12 citations · 34 across the 11 of their papers we have counts for
11 papers
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…
Personalized Interpretability -- Interactive Alignment of Prototypical Parts Networks
Tomasz Michalski, Adam Wróbel, Andrea Bontempelli +6
Concept-based interpretable neural networks have gained significant attention due to their intuitive and easy-to-understand explanations based on case-based reasoning, such as "thi…
DiversityOne: A Multi-Country Smartphone Sensor Dataset for Everyday Life Behavior Modeling
Matteo Busso, Andrea Bontempelli, Leonardo Javier Malcotti +23
Understanding everyday life behavior of young adults through personal devices, e.g., smartphones and smartwatches, is key for various applications, from enhancing the user experien…
Egocentric Hierarchical Visual Semantics
Luca Erculiani, Andrea Bontempelli, Andrea Passerini +1
We are interested in aligning how people think about objects and what machines perceive, meaning by this the fact that object recognition, as performed by a machine, should follow…
Lifelong Personal Context Recognition
Andrea Bontempelli, Marcelo Rodas Britez, Xiaoyue Li +5
We focus on the development of AIs which live in lifelong symbiosis with a human. The key prerequisite for this task is that the AI understands - at any moment in time - the person…
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…
Streaming and Learning the Personal Context
Fausto Giunchiglia, Marcelo Rodas Britez, Andrea Bontempelli +1
The representation of the personal context is complex and essential to improve the help machines can give to humans for making sense of the world, and the help humans can give to m…
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…