22 citations · 138 across the 48 of their papers we have counts for
8 papers · 1 filter
Machine Learning for Utility Prediction in Argument-Based Computational Persuasion
Ivan Donadello, Anthony Hunter, Stefano Teso +1
Automated persuasion systems (APS) aim to persuade a user to believe something by entering into a dialogue in which arguments and counterarguments are exchanged. To maximize the pr…
Toward a Unified Framework for Debugging Concept-based Models
Andrea Bontempelli, Fausto Giunchiglia, Andrea Passerini +1
In this paper, we tackle interactive debugging of "gray-box" concept-based models (CBMs). These models learn task-relevant concepts appearing in the inputs and then compute a predi…
Learning Mixed-Integer Linear Programs from Contextual Examples
Mohit Kumar, Samuel Kolb, Luc De Raedt +1
Mixed-integer linear programs (MILPs) are widely used in artificial intelligence and operations research to model complex decision problems like scheduling and routing. Designing s…
Interactive Label Cleaning with Example-based Explanations
Stefano Teso, Andrea Bontempelli, Fausto Giunchiglia +1
We tackle sequential learning under label noise in applications where a human supervisor can be queried to relabel suspicious examples. Existing approaches are flawed, in that they…
Neuro-Symbolic Constraint Programming for Structured Prediction
Paolo Dragone, Stefano Teso, Andrea Passerini
We propose Nester, a method for injecting neural networks into constrained structured predictors. The job of the neural network(s) is to compute an initial, raw prediction that is…
Human-in-the-loop Handling of Knowledge Drift
Andrea Bontempelli, Fausto Giunchiglia, Andrea Passerini +1
We introduce and study knowledge drift (KD), a complex form of drift that occurs in hierarchical classification. Under KD the vocabulary of concepts, their individual distributions…