4 citations · 6 across the 4 of their papers we have counts for
4 papers
Nonparametric Probabilistic Regression with Coarse Learners
Brian Lucena
Probabilistic Regression refers to predicting a full probability density function for the target conditional on the features. We present a nonparametric approach to this problem wh…
Loss Functions for Classification using Structured Entropy
Brian Lucena
Cross-entropy loss is the standard metric used to train classification models in deep learning and gradient boosting. It is well-known that this loss function fails to account for…
StructureBoost: Efficient Gradient Boosting for Structured Categorical Variables
Brian Lucena
Gradient boosting methods based on Structured Categorical Decision Trees (SCDT) have been demonstrated to outperform numerical and one-hot-encodings on problems where the categoric…
Exploiting Categorical Structure Using Tree-Based Methods
Brian Lucena
Standard methods of using categorical variables as predictors either endow them with an ordinal structure or assume they have no structure at all. However, categorical variables of…