Ondrej Kuzelka | DeepAI

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∙ 08/17/2023

Lifted Algorithms for Symmetric Weighted First-Order Model Sampling

Weighted model counting (WMC) is the task of computing the weighted sum ...

0 Yuanhong Wang, et al. ∙

∙ 02/09/2023

On Discovering Interesting Combinatorial Integer Sequences

We study the problem of generating interesting integer sequences with a ...

0 Martin Svatos, et al. ∙

∙ 02/06/2023

On Exact Sampling in the Two-Variable Fragment of First-Order Logic

In this paper, we study the sampling problem for first-order logic propo...

0 Yuanhong Wang, et al. ∙

∙ 11/02/2022

Jump-Diffusion Langevin Dynamics for Multimodal Posterior Sampling

Bayesian methods of sampling from a posterior distribution are becoming ...

0 Jacopo Guidolin, et al. ∙

∙ 11/02/2022

Lifted Inference with Linear Order Axiom

We consider the task of weighted first-order model counting (WFOMC) used...

0 Jan Tóth, et al. ∙

∙ 01/26/2022

First-Order Context-Specific Likelihood Weighting in Hybrid Probabilistic Logic Programs

Statistical relational AI and probabilistic logic programming have so fa...

0 Nitesh Kumar, et al. ∙

∙ 01/24/2021

Context-Specific Likelihood Weighting

Sampling is a popular method for approximate inference when exact infere...

0 Nitesh Kumar, et al. ∙

∙ 11/06/2020

Learning with Molecules beyond Graph Neural Networks

We demonstrate a deep learning framework which is inherently based in th...

0 Gustav Sourek, et al. ∙

∙ 07/13/2020

Beyond Graph Neural Networks with Lifted Relational Neural Networks

We demonstrate a declarative differentiable programming framework based ...

18 Gustav Sourek, et al. ∙

∙ 07/10/2020

Weighted First-Order Model Counting in the Two-Variable Fragment With Counting Quantifiers

It is known due to the work of Van den Broeck et al [KR, 2014] that weig...

1 Ondrej Kuzelka, et al. ∙

∙ 06/04/2020

Lifted Inference in 2-Variable Markov Logic Networks with Function and Cardinality Constraints Using Discrete Fourier Transform

In this paper we show that inference in 2-variable Markov logic networks...

8 Ondrej Kuzelka, et al. ∙

∙ 02/24/2020

Markov Logic Networks with Complex Weights: Expressivity, Liftability and Fourier Transforms

We study expressivity of Markov logic networks (MLNs). We introduce comp...

2 Ondrej Kuzelka, et al. ∙

∙ 01/15/2020

Approximate Weighted First-Order Model Counting: Exploiting Fast Approximate Model Counters and Symmetry

We study the symmetric weighted first-order model counting task and pres...

0 Timothy van Bremen, et al. ∙

∙ 01/15/2020

Domain-Liftability of Relational Marginal Polytopes

We study computational aspects of relational marginal polytopes which ar...

0 Ondrej Kuzelka, et al. ∙

∙ 05/31/2019

Neural Markov Logic Networks

We introduce Neural Markov Logic Networks (NMLNs), a statistical relatio...

0 Giuseppe Marra, et al. ∙

∙ 03/07/2019

Lifted Weight Learning of Markov Logic Networks Revisited

We study lifted weight learning of Markov logic networks. We show that t...

0 Ondrej Kuzelka, et al. ∙

∙ 07/03/2018

Quantified Markov Logic Networks

Markov Logic Networks (MLNs) are well-suited for expressing statistics s...

0 Víctor Gutiérrez-Basulto, et al. ∙

∙ 07/03/2018

Markov Logic Networks with Statistical Quantifiers

Markov Logic Networks (MLNs) are well-suited for expressing statistics s...

0 Víctor Gutiérrez-Basulto, et al. ∙

∙ 04/17/2018

VC-Dimension Based Generalization Bounds for Relational Learning

In many applications of relational learning, the available data can be s...

0 Ondrej Kuzelka, et al. ∙

∙ 03/15/2018

PAC-Reasoning in Relational Domains

We consider the problem of predicting plausible missing facts in relatio...

0 Ondrej Kuzelka, et al. ∙

∙ 10/05/2017

Stacked Structure Learning for Lifted Relational Neural Networks

Lifted Relational Neural Networks (LRNNs) describe relational domains us...

0 Gustav Sourek, et al. ∙

∙ 09/18/2017

Relational Marginal Problems: Theory and Estimation

In the propositional setting, the marginal problem is to find a (maximum...

0 Ondrej Kuzelka, et al. ∙

∙ 05/19/2017

Induction of Interpretable Possibilistic Logic Theories from Relational Data

The field of Statistical Relational Learning (SRL) is concerned with lea...

0 Ondrej Kuzelka, et al. ∙

∙ 11/18/2016

Stratified Knowledge Bases as Interpretable Probabilistic Models (Extended Abstract)

In this paper, we advocate the use of stratified logical theories for re...

0 Ondrej Kuzelka, et al. ∙

∙ 08/20/2015

Lifted Relational Neural Networks

We propose a method combining relational-logic representations with neur...

0 Gustav Sourek, et al. ∙

∙ 06/03/2015

Encoding Markov Logic Networks in Possibilistic Logic

Markov logic uses weighted formulas to compactly encode a probability di...

0 Ondrej Kuzelka, et al. ∙

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