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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...
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07/17/2023
Efficient Computation of Counterfactual Bounds
We assume to be given structural equations over discrete variables induc...
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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...
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09/01/2022
Nonlinear desirability theory
Desirability can be understood as an extension of Anscombe and Aumann's ...
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07/26/2022
Bounding Counterfactuals under Selection Bias
Causal analysis may be affected by selection bias, which is defined as t...
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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...
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12/17/2021
Correlated Product of Experts for Sparse Gaussian Process Regression
Gaussian processes (GPs) are an important tool in machine learning and s...
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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...
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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...
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02/26/2021
Information algebras of coherent sets of gambles
In this paper, we show that coherent sets of gambles can be embedded int...
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11/04/2020
EM Based Bounding of Unidentifiable Queries in Structural Causal Models
A structural causal model is made of endogenous (manifest) and exogenous...
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09/17/2020
Automatic Forecasting using Gaussian Processes
Automatic forecasting is the task of receiving a time series and returni...
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08/02/2020
Structural Causal Models Are (Solvable by) Credal Networks
A structural causal model is made of endogenous (manifest) and exogenous...
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07/13/2020
Orthogonally Decoupled Variational Fourier Features
Sparse inducing points have long been a standard method to fit Gaussian ...
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06/07/2019
Reconciling Hierarchical Forecasts via Bayes' Rule
When time series are organized into hierarchies, the forecasts have to s...
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05/28/2019
Recursive Estimation for Sparse Gaussian Process Regression
Gaussian Processes (GPs) are powerful kernelized methods for non-paramet...
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02/12/2019
Computational Complexity and the Nature of Quantum Mechanics
Quantum theory (QT) has been confirmed by numerous experiments, yet we s...
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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...
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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...
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07/19/2017
Entropy-based Pruning for Learning Bayesian Networks using BIC
For decomposable score-based structure learning of Bayesian networks, ex...
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09/28/2016
Statistical comparison of classifiers through Bayesian hierarchical modelling
Usually one compares the accuracy of two competing classifiers via null ...
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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...
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01/07/2016
State Space representation of non-stationary Gaussian Processes
The state space (SS) representation of Gaussian processes (GP) has recen...
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06/01/2015
Desirability and the birth of incomplete preferences
We establish an equivalence between two seemingly different theories: on...
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08/07/2014
Updating with incomplete observations
Currently, there is renewed interest in the problem, raised by Shafer in...
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08/07/2014
Robust Feature Selection by Mutual Information Distributions
Mutual information is widely used in artificial intelligence, in a descr...
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01/15/2014
Conservative Inference Rule for Uncertain Reasoning under Incompleteness
In this paper we formulate the problem of inference under incomplete inf...
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10/16/2012
The Complexity of Approximately Solving Influence Diagrams
Influence diagrams allow for intuitive and yet precise description of co...
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09/08/2011
Solving Limited Memory Influence Diagrams
We present a new algorithm for exactly solving decision making problems ...
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08/15/2010
