Extends gradient boosting framework to vector inputs and sketches an efficient algorithm for histogram-based decision trees.
A Blockwise Descent Algorithm for Group-penalized Multiresponse and Multinomial Regression
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abstract
In this paper we purpose a blockwise descent algorithm for group-penalized multiresponse regression. Using a quasi-newton framework we extend this to group-penalized multinomial regression. We give a publicly available implementation for these in R, and compare the speed of this algorithm to a competing algorithm --- we show that our implementation is an order of magnitude faster than its competitor, and can solve gene-expression-sized problems in real time.
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stat.ML 1years
2026 1verdicts
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Gradient boosting with vector-valued leafs
Extends gradient boosting framework to vector inputs and sketches an efficient algorithm for histogram-based decision trees.