138 citations · 532 across the 35 of their papers we have counts for
3 papers · 1 filter
Neural Additive Models for Location Scale and Shape: A Framework for Interpretable Neural Regression Beyond the Mean
Anton Thielmann, René-Marcel Kruse, Thomas Kneib +1
Deep neural networks (DNNs) have proven to be highly effective in a variety of tasks, making them the go-to method for problems requiring high-level predictive power. Despite this…
Distributional Gradient Boosting Machines
Alexander März, Thomas Kneib
We present a unified probabilistic gradient boosting framework for regression tasks that models and predicts the entire conditional distribution of a univariate response variable a…
Boosting Joint Models for Longitudinal and Time-to-Event Data
Elisabeth Waldmann, David Taylor-Robinson, Nadja Klein +4
Joint Models for longitudinal and time-to-event data have gained a lot of attention in the last few years as they are a helpful technique to approach common a data structure in cli…