9 citations · 38 across the 16 of their papers we have counts for
7 papers · 1 filter
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…
Learning MAX-SAT from Contextual Examples for Combinatorial Optimisation
Mohit Kumar, Samuel Kolb, Stefano Teso +1
Combinatorial optimisation problems are ubiquitous in artificial intelligence. Designing the underlying models, however, requires substantial expertise, which is a limiting factor…
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…
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…
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…
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…