5 citations · 10 across the 6 of their papers we have counts for
7 papers
Does task decomposition improve automatic NLG evaluation?
Sebastian Steindl, Nikos Voskarides, Alberto Gasparin +1
The LLM-as-a-judge (LLMaJ) framework has emerged as a promising solution for cheap, reproducible, reference-free Natural Language Generation (NLG) evaluation. Prior work seeks to i…
Post-hoc Alignment of LLM-judges to Human Judgment Distribution
Sebastian Steindl, Nikos Voskarides, Alberto Gasparin +1
The LLM-as-a-judge (LLMaJ) framework offers a cost-effective and reproducible solution for automatic evaluation. However, current evaluation practices typically compare LLMaJ judgm…
Fortuna: A Library for Uncertainty Quantification in Deep Learning
Gianluca Detommaso, Alberto Gasparin, Michele Donini +3
We present Fortuna, an open-source library for uncertainty quantification in deep learning. Fortuna supports a range of calibration techniques, such as conformal prediction that ca…
Uncertainty Calibration in Bayesian Neural Networks via Distance-Aware Priors
Gianluca Detommaso, Alberto Gasparin, Andrew Wilson +1
As we move away from the data, the predictive uncertainty should increase, since a great variety of explanations are consistent with the little available information. We introduce…
Ranking Micro-Influencers: a Novel Multi-Task Learning and Interpretable Framework
Adam Elwood, Alberto Gasparin, Alessandro Rozza
With the rise in use of social media to promote branded products, the demand for effective influencer marketing has increased. Brands are looking for improved ways to identify valu…
Deep Learning for Time Series Forecasting: The Electric Load Case
Alberto Gasparin, Slobodan Lukovic, Cesare Alippi
Management and efficient operations in critical infrastructure such as Smart Grids take huge advantage of accurate power load forecasting which, due to its nonlinear nature, remain…
Deriving item features relevance from collaborative domain knowledge
Maurizio Ferrari Dacrema, Alberto Gasparin, Paolo Cremonesi
An Item based recommender system works by computing a similarity between items, which can exploit past user interactions (collaborative filtering) or item features (content based f…