3 papers
cs.AI2025
Efficient Prediction of Pass@k Scaling in Large Language Models
Joshua Kazdan, Rylan Schaeffer, Youssef Allouah +4
Assessing the capabilities and risks of frontier AI systems is a critical area of research, and recent work has shown that repeated sampling from models can dramatically increase b…
cs.LG2023
Harnessing the Power of Choices in Decision Tree Learning
Guy Blanc, Jane Lange, Chirag Pabbaraju +3
We propose a simple generalization of standard and empirically successful decision tree learning algorithms such as ID3, C4.5, and CART. These algorithms, which have been central t…
cs.LG2023
MAPTree: Beating "Optimal" Decision Trees with Bayesian Decision Trees
Colin Sullivan, Mo Tiwari, Sebastian Thrun
Decision trees remain one of the most popular machine learning models today, largely due to their out-of-the-box performance and interpretability. In this work, we present a Bayesi…