no code implementations • 31 Mar 2021 • Paolo Dragone, Stefano Teso, Andrea Passerini
We propose Nester, a method for injecting neural networks into constrained structured predictors.
1 code implementation • 22 Nov 2017 • Paolo Dragone, Stefano Teso, Mohit Kumar, Andrea Passerini
We propose a decomposition technique for large preference-based decision problems relying exclusively on inference and feedback over partial configurations.
1 code implementation • 21 Nov 2017 • Paolo Dragone, Stefano Teso, Andrea Passerini
The preferences are typically learned by querying the user for choice feedback over pairs or sets of objects.
no code implementations • 6 Dec 2016 • Stefano Teso, Paolo Dragone, Andrea Passerini
When faced with complex choices, users refine their own preference criteria as they explore the catalogue of options.
no code implementations • 22 Nov 2015 • Paolo Dragone
Our work is focused on the reimplementation of the resolution rules from Fern\'andez (2006) with a probabilistic account of the dialogue state.