Presentation
PINE: Efficient Yet Effective Piecewise Linear Trees
DescriptionDecision trees are popularly used in statistics and machine learning. Piecewise linear trees, a type of model-based decision tree, employ linear models to evaluate splits and predict outcomes at the leaf nodes. While they can offer high accuracy, they are computationally expensive, and currently, no scalable implementations exist without harming accuracy.
We introduce PINE, an efficient yet effective approach for training piecewise linear trees, incorporating various algorithmic and system optimizations. These optimizations enable fast training on multicore CPUs without sacrificing model accuracy. We also present PINEBoost, which applies gradient boosting to PINE, and compare its performance with existing frameworks. Experimental results demonstrate that PINE and PINEBoost achieve superior accuracy and faster convergence rates across general datasets in regression tasks compared to state-of-the-art gradient boosting decision trees.
We introduce PINE, an efficient yet effective approach for training piecewise linear trees, incorporating various algorithmic and system optimizations. These optimizations enable fast training on multicore CPUs without sacrificing model accuracy. We also present PINEBoost, which applies gradient boosting to PINE, and compare its performance with existing frameworks. Experimental results demonstrate that PINE and PINEBoost achieve superior accuracy and faster convergence rates across general datasets in regression tasks compared to state-of-the-art gradient boosting decision trees.

Event Type
ACM Student Research Competition: Graduate Poster
ACM Student Research Competition: Undergraduate Poster
Doctoral Showcase
Posters
TimeTuesday, 19 November 202412pm - 5pm EST
LocationB302-B305
TP
XO/EX