arXiv:2605. 13830v2 Announce Type: replace-cross Abstract: Decision tree ensembles (DTE) are a popular model for a wide range of AI classification tasks, used in multiple safety critical domains, and hence verifying properties on these models has been an active topic of study over the last decade.
By Ajinkya Naik, Chaitanya Garg, S. Akshay, Ashutosh Gupta, Kuldeep S. Meel
arXiv:2606. 30995v1 Announce Type: new Abstract: Recent work has shown that well-optimized individual decision trees can match complex black box models in some settings, primarily in noisy domains.
By Zakk Heile, Hayden McTavish, Margo Seltzer, Cynthia Rudin
arXiv:2606. 01746v1 Announce Type: cross Abstract: Modern neural networks are highly susceptible to adversarial perturbations.
By Kai Wang
arXiv:2503. 12902v4 Announce Type: replace Abstract: Model trees provide an appealing way to perform interpretable machine learning for both classification and regression problems.
By Sabino Francesco Roselli, Eibe Frank
arXiv:2608.24104v1 Announce Type: cross
Abstract: Numerous methods have been developed to quantify feature attributions in individual predictions for tree ensembles. However, many applications requir...
By Zhongli Jiang, Min Zhang, Dabao Zhang
arXiv:2608. 15725v1 Announce Type: new Abstract: Predictive models in clinical and regulated settings must be accurate and fully auditable.
By Srikumar Krishnamoorthy