Search Results for author:
Found 7 papers, 3 papers with code
ILMART: Interpretable Ranking with Constrained LambdaMART
Interpretable Learning to Rank (LtR) is an emerging field within the research area of explainable AI, aiming at developing intelligible and accurate predictive models.
EiFFFeL: Enforcing Fairness in Forests by Flipping Leaves
Nowadays Machine Learning (ML) techniques are extensively adopted in many socially sensitive systems, thus requiring to carefully study the fairness of the decisions taken by such systems.
Beyond Robustness: Resilience Verification of Tree-Based Classifiers
In this paper we criticize the robustness measure traditionally employed to assess the performance of machine learning models deployed in adversarial settings.
Learning Early Exit Strategies for Additive Ranking Ensembles
Modern search engine ranking pipelines are commonly based on large machine-learned ensembles of regression trees.
Query-level Early Exit for Additive Learning-to-Rank Ensembles
In this paper, we investigate the novel problem of \textit{query-level early exiting}, aimed at deciding the profitability of early stopping the traversal of the ranking ensemble for all the candidate documents to be scored for a query, by simply returning a ranking based on the additive scores computed by a limited portion of the ensemble.
Feature Partitioning for Robust Tree Ensembles and their Certification in Adversarial Scenarios
The attacker aims at finding a minimal perturbation of a test instance that changes the model outcome.
Treant: Training Evasion-Aware Decision Trees
Despite its success and popularity, machine learning is now recognized as vulnerable to evasion attacks, i. e., carefully crafted perturbations of test inputs designed to force prediction errors.
