Zobrazeno 1 - 10
of 474
pro vyhledávání: '"LEPRI, BRUNO"'
Algorithmic Recourse (AR) aims to provide users with actionable steps to overturn unfavourable decisions made by machine learning predictors. However, these actions often take time to implement (e.g., getting a degree can take years), and their effec
Externí odkaz:
http://arxiv.org/abs/2410.08007
Autor:
Campedelli, Gian Maria, Penzo, Nicolò, Stefan, Massimo, Dessì, Roberto, Guerini, Marco, Lepri, Bruno, Staiano, Jacopo
As Large Language Model (LLM)-based agents become increasingly autonomous and will more freely interact with each other, studying interactions between them becomes crucial to anticipate emergent phenomena and potential risks. Drawing inspiration from
Externí odkaz:
http://arxiv.org/abs/2410.07109
Assessing the performance of systems to classify Multi-Party Conversations (MPC) is challenging due to the interconnection between linguistic and structural characteristics of conversations. Conventional evaluation methods often overlook variances in
Externí odkaz:
http://arxiv.org/abs/2409.18602
In recent years, there have been significant advancements in computer vision which have led to the widespread deployment of image recognition and generation systems in socially relevant applications, from hiring to security screening. However, the pr
Externí odkaz:
http://arxiv.org/abs/2408.11448
The challenge of Multimodal Deformable Image Registration (MDIR) lies in the conversion and alignment of features between images of different modalities. Generative models (GMs) cannot retain the necessary information enough from the source modality
Externí odkaz:
http://arxiv.org/abs/2408.10703
We present the first application of modern Hopfield networks to the problem of portfolio optimization. We performed an extensive study based on combinatorial purged cross-validation over several datasets and compared our results to both traditional a
Externí odkaz:
http://arxiv.org/abs/2407.17645
Affective polarization and increasing social divisions affect social mixing and the spread of information across online and physical spaces, reinforcing social and electoral cleavages and influencing political outcomes. Here, using aggregated and de-
Externí odkaz:
http://arxiv.org/abs/2407.12146
The separation power of a machine learning model refers to its capacity to distinguish distinct inputs, and it is often employed as a proxy for its expressivity. In this paper, we propose a theoretical framework to investigate the separation power of
Externí odkaz:
http://arxiv.org/abs/2406.08966
Predicting the locations an individual will visit in the future is crucial for solving many societal issues like disease diffusion and reduction of pollution. However, next-location predictors require a significant amount of individual-level informat
Externí odkaz:
http://arxiv.org/abs/2405.20962
Autor:
Gulati, Aditya, Martinez-Garcia, Marina, Fernandez, Daniel, Lozano, Miguel Angel, Lepri, Bruno, Oliver, Nuria
The impact of cognitive biases on decision-making in the digital world remains under-explored despite its well-documented effects in physical contexts. This study addresses this gap by investigating the attractiveness halo effect using AI-based beaut
Externí odkaz:
http://arxiv.org/abs/2407.11981