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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.

no code implementations • 4 Nov 2020 • Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas

Structural causal models are the fundamental modelling unit in Pearl's causal theory; in principle they allow us to solve any causal inference query, such as causal effects or counterfactuals.

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 • Gert de Cooman, Marco Zaffalon

This is a fundamental problem, and of particular interest for Bayesian networks.

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 • 15 Jan 2014 • Marco Zaffalon, Enrique Miranda

In this paper we formulate the problem of inference under incomplete information in very general terms.

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