no code implementations • 13 Feb 2024 • Lorenzo Cascioli, Laurens Devos, Ondřej Kuželka, Jesse Davis
Tree ensembles are one of the most widely used model classes.
no code implementations • 17 Aug 2023 • Yuanhong Wang, Juhua Pu, Yuyi Wang, Ondřej Kuželka
Specifically, we prove the domain-liftability under sampling for the two-variables fragment of first-order logic with counting quantifiers in this paper, by devising an efficient sampling algorithm for this fragment that runs in time polynomial in the domain size.
1 code implementation • 6 Feb 2023 • Yuanhong Wang, Juhua Pu, Yuyi Wang, Ondřej Kuželka
In this paper, we study the sampling problem for first-order logic proposed recently by Wang et al. -- how to efficiently sample a model of a given first-order sentence on a finite domain?
no code implementations • 2 Nov 2022 • Jan Tóth, Ondřej Kuželka
In this paper, we prove that adding a linear order axiom (which forces one of the predicates in $\phi$ to introduce a linear ordering of the domain elements in each model of $\phi$) on top of the counting quantifiers still permits a computation time polynomial in the domain size.
no code implementations • 2 Nov 2022 • Jacopo Guidolin, Vyacheslav Kungurtsev, Ondřej Kuželka
Bayesian methods of sampling from a posterior distribution are becoming increasingly popular due to their ability to precisely display the uncertainty of a model fit.
1 code implementation • 24 Jan 2021 • Nitesh Kumar, Ondřej Kuželka
Sampling is a popular method for approximate inference when exact inference is impractical.
no code implementations • ICLR 2020 • Giuseppe Marra, Ondřej Kuželka
We introduce neural Markov logic networks (NMLNs), a statistical relational learning system that borrows ideas from Markov logic.