22 citations · 138 across the 48 of their papers we have counts for
7 papers · 1 filter
Multi-Modal Subjective Context Modelling and Recognition
Qiang Shen, Stefano Teso, Wanyi Zhang +2
Applications like personal assistants need to be aware ofthe user's context, e.g., where they are, what they are doing, and with whom. Context information is usually inferred from…
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…
Machine Guides, Human Supervises: Interactive Learning with Global Explanations
Teodora Popordanoska, Mohit Kumar, Stefano Teso
We introduce explanatory guided learning (XGL), a novel interactive learning strategy in which a machine guides a human supervisor toward selecting informative examples for a class…
Toward Machine-Guided, Human-Initiated Explanatory Interactive Learning
Teodora Popordanoska, Mohit Kumar, Stefano Teso
Recent work has demonstrated the promise of combining local explanations with active learning for understanding and supervising black-box models. Here we show that, under specific…
Efficient Generation of Structured Objects with Constrained Adversarial Networks
Luca Di Liello, Pierfrancesco Ardino, Jacopo Gobbi +3
Generative Adversarial Networks (GANs) struggle to generate structured objects like molecules and game maps. The issue is that structured objects must satisfy hard requirements (e.…
Human-Machine Collaboration for Democratizing Data Science
Clément Gautrais, Yann Dauxais, Stefano Teso +3
Everybody wants to analyse their data, but only few posses the data science expertise to to this. Motivated by this observation we introduce a novel framework and system \textsc{Vi…