10 citations · 26 across the 8 of their papers we have counts for
11 papers
ILMART: Interpretable Ranking with Constrained LambdaMART
Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando +2
Interpretable Learning to Rank (LtR) is an emerging field within the research area of explainable AI, aiming at developing intelligible and accurate predictive models. While most o…
Beyond Robustness: Resilience Verification of Tree-Based Classifiers
Stefano Calzavara, Lorenzo Cazzaro, Claudio Lucchese +2
In this paper we criticize the robustness measure traditionally employed to assess the performance of machine learning models deployed in adversarial settings. To mitigate the limi…
EiFFFeL: Enforcing Fairness in Forests by Flipping Leaves
Seyum Assefa Abebe, Claudio Lucchese, Salvatore Orlando
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…
Learning Early Exit Strategies for Additive Ranking Ensembles
Francesco Busolin, Claudio Lucchese, Franco Maria Nardini +3
Modern search engine ranking pipelines are commonly based on large machine-learned ensembles of regression trees. We propose LEAR, a novel - learned - technique aimed to reduce the…
Query-level Early Exit for Additive Learning-to-Rank Ensembles
Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando +2
Search engine ranking pipelines are commonly based on large ensembles of machine-learned decision trees. The tight constraints on query response time recently motivated researchers…
Feature Partitioning for Robust Tree Ensembles and their Certification in Adversarial Scenarios
Stefano Calzavara, Claudio Lucchese, Federico Marcuzzi +1
Machine learning algorithms, however effective, are known to be vulnerable in adversarial scenarios where a malicious user may inject manipulated instances. In this work we focus o…