Showing cs.LGShow all
3 papers · 1 filter
cs.LG2026
ArborEnum: Decision Tree Rashomon Sets over Continuous Features
Zakk Heile, Hayden McTavish, Margo Seltzer +1
The Rashomon effect describes the phenomenon that many models can achieve nearly equivalent performance on the same learning task, with significant ramifications for robustness, fe…
cs.LG2026
Multistage Defer Trees for Hybrid Interpretability: If at First You Can't Succeed, Tree Again
Zakk Heile, Hayden McTavish, Margo Seltzer +1
Recent work has shown that well-optimized individual decision trees can match complex black box models in some settings, primarily in noisy domains. For the remaining settings, how…
cs.LG2026
From Rashomon Theory to PRAXIS: Efficient Decision Tree Rashomon Sets
Zakk Heile, Hayden McTavish, Varun Babbar +2
Standard machine learning pipelines often admit many near-optimal models. These "Rashomon sets" pose a range of challenges and opportunities for uncertainty-aware, robust decision…