activity
20082026
most citedParameterized Complexity Results for Exact Bayesian Network Structure Learning

58 citations · 239 across the 47 of their papers we have counts for

collaborators

64 papers

cs.DM2026

Structural Parameterizations for Eternal Vertex Cover

Neeldhara Misra, Sebastian Ordyniak, Giacomo Paesani +1

Eternal Vertex Cover (EVC) is a turn-based attacker-defender game on an undirected graph . To begin with, the defender places guards on vertices of . The attacker, on the…

cs.DS2026

On the Parameterized Complexity of Min-Sum-Radii

Pankaj Kumar, Haiko Müller, Sebastian Ordyniak +1

In the Min-Sum-Radii (MSR) clustering problem, we are given a finite set X of n points in a metric space. The objective is to find at most k clusters centered at a subset of these…

cs.CC2026

Clausal Deletion Backdoors for QBF: a Parameterized Complexity Approach

Leif Eriksson, Victor Lagerkvist, Sebastian Ordyniak +3

Determining the validity of a quantified Boolean formula (QBF) is a PSPACE-complete problem with rich expressive power. Despite interest in efficient solvers, there is, compared to…

cs.DS2026

Backdoors for Quantified Boolean Formulas

Leif Eriksson, Victor Lagerkvist, Sebastian Ordyniak +3

The quantified Boolean formula problem (QBF) is a well-known PSpace-complete problem with rich expressive power, and is generally viewed as the SAT analogue for PSpace. Given that…

cs.DS2026

Optimal FPT-Approximability for Modular Linear Equations

Konrad K. Dabrowski, Peter Jonsson, Sebastian Ordyniak +2

We show optimal FPT-approximability results for solving almost satisfiable systems of modular linear equations, completing the picture of the parameterized complexity and FPT-appro…

cs.AI2025

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…