Marco Zaffalon

research

∙ 07/31/2023

Approximating Counterfactual Bounds while Fusing Observational, Biased and Randomised Data Sources

We address the problem of integrating data from multiple, possibly biase...

0 Marco Zaffalon, et al. ∙

research

∙ 07/17/2023

Efficient Computation of Counterfactual Bounds

We assume to be given structural equations over discrete variables induc...

0 Marco Zaffalon, et al. ∙

research

∙ 12/06/2022

Learning to Bound Counterfactual Inference in Structural Causal Models from Observational and Randomised Data

We address the problem of integrating data from multiple observational a...

0 Marco Zaffalon, et al. ∙

research

∙ 09/01/2022

Nonlinear desirability theory

Desirability can be understood as an extension of Anscombe and Aumann's ...

0 Enrique Miranda, et al. ∙

research

∙ 07/26/2022

Bounding Counterfactuals under Selection Bias

Causal analysis may be affected by selection bias, which is defined as t...

0 Marco Zaffalon, et al. ∙

research

∙ 03/08/2022

Why we should interpret density matrices as moment matrices: the case of (in)distinguishable particles and the emergence of classical reality

We introduce a formulation of quantum theory (QT) as a general probabili...

0 Alessio Benavoli, et al. ∙

research

∙ 12/17/2021

Correlated Product of Experts for Sparse Gaussian Process Regression

Gaussian processes (GPs) are an important tool in machine learning and s...

0 Manuel Schürch, et al. ∙

research

∙ 05/27/2021

Algebras of Sets and Coherent Sets of Gambles

In a recent work we have shown how to construct an information algebra o...

0 Juerg Kohlas, et al. ∙

research

∙ 05/25/2021

Information algebras of coherent sets of gambles in general possibility spaces

In this paper, we show that coherent sets of gambles can be embedded int...

0 Juerg Kohlas, et al. ∙

research

∙ 02/26/2021

Information algebras of coherent sets of gambles

In this paper, we show that coherent sets of gambles can be embedded int...

0 Juerg Kohlas, et al. ∙

research

∙ 11/04/2020

EM Based Bounding of Unidentifiable Queries in Structural Causal Models

A structural causal model is made of endogenous (manifest) and exogenous...

0 Marco Zaffalon, et al. ∙

research

∙ 09/17/2020

Automatic Forecasting using Gaussian Processes

Automatic forecasting is the task of receiving a time series and returni...

0 Giorgio Corani, et al. ∙

research

∙ 08/02/2020

Structural Causal Models Are (Solvable by) Credal Networks

A structural causal model is made of endogenous (manifest) and exogenous...

0 Marco Zaffalon, et al. ∙

research

∙ 07/13/2020

Orthogonally Decoupled Variational Fourier Features

Sparse inducing points have long been a standard method to fit Gaussian ...

0 Dario Azzimonti, et al. ∙

research

∙ 06/07/2019

Reconciling Hierarchical Forecasts via Bayes' Rule

When time series are organized into hierarchies, the forecasts have to s...

0 Giorgio Corani, et al. ∙

research

∙ 05/28/2019

Recursive Estimation for Sparse Gaussian Process Regression

Gaussian Processes (GPs) are powerful kernelized methods for non-paramet...

0 Manuel Schürch, et al. ∙

research

∙ 02/12/2019

Computational Complexity and the Nature of Quantum Mechanics

Quantum theory (QT) has been confirmed by numerous experiments, yet we s...

0 Alessio Benavoli, et al. ∙

research

∙ 02/09/2019

Computational Complexity and the Nature of Quantum Mechanics (Extended version)

Quantum theory (QT) has been confirmed by numerous experiments, yet we s...

0 Alessio Benavoli, et al. ∙

research

∙ 02/07/2018

Efficient Learning of Bounded-Treewidth Bayesian Networks from Complete and Incomplete Data Sets

Learning a Bayesian networks with bounded treewidth is important for red...

0 Mauro Scanagatta, et al. ∙

research

∙ 07/19/2017

Entropy-based Pruning for Learning Bayesian Networks using BIC

For decomposable score-based structure learning of Bayesian networks, ex...

0 Cassio P. de Campos, et al. ∙

research

∙ 09/28/2016

Statistical comparison of classifiers through Bayesian hierarchical modelling

Usually one compares the accuracy of two competing classifiers via null ...

0 Giorgio Corani, et al. ∙

research

∙ 06/14/2016

Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis

The machine learning community adopted the use of null hypothesis signif...

0 Alessio Benavoli, et al. ∙

research

∙ 01/07/2016

State Space representation of non-stationary Gaussian Processes

The state space (SS) representation of Gaussian processes (GP) has recen...

0 Alessio Benavoli, et al. ∙

research

∙ 06/01/2015

Desirability and the birth of incomplete preferences

We establish an equivalence between two seemingly different theories: on...

0 Marco Zaffalon, et al. ∙

research

∙ 08/07/2014

Updating with incomplete observations

Currently, there is renewed interest in the problem, raised by Shafer in...

0 Gert de Cooman, et al. ∙

research

∙ 08/07/2014

Robust Feature Selection by Mutual Information Distributions

Mutual information is widely used in artificial intelligence, in a descr...

0 Marco Zaffalon, et al. ∙

research

∙ 01/15/2014

Conservative Inference Rule for Uncertain Reasoning under Incompleteness

In this paper we formulate the problem of inference under incomplete inf...

0 Marco Zaffalon, et al. ∙

research

∙ 10/16/2012

The Complexity of Approximately Solving Influence Diagrams

Influence diagrams allow for intuitive and yet precise description of co...

0 Denis D. Mauá, et al. ∙

research

∙ 09/08/2011

Solving Limited Memory Influence Diagrams

We present a new algorithm for exactly solving decision making problems ...

0 Denis Deratani Mauá, et al. ∙

research

∙ 08/15/2010

Epistemic irrelevance in credal nets: the case of imprecise Markov trees

We focus on credal nets, which are graphical models that generalise Baye...

0 Gert de Cooman, et al. ∙

Marco Zaffalon

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