12 citations · 34 across the 11 of their papers we have counts for
6 papers · 1 filter
Help the machine to help you: an evaluation in the wild of egocentric data cleaning via skeptical learning
Andrea Bontempelli, Matteo Busso, Leonardo Javier Malcotti +1
Any digital personal assistant, whether used to support task performance, answer questions, or manage work and daily life, including fitness schedules, requires high-quality annota…
Concept-level Debugging of Part-Prototype Networks
Andrea Bontempelli, Stefano Teso, Katya Tentori +2
Part-prototype Networks (ProtoPNets) are concept-based classifiers designed to achieve the same performance as black-box models without compromising transparency. ProtoPNets comput…
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…
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…
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…
Learning in the Wild with Incremental Skeptical Gaussian Processes
Andrea Bontempelli, Stefano Teso, Fausto Giunchiglia +1
The ability to learn from human supervision is fundamental for personal assistants and other interactive applications of AI. Two central challenges for deploying interactive learne…