58 citations · 239 across the 47 of their papers we have counts for
10 papers · 1 filter
Explaining Decisions in ML Models: a Parameterized Complexity Analysis (Part I)
Sebastian Ordyniak, Giacomo Paesani, Mateusz Rychlicki +1
This paper presents a comprehensive theoretical investigation into the parameterized complexity of explanation problems in various machine learning (ML) models. Contrary to the pre…
Explaining Decisions in ML Models: a Parameterized Complexity Analysis
Sebastian Ordyniak, Giacomo Paesani, Mateusz Rychlicki +1
This paper presents a comprehensive theoretical investigation into the parameterized complexity of explanation problems in various machine learning (ML) models. Contrary to the pre…
Solving Infinite-Domain CSPs Using the Patchwork Property
Konrad K. Dabrowski, Peter Jonsson, Sebastian Ordyniak +1
The constraint satisfaction problem (CSP) has important applications in computer science and AI. In particular, infinite-domain CSPs have been intensively used in subareas of AI su…
Clique-Width and Directed Width Measures for Answer-Set Programming
Bernhard Bliem, Sebastian Ordyniak, Stefan Woltran
Disjunctive Answer Set Programming (ASP) is a powerful declarative programming paradigm whose main decision problems are located on the second level of the polynomial hierarchy. Id…
Backdoors into Heterogeneous Classes of SAT and CSP
Serge Gaspers, Neeldhara Misra, Sebastian Ordyniak +2
In this paper we extend the classical notion of strong and weak backdoor sets for SAT and CSP by allowing that different instantiations of the backdoor variables result in instance…
Parameterized Complexity Results for Exact Bayesian Network Structure Learning
Sebastian Ordyniak, Stefan Szeider
Bayesian network structure learning is the notoriously difficult problem of discovering a Bayesian network that optimally represents a given set of training data. In this paper we…