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pro vyhledávání: '"Kuzelka, Ondrej"'
The Weighted First-Order Model Counting Problem (WFOMC) asks to compute the weighted sum of models of a given first-order logic sentence over a given domain. It can be solved in time polynomial in the domain size for sentences from the two-variable f
Externí odkaz:
http://arxiv.org/abs/2407.11877
Autor:
Tóth, Jan, Kuželka, Ondřej
We study the time complexity of the weighted first-order model counting (WFOMC) over the logical language with two variables and counting quantifiers. The problem is known to be solvable in time polynomial in the domain size. However, the degree of t
Externí odkaz:
http://arxiv.org/abs/2404.12905
Tree ensembles are one of the most widely used model classes. However, these models are susceptible to adversarial examples, i.e., slightly perturbed examples that elicit a misprediction. There has been significant research on designing approaches to
Externí odkaz:
http://arxiv.org/abs/2402.08586
Publikováno v:
Artificial Intelligence 331 (2024): 104114
Weighted model counting (WMC) is the task of computing the weighted sum of all satisfying assignments (i.e., models) of a propositional formula. Similarly, weighted model sampling (WMS) aims to randomly generate models with probability proportional t
Externí odkaz:
http://arxiv.org/abs/2308.08828
We study the problem of generating interesting integer sequences with a combinatorial interpretation. For this we introduce a two-step approach. In the first step, we generate first-order logic sentences which define some combinatorial objects, e.g.,
Externí odkaz:
http://arxiv.org/abs/2302.04606
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? We extend their result for the universally-quantified subfrag
Externí odkaz:
http://arxiv.org/abs/2302.02730
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. Classical methods based on iterative random sampling and posterior evaluation such
Externí odkaz:
http://arxiv.org/abs/2211.01774
Autor:
Tóth, Jan, Kuželka, Ondřej
We consider the task of weighted first-order model counting (WFOMC) used for probabilistic inference in the area of statistical relational learning. Given a formula $\phi$, domain size $n$ and a pair of weight functions, what is the weighted sum of a
Externí odkaz:
http://arxiv.org/abs/2211.01164
Statistical relational AI and probabilistic logic programming have so far mostly focused on discrete probabilistic models. The reasons for this is that one needs to provide constructs to succinctly model the independencies in such models, and also pr
Externí odkaz:
http://arxiv.org/abs/2201.11165
Publikováno v:
In International Journal of Approximate Reasoning August 2024 171
