no code implementations • 26 Feb 2024 • Marco Zaffalon, Alessandro Antonucci
We prove that (i) the likelihood of such a dataset from the original Bayesian network is dominated by the global maximum of the likelihood from the empirical one; and that (ii) such a maximum is attained if and only if the parameters of the Bayesian network are consistent with those of the empirical model.
1 code implementation • 22 Dec 2023 • Alessandro Antonucci, Gregorio Piqué, Marco Zaffalon
We evaluate the ability of large language models (LLMs) to infer causal relations from natural language.
1 code implementation • 5 Oct 2023 • David Huber, Yizuo Chen, Alessandro Antonucci, Adnan Darwiche, Marco Zaffalon
We discuss the problem of bounding partially identifiable queries, such as counterfactuals, in Pearlian structural causal models.
no code implementations • 31 Jul 2023 • Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas, David Huber
We address the problem of integrating data from multiple, possibly biased, observational and interventional studies, to eventually compute counterfactuals in structural causal models.
no code implementations • 17 Jul 2023 • Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas, David Huber, Dario Azzimonti
This allows us to compute exact counterfactual bounds via algorithms for credal nets on a subclass of structural causal models.
1 code implementation • 6 Dec 2022 • Marco Zaffalon, Alessandro Antonucci, David Huber, Rafael Cabañas
We address the problem of integrating data from multiple, possibly biased, observational and interventional studies, to eventually compute counterfactuals in structural causal models.
no code implementations • 1 Sep 2022 • Enrique Miranda, Marco Zaffalon
We show how Allais paradox finds a solution in the new theory, and discuss the role of sets of probabilities in the theory.
1 code implementation • 26 Jul 2022 • Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas, David Huber, Dario Azzimonti
Causal analysis may be affected by selection bias, which is defined as the systematic exclusion of data from a certain subpopulation.
no code implementations • 17 Dec 2021 • Manuel Schürch, Dario Azzimonti, Alessio Benavoli, Marco Zaffalon
Gaussian processes (GPs) are an important tool in machine learning and statistics with applications ranging from social and natural science through engineering.
no code implementations • 27 May 2021 • Juerg Kohlas, Arianna Casanova, Marco Zaffalon
In a recent work we have shown how to construct an information algebra of coherent sets of gambles defined on general possibility spaces.
no code implementations • 25 May 2021 • Juerg Kohlas, Arianna Casanova, Marco Zaffalon
In this paper, we show that coherent sets of gambles can be embedded into the algebraic structure of information algebra.
no code implementations • 26 Feb 2021 • Arianna Casanova, Juerg Kohlas, Marco Zaffalon
In this paper, we show that coherent sets of gambles and coherent lower and upper previsions can be embedded into the algebraic structure of information algebra.
1 code implementation • 4 Nov 2020 • Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas
Structural causal models are the basic modelling unit in Pearl's causal theory; in principle they allow us to solve counterfactuals, which are at the top rung of the ladder of causation.
1 code implementation • 17 Sep 2020 • Giorgio Corani, Alessio Benavoli, Marco Zaffalon
Automatic forecasting is the task of receiving a time series and returning a forecast for the next time steps without any human intervention.
1 code implementation • 2 Aug 2020 • Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas
A structural causal model is made of endogenous (manifest) and exogenous (latent) variables.
no code implementations • 13 Jul 2020 • Dario Azzimonti, Manuel Schürch, Alessio Benavoli, Marco Zaffalon
Sparse inducing points have long been a standard method to fit Gaussian processes to big data.
1 code implementation • 28 May 2019 • Manuel Schürch, Dario Azzimonti, Alessio Benavoli, Marco Zaffalon
Gaussian Processes (GPs) are powerful kernelized methods for non-parameteric regression used in many applications.
no code implementations • 7 Feb 2018 • Mauro Scanagatta, Giorgio Corani, Marco Zaffalon, Jaemin Yoo, U Kang
We present a novel anytime algorithm (k-MAX) method for this task, which scales up to thousands of variables.
no code implementations • 19 Jul 2017 • Cassio P. de Campos, Mauro Scanagatta, Giorgio Corani, Marco Zaffalon
For decomposable score-based structure learning of Bayesian networks, existing approaches first compute a collection of candidate parent sets for each variable and then optimize over this collection by choosing one parent set for each variable without creating directed cycles while maximizing the total score.
no code implementations • NeurIPS 2016 • Mauro Scanagatta, Giorgio Corani, Cassio P. de Campos, Marco Zaffalon
We present a method for learning treewidth-bounded Bayesian networks from data sets containing thousands of variables.
1 code implementation • 28 Sep 2016 • Giorgio Corani, Alessio Benavoli, Janez Demšar, Francesca Mangili, Marco Zaffalon
Usually one compares the accuracy of two competing classifiers via null hypothesis significance tests (nhst).
1 code implementation • 14 Jun 2016 • Alessio Benavoli, Giorgio Corani, Janez Demsar, Marco Zaffalon
The machine learning community adopted the use of null hypothesis significance testing (NHST) in order to ensure the statistical validity of results.
no code implementations • 11 May 2016 • Mauro Scanagatta, Giorgio Corani, Cassio P. de Campos, Marco Zaffalon
We present a method for learning treewidth-bounded Bayesian networks from data sets containing thousands of variables.
no code implementations • 7 Jan 2016 • Alessio Benavoli, Marco Zaffalon
The state space (SS) representation of Gaussian processes (GP) has recently gained a lot of interest.
no code implementations • NeurIPS 2015 • Mauro Scanagatta, Cassio P. de Campos, Giorgio Corani, Marco Zaffalon
We present a method for learning Bayesian networks from data sets containingthousands of variables without the need for structure constraints.
no code implementations • 1 Jun 2015 • Marco Zaffalon, Enrique Miranda
On this basis, we obtain new results and insights: in particular, we show that the theory of incomplete preferences can be developed assuming only the existence of a worst act---no best act is needed---, and that a weakened Archimedean axiom suffices too; this axiom allows us also to address some controversy about the regularity assumption (that probabilities should be positive---they need not), which enables us also to deal with uncountable possibility spaces; we show that it is always possible to extend in a minimal way a preference relation to one with a worst act, and yet the resulting relation is never Archimedean, except in a trivial case; we show that the traditional notion of state independence coincides with the notion called strong independence in imprecise probability---this leads us to give much a weaker definition of state independence than the traditional one; we rework and uniform the notions of complete preferences, beliefs, values; we argue that Archimedeanity does not capture all the problems that can be modelled with sets of expected utilities and we provide a new notion that does precisely that.
no code implementations • 7 Aug 2014 • Marco Zaffalon, Marcus Hutter
Mutual information is widely used in artificial intelligence, in a descriptive way, to measure the stochastic dependence of discrete random variables.
no code implementations • 7 Aug 2014 • Gert de Cooman, Marco Zaffalon
This is a fundamental problem, and of particular interest for Bayesian networks.
no code implementations • 15 Jan 2014 • Marco Zaffalon, Enrique Miranda
In this paper we formulate the problem of inference under incomplete information in very general terms.