The voice of COVID-19: Breath and cough recording classification with temporal decision trees and random forests

F Manzella, G Pagliarini, G Sciavicco, IE Stan - Artificial Intelligence in …, 2023 - Elsevier
Symbolic learning is the logic-based approach to machine learning, and its mission is to
provide algorithms and methodologies to extract logical information from data and express it …

Learning linear temporal properties from noisy data: A maxsat-based approach

JR Gaglione, D Neider, R Roy, U Topcu… - Automated Technology for …, 2021 - Springer
We address the problem of inferring descriptions of system behavior using Linear Temporal
Logic (LTL) from a finite set of positive and negative examples. Most of the existing …

Knowledge extraction with interval temporal logic decision trees

G Sciavicco, SI Eduard - arXiv preprint arXiv:2305.16864, 2023 - arxiv.org
Multivariate temporal, or time, series classification is, in a way, the temporal generalization of
(numeric) classification, as every instance is described by multiple time series instead of …

Bridging ltlf inference to GNN inference for learning ltlf formulae

W Luo, P Liang, J Du, H Wan, B Peng… - Proceedings of the AAAI …, 2022 - ojs.aaai.org
Learning linear temporal logic on finite traces (LTLf) formulae aims to learn a target formula
that characterizes the high-level behavior of a system from observation traces in planning …

Maxsat-based temporal logic inference from noisy data

JR Gaglione, D Neider, R Roy, U Topcu… - Innovations in Systems and …, 2022 - Springer
We address the problem of inferring descriptions of system behavior using temporal logic
from a finite set of positive and negative examples. In this paper, we consider two formalisms …

[PDF][PDF] Interval temporal random forests with an application to COVID-19 diagnosis

F Manzella, G Pagliarini, G Sciavicco… - … and Reasoning (TIME …, 2021 - drops.dagstuhl.de
Symbolic learning is the logic-based approach to machine learning. The mission of symbolic
learning is to provide algorithms and methodologies to extract logical information from data …

End-to-End Learning of LTLf Formulae by Faithful LTLf Encoding

H Wan, P Liang, J Du, W Luo, R Ye… - Proceedings of the AAAI …, 2024 - ojs.aaai.org
A key problem in rule mining is automatically discovering the underlying tree-structured
rules from large amounts of data. In this paper, we examine learning linear temporal logic on …

Bandit Interpretability of Deep Models via Confidence Selection

X Duan, H Li, P Wang, T Wang, B Liu, B Zhang - Neurocomputing, 2023 - Elsevier
Interpretability of black-box deep models is yet challenging because existing model-agnostic
methods mainly locally explain the behavior of the classifier by learning a linear proxy …

Uncertainty-aware signal temporal logic inference

N Baharisangari, JR Gaglione, D Neider… - … Workshop on Numerical …, 2021 - Springer
Temporal logic inference is the process of extracting formal descriptions of system behaviors
from data in the form of temporal logic formulas. The existing temporal logic inference …

Decision tree learning with spatial modal logics

G Pagliarini, G Sciavicco - arXiv preprint arXiv:2109.08325, 2021 - arxiv.org
Symbolic learning represents the most straightforward approach to interpretable modeling,
but its applications have been hampered by a single structural design choice: the adoption …