Search Results for author:
Found 14 papers, 5 papers with code
A Learning-to-Rank Formulation of Clustering-Based Approximate Nearest Neighbor Search
Its objective is to return a set of $k$ data points that are closest to a query point, with its accuracy measured by the proportion of exact nearest neighbors captured in the returned set.
Verifiable Boosted Tree Ensembles
Verifiable learning advocates for training machine learning models amenable to efficient security verification.
Fast Inference of Tree Ensembles on ARM Devices
Second, we extend our implementation from ranking models to classification models such as Random Forests.
Efficient and Effective Tree-based and Neural Learning to Rank
We believe that by understanding the fundamentals underpinning these algorithmic and data structure solutions for containing the contentious relationship between efficiency and effectiveness, one can better identify future directions and more efficiently determine the merits of ideas.
A Theoretical Framework for AI Models Explainability with Application in Biomedicine
EXplainable Artificial Intelligence (XAI) is a vibrant research topic in the artificial intelligence community, with growing interest across methods and domains.
Explainable Global Fairness Verification of Tree-Based Classifiers
We present a new approach to the global fairness verification of tree-based classifiers.
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.
Certifying Decision Trees Against Evasion Attacks by Program Analysis
Machine learning has proved invaluable for a range of different tasks, yet it also proved vulnerable to evasion attacks, i. e., maliciously crafted perturbations of input data designed to force mispredictions.
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.
