay, Robert Tibshirani
In many domains such as healthcare or finance, data often come in different assays or measurement modalities, with features in each assay having a common theme. Simply concatenatin…