Search Results for author:
Found 28 papers, 13 papers with code
Safe Road-Crossing by Autonomous Wheelchairs: a Novel Dataset and its Experimental Evaluation
Safe road-crossing by self-driving vehicles is a crucial problem to address in smart-cities.
A Note on Bayesian Networks with Latent Root Variables
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.
Zero-shot Causal Graph Extrapolation from Text via LLMs
We evaluate the ability of large language models (LLMs) to infer causal relations from natural language.
Tractable Bounding of Counterfactual Queries by Knowledge Compilation
We discuss the problem of bounding partially identifiable queries, such as counterfactuals, in Pearlian structural causal models.
Hierarchical Multi-Agent Reinforcement Learning for Air Combat Maneuvering
In our framework, the decision-making process is divided into two stages of abstraction, where heterogeneous low-level policies control the action of individual units, and a high-level commander policy issues macro commands given the overall mission targets.
Approximating Counterfactual Bounds while Fusing Observational, Biased and Randomised Data Sources
We address the problem of integrating data from multiple, possibly biased, observational and interventional studies, to eventually compute counterfactuals in structural causal models.
Efficient Computation of Counterfactual Bounds
This allows us to compute exact counterfactual bounds via algorithms for credal nets on a subclass of structural causal models.
Learning to Bound Counterfactual Inference from Observational, Biased and Randomised Data
We address the problem of integrating data from multiple, possibly biased, observational and interventional studies, to eventually compute counterfactuals in structural causal models.
Modelling Assessment Rubrics through Bayesian Networks: a Pragmatic Approach
Automatic assessment of learner competencies is a fundamental task in intelligent tutoring systems.
Bounding Counterfactuals under Selection Bias
Causal analysis may be affected by selection bias, which is defined as the systematic exclusion of data from a certain subpopulation.
Belief Revision in Sentential Decision Diagrams
Belief revision is the task of modifying a knowledge base when new information becomes available, while also respecting a number of desirable properties.
ADAPQUEST: A Software for Web-Based Adaptive Questionnaires based on Bayesian Networks
We introduce ADAPQUEST, a software tool written in Java for the development of adaptive questionnaires based on Bayesian networks.
Structural Learning of Probabilistic Sentential Decision Diagrams under Partial Closed-World Assumption
Probabilistic sentential decision diagrams are a class of structured-decomposable probabilistic circuits especially designed to embed logical constraints.
A New Score for Adaptive Tests in Bayesian and Credal Networks
Graphical models, such as Bayesian networks, are used for adaptive tests as they allow to model the uncertainty about the questions and the skills in an explainable fashion, especially when coping with multiple skills.
CREPO: An Open Repository to Benchmark Credal Network Algorithms
Credal networks are a popular class of imprecise probabilistic graphical models obtained as a Bayesian network generalization based on, so-called credal, sets of probability mass functions.
Robust Model Checking with Imprecise Markov Reward Models
However, only extensions based on discrete-time imprecise Markov chains have been considered so far for such a robust approach to model checking.
Logic in Computer Science Logic Probability
Relation Clustering in Narrative Knowledge Graphs
When coping with literary texts such as novels or short stories, the extraction of structured information in the form of a knowledge graph might be hindered by the huge number of possible relations between the entities corresponding to the characters in the novel and the consequent hurdles in gathering supervised information about them.
Causal Expectation-Maximisation
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.
SST-BERT at SemEval-2020 Task 1: Semantic Shift Tracing by Clustering in BERT-based Embedding Spaces
Lexical semantic change detection (also known as semantic shift tracing) is a task of identifying words that have changed their meaning over time.
Tractable Inference in Credal Sentential Decision Diagrams
In this work, we develop the credal sentential decision diagrams, a generalisation of their probabilistic counterpart that allows for replacing the local probabilities with (so-called credal) sets of mass functions.
Structural Causal Models Are (Solvable by) Credal Networks
A structural causal model is made of endogenous (manifest) and exogenous (latent) variables.
Temporal Embeddings and Transformer Models for Narrative Text Understanding
We present two deep learning approaches to narrative text understanding for character relationship modelling.
Approximate MMAP by Marginal Search
We present a heuristic strategy for marginal MAP (MMAP) queries in graphical models.
A Bayesian Approach to Conversational Recommendation Systems
We present a conversational recommendation system based on a Bayesian approach.
Reliable Uncertain Evidence Modeling in Bayesian Networks by Credal Networks
A reliable modeling of uncertain evidence in Bayesian networks based on a set-valued quantification is proposed.
Global Sensitivity Analysis for MAP Inference in Graphical Models
We study the sensitivity of a MAP configuration of a discrete probabilistic graphical model with respect to perturbations of its parameters.
On the Complexity of Strong and Epistemic Credal Networks
In this paper, we study inferential complexity under the concepts of epistemic irrelevance and strong independence.
