90 citations · 514 across the 54 of their papers we have counts for
8 papers · 1 filter
Automatic Causal Fairness Analysis with LLM-Generated Reporting
Alessia Berarducci, Eric Rossetto, Alessandro Antonucci +1
AutoML, intended as the process of automating the application of machine learning to real-world problems, is a key step for AI popularisation. Most AutoML frameworks are not accoun…
The AI off-switch problem as a signalling game: bounded rationality and incomparability
Alessio Benavoli, Alessandro Facchini, Marco Zaffalon
The off-switch problem is a critical challenge in AI control: if an AI system resists being switched off, it poses a significant risk. In this paper, we model the off-switch proble…
Statistical comparison of classifiers through Bayesian hierarchical modelling
Giorgio Corani, Alessio Benavoli, Janez Demšar +2
Usually one compares the accuracy of two competing classifiers via null hypothesis significance tests (nhst). Yet the nhst tests suffer from important shortcomings, which can be ov…
State Space representation of non-stationary Gaussian Processes
Alessio Benavoli, Marco Zaffalon
The state space (SS) representation of Gaussian processes (GP) has recently gained a lot of interest. The main reason is that it allows to compute GPs based inferences in O(n), whe…
Limits of Learning about a Categorical Latent Variable under Prior Near-Ignorance
Alberto Piatti, Marco Zaffalon, Fabio Trojani +1
In this paper, we consider the coherent theory of (epistemic) uncertainty of Walley, in which beliefs are represented through sets of probability distributions, and we focus on the…
Robust Inference of Trees
Marco Zaffalon, Marcus Hutter
This paper is concerned with the reliable inference of optimal tree-approximations to the dependency structure of an unknown distribution generating data. The traditional approach…
Distribution of Mutual Information from Complete and Incomplete Data
Marcus Hutter, Marco Zaffalon
Mutual information is widely used, in a descriptive way, to measure the stochastic dependence of categorical random variables. In order to address questions such as the reliability…
Bayesian Treatment of Incomplete Discrete Data applied to Mutual Information and Feature Selection
Marcus Hutter, Marco Zaffalon
Given the joint chances of a pair of random variables one can compute quantities of interest, like the mutual information. The Bayesian treatment of unknown chances involves comput…