
Structural Causal Models Are (Solvable by) Credal Networks
A structural causal model is made of endogenous (manifest) and exogenous...
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Orthogonally Decoupled Variational Fourier Features
Sparse inducing points have long been a standard method to fit Gaussian ...
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Reconciling Hierarchical Forecasts via Bayes' Rule
When time series are organized into hierarchies, the forecasts have to s...
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Recursive Estimation for Sparse Gaussian Process Regression
Gaussian Processes (GPs) are powerful kernelized methods for nonparamet...
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Computational Complexity and the Nature of Quantum Mechanics
Quantum theory (QT) has been confirmed by numerous experiments, yet we s...
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Computational Complexity and the Nature of Quantum Mechanics (Extended version)
Quantum theory (QT) has been confirmed by numerous experiments, yet we s...
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Efficient Learning of BoundedTreewidth Bayesian Networks from Complete and Incomplete Data Sets
Learning a Bayesian networks with bounded treewidth is important for red...
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Entropybased Pruning for Learning Bayesian Networks using BIC
For decomposable scorebased structure learning of Bayesian networks, ex...
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Statistical comparison of classifiers through Bayesian hierarchical modelling
Usually one compares the accuracy of two competing classifiers via null ...
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Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis
The machine learning community adopted the use of null hypothesis signif...
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State Space representation of nonstationary Gaussian Processes
The state space (SS) representation of Gaussian processes (GP) has recen...
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Desirability and the birth of incomplete preferences
We establish an equivalence between two seemingly different theories: on...
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Updating with incomplete observations
Currently, there is renewed interest in the problem, raised by Shafer in...
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Robust Feature Selection by Mutual Information Distributions
Mutual information is widely used in artificial intelligence, in a descr...
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Conservative Inference Rule for Uncertain Reasoning under Incompleteness
In this paper we formulate the problem of inference under incomplete inf...
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The Complexity of Approximately Solving Influence Diagrams
Influence diagrams allow for intuitive and yet precise description of co...
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Solving Limited Memory Influence Diagrams
We present a new algorithm for exactly solving decision making problems ...
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Epistemic irrelevance in credal nets: the case of imprecise Markov trees
We focus on credal nets, which are graphical models that generalise Baye...
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Marco Zaffalon
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Professor at SUPSI  Dalle Molle Institute for Artificial Intelligence( IDSIA )