89 citations · 247 across the 22 of their papers we have counts for
23 papers
dPASP: A Comprehensive Differentiable Probabilistic Answer Set Programming Environment For Neurosymbolic Learning and Reasoning
Renato Lui Geh, Jonas Gonçalves, Igor Cataneo Silveira +2
We present dPASP, a novel declarative probabilistic logic programming framework for differentiable neuro-symbolic reasoning. The framework allows for the specification of discrete…
Markov Conditions and Factorization in Logical Credal Networks
Fabio Gagliardi Cozman
We examine the recently proposed language of Logical Credal Networks, in particular investigating the consequences of various Markov conditions. We introduce the notion of structur…
mRAT-SQL+GAP:A Portuguese Text-to-SQL Transformer
Marcelo Archanjo José, Fabio Gagliardi Cozman
The translation of natural language questions to SQL queries has attracted growing attention, in particular in connection with transformers and similar language models. A large num…
DEEPAGÉ: Answering Questions in Portuguese about the Brazilian Environment
Flávio Nakasato Cação, Marcos Menon José, André Seidel Oliveira +3
The challenge of climate change and biome conservation is one of the most pressing issues of our time - particularly in Brazil, where key environmental reserves are located. Given…
Why should I not follow you? Reasons For and Reasons Against in Responsible Recommender Systems
Gustavo Padilha Polleti, Douglas Luan de Souza, Fabio Cozman
A few Recommender Systems (RS) resort to explanations so as to enhance trust in recommendations. However, current techniques for explanation generation tend to strongly uphold the…
Measuring Unfairness through Game-Theoretic Interpretability
Juliana Cesaro, Fabio G. Cozman
One often finds in the literature connections between measures of fairness and measures of feature importance employed to interpret trained classifiers. However, there seems to be…
A Fully Attention-Based Information Retriever
Alvaro Henrique Chaim Correia, Jorge Luiz Moreira Silva, Thiago de Castro Martins +1
Recurrent neural networks are now the state-of-the-art in natural language processing because they can build rich contextual representations and process texts of arbitrary length.…
Interpreting Embedding Models of Knowledge Bases: A Pedagogical Approach
Arthur Colombini Gusmão, Alvaro Henrique Chaim Correia, Glauber De Bona +1
Knowledge bases are employed in a variety of applications from natural language processing to semantic web search; alas, in practice their usefulness is hurt by their incompletenes…
Speeding-up ProbLog's Parameter Learning
Francisco H. O. V. de Faria, Arthur C. Gusmão, Fabio G. Cozman +1
ProbLog is a state-of-art combination of logic programming and probabilities; in particular ProbLog offers parameter learning through a variant of the EM algorithm. However, the re…
On the Semantics and Complexity of Probabilistic Logic Programs
Fabio Gagliardi Cozman, Denis Deratani Mauá
We examine the meaning and the complexity of probabilistic logic programs that consist of a set of rules and a set of independent probabilistic facts (that is, programs based on Sa…
The Complexity of Bayesian Networks Specified by Propositional and Relational Languages
Fabio Gagliardi Cozman, Denis Deratani Mauá
We examine the complexity of inference in Bayesian networks specified by logical languages. We consider representations that range from fragments of propositional logic to function…
First-Order Bayesian Network Specifications Capture the Complexity Class PP
Fabio Gagliardi Cozman
The point of this note is to prove that a language is in the complexity class PP if and only if the strings of the language encode valid inferences in a Bayesian network defined us…
Quasi-Bayesian Strategies for Efficient Plan Generation: Application to the Planning to Observe Problem
Fabio Gagliardi Cozman, Eric Krotkov
Quasi-Bayesian theory uses convex sets of probability distributions and expected loss to represent preferences about plans. The theory focuses on decision robustness, i.e., the ext…
Robustness Analysis of Bayesian Networks with Local Convex Sets of Distributions
Fabio Gagliardi Cozman
Robust Bayesian inference is the calculation of posterior probability bounds given perturbations in a probabilistic model. This paper focuses on perturbations that can be expressed…
Irrelevance and Independence Relations in Quasi-Bayesian Networks
Fabio Gagliardi Cozman
This paper analyzes irrelevance and independence relations in graphical models associated with convex sets of probability distributions (called Quasi-Bayesian networks). The basic…
Separation Properties of Sets of Probability Measures
Fabio Gagliardi Cozman
This paper analyzes independence concepts for sets of probability measures associated with directed acyclic graphs. The paper shows that epistemic independence and the standard Mar…
Inference with Seperately Specified Sets of Probabilities in Credal Networks
Jose Carlos Ferreira da Rocha, Fabio Gagliardi Cozman
We present new algorithms for inference in credal networks --- directed acyclic graphs associated with sets of probabilities. Credal networks are here interpreted as encoding stron…
Inference in Polytrees with Sets of Probabilities
Jose Carlos Ferreira da Rocha, Fabio Gagliardi Cozman, Cassio Polpo de Campos
Inferences in directed acyclic graphs associated with probability sets and probability intervals are NP-hard, even for polytrees. In this paper we focus on such inferences, and pro…
Propositional and Relational Bayesian Networks Associated with Imprecise and Qualitative Probabilistic Assesments
Fabio Gagliardi Cozman, Cassio Polpo de Campos, Jaime Ide +1
This paper investigates a representation language with flexibility inspired by probabilistic logic and compactness inspired by relational Bayesian networks. The goal is to handle p…
Belief Updating and Learning in Semi-Qualitative Probabilistic Networks
Cassio Polpo de Campos, Fabio Gagliardi Cozman
This paper explores semi-qualitative probabilistic networks (SQPNs) that combine numeric and qualitative information. We first show that exact inferences with SQPNs are NPPP-Comple…
Complexity Analysis and Variational Inference for Interpretation-based Probabilistic Description Logic
Fabio Gagliardi Cozman, Rodrigo Bellizia Polastro
This paper presents complexity analysis and variational methods for inference in probabilistic description logics featuring Boolean operators, quantification, qualified number rest…
Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence (2011)
Fabio Cozman, Avi Pfeffer
This is the Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence, which was held in Barcelona, Spain, July 14 - 17 2011.
Concentration Inequalities and Laws of Large Numbers under Epistemic Irrelevance
Fabio Gagliardi Cozman
This paper presents concentration inequalities and laws of large numbers under weak assumptions of irrelevance, expressed through lower and upper expectations. The results are vari…