39 citations · 102 across the 36 of their papers we have counts for
23 papers · 1 filter
CombEval: A Framework for Evaluating Combinatorial Counting in Large Language Models
Yuxu Zhou, Ondřej Kuželka, Yuyi Wang +2
We present CombEval, a dynamic benchmark for evaluating combinatorial counting in large language models. CombEval represents each problem as a typed Cofola specification over entit…
Solving Combinatorial Counting Problems with Weighted First-Order Model Counting
Yuanhong Wang, Juhua Pu, Yuxu Zhou +2
Combinatorial counting problems pervade artificial intelligence, statistics, and discrete mathematics. Whether the task is enumerating subsets, multisets, permutations, partitions,…
Faster Lifting for Ordered Domains with Predecessor Relations
Kuncheng Zou, Jiahao Mai, Yonggang Zhang +4
We investigate lifted inference on ordered domains with predecessor relations, where the elements of the domain respect a total (cyclic) order, and every element has a distinct (cl…
Lifted Algorithms for Symmetric Weighted First-Order Model Sampling
Yuanhong Wang, Juhua Pu, Yuyi Wang +1
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…
On Exact Sampling in the Two-Variable Fragment of First-Order Logic
Yuanhong Wang, Juhua Pu, Yuyi Wang +1
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…
Lifted Inference with Linear Order Axiom
Jan Tóth, Ondřej Kuželka
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 , domain siz…
First-Order Context-Specific Likelihood Weighting in Hybrid Probabilistic Logic Programs
Nitesh Kumar, Ondrej Kuzelka, Luc De Raedt
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 constru…
Context-Specific Likelihood Weighting
Nitesh Kumar, Ondřej Kuželka
Sampling is a popular method for approximate inference when exact inference is impractical. Generally, sampling algorithms do not exploit context-specific independence (CSI) proper…
Weighted First-Order Model Counting in the Two-Variable Fragment With Counting Quantifiers
Ondrej Kuzelka
It is known due to the work of Van den Broeck et al [KR, 2014] that weighted first-order model counting (WFOMC) in the two-variable fragment of first-order logic can be solved in t…
Lifted Inference in 2-Variable Markov Logic Networks with Function and Cardinality Constraints Using Discrete Fourier Transform
Ondrej Kuzelka
In this paper we show that inference in 2-variable Markov logic networks (MLNs) with cardinality and function constraints is domain-liftable. To obtain this result we use existing…
Complex Markov Logic Networks: Expressivity and Liftability
Ondrej Kuzelka
We study expressivity of Markov logic networks (MLNs). We introduce complex MLNs, which use complex-valued weights, and we show that, unlike standard MLNs with real-valued weights,…
Approximate Weighted First-Order Model Counting: Exploiting Fast Approximate Model Counters and Symmetry
Timothy van Bremen, Ondrej Kuzelka
We study the symmetric weighted first-order model counting task and present ApproxWFOMC, a novel anytime method for efficiently bounding the weighted first-order model count in the…
Domain-Liftability of Relational Marginal Polytopes
Ondrej Kuzelka, Yuyi Wang
We study computational aspects of relational marginal polytopes which are statistical relational learning counterparts of marginal polytopes, well-known from probabilistic graphica…
Learning Distributional Programs for Relational Autocompletion
Kumar Nitesh, Kuzelka Ondrej, De Raedt Luc
Relational autocompletion is the problem of automatically filling out some missing values in multi-relational data. We tackle this problem within the probabilistic logic programmin…
Lifted Weight Learning of Markov Logic Networks Revisited
Ondrej Kuzelka, Vyacheslav Kungurtsev
We study lifted weight learning of Markov logic networks. We show that there is an algorithm for maximum-likelihood learning of 2-variable Markov logic networks which runs in time…
Quantified Markov Logic Networks
Víctor Gutiérrez-Basulto, Jean Christoph Jung, Ondrej Kuzelka
Markov Logic Networks (MLNs) are well-suited for expressing statistics such as "with high probability a smoker knows another smoker" but not for expressing statements such as "ther…
PAC-Reasoning in Relational Domains
Ondrej Kuzelka, Yuyi Wang, Jesse Davis +1
We consider the problem of predicting plausible missing facts in relational data, given a set of imperfect logical rules. In particular, our aim is to provide bounds on the (expect…
Relational Marginal Problems: Theory and Estimation
Ondrej Kuzelka, Yuyi Wang, Jesse Davis +1
In the propositional setting, the marginal problem is to find a (maximum-entropy) distribution that has some given marginals. We study this problem in a relational setting and make…
Induction of Interpretable Possibilistic Logic Theories from Relational Data
Ondrej Kuzelka, Jesse Davis, Steven Schockaert
The field of Statistical Relational Learning (SRL) is concerned with learning probabilistic models from relational data. Learned SRL models are typically represented using some kin…
Stratified Knowledge Bases as Interpretable Probabilistic Models (Extended Abstract)
Ondrej Kuzelka, Jesse Davis, Steven Schockaert
In this paper, we advocate the use of stratified logical theories for representing probabilistic models. We argue that such encodings can be more interpretable than those obtained…
Learning Possibilistic Logic Theories from Default Rules
Ondrej Kuzelka, Jesse Davis, Steven Schockaert
We introduce a setting for learning possibilistic logic theories from defaults of the form "if alpha then typically beta". We first analyse this problem from the point of view of m…
Lifted Relational Neural Networks
Gustav Sourek, Vojtech Aschenbrenner, Filip Zelezny +1
We propose a method combining relational-logic representations with neural network learning. A general lifted architecture, possibly reflecting some background domain knowledge, is…
Encoding Markov Logic Networks in Possibilistic Logic
Ondrej Kuzelka, Jesse Davis, Steven Schockaert
Markov logic uses weighted formulas to compactly encode a probability distribution over possible worlds. Despite the use of logical formulas, Markov logic networks (MLNs) can be di…