Ondřej Kuželka

发表

Hoeffding and Bernstein inequalities for U-statistics without replacement

Yuyi Wang, Jianhang Ai, Ondřej Kuželka, 2022, Statistics & Probability Letters.

Domain-Lifted Sampling for Universal Two-Variable Logic and Extensions

Timothy van Bremen, Yuyi Wang, Yuanhong Wang, 2022, AAAI.

Lifted Inference with Linear Order Axiom

Ondřej Kuželka, J. T'oth, 2022, Proceedings of the AAAI Conference on Artificial Intelligence.

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

L. D. Raedt, Nitesh Kumar, Ondřej Kuželka, 2022, J. Artif. Intell. Res..

Learning Distributional Programs for Relational Autocompletion pdf

Nitesh Kumar, Luc De Raedt, Ondrej Kuzelka, 2020, Theory Pract. Log. Program..

Beyond graph neural networks with lifted relational neural networks pdf

Gustav Sourek, Filip Zelezny, Ondrej Kuzelka, 2020, Machine Learning.

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

Ondrej Kuzelka, Ondřej Kuželka, 2020, J. Artif. Intell. Res..

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

Ondrej Kuzelka, Ondřej Kuželka, 2020, ArXiv.

Complex Markov Logic Networks: Expressivity and Liftability

Ondrej Kuzelka, Ondřej Kuželka, 2020, UAI.

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

Ondrej Kuzelka, Ondřej Kuželka, 2020, ArXiv.

Lifted Inference in 2-Variable Markov Logic Networks with Function and Cardinality Constraints Using DFT

Ondrej Kuzelka, Ondřej Kuželka, 2020 .

Lifted Weight Learning of Markov Logic Networks Revisited pdf

Vyacheslav Kungurtsev, Ondrej Kuzelka, V. Kungurtsev, 2019, AISTATS.

Relational Marginal Problems: Theory and Estimation

Steven Schockaert, Jesse Davis, Ondrej Kuzelka, 2017, AAAI.

Shrinking Covariance Matrices using Biological Background Knowledge

F. Zelezný, Ondrej Kuzelka, Ondřej Kuželka, 2010 .

Lifted Relational Neural Networks: Efficient Learning of Latent Relational Structures

Steven Schockaert, Ondrej Kuzelka, Filip Zelezný, 2018, J. Artif. Intell. Res..

Reducing Examples in Relational Learning with Bounded-Treewidth Hypotheses

Ondrej Kuzelka, Filip Zelezný, Andrea Szabóová, 2012, NFMCP.

Block-wise construction of tree-like relational features with monotone reducibility and redundancy

Ondrej Kuzelka, Filip Zelezný, F. Zelezný, 2011, Machine Learning.

Taming the Complexity of Inductive Logic Programming

Ondrej Kuzelka, Filip Zelezný, F. Zelezný, 2009, SOFSEM.

Fast Construction of Relational Features for Machine Learning

Ondřej Kuželka, Ondřej Kuželka, 2013 .

Gaussian Logic for Predictive Classification

Ondrej Kuzelka, Filip Zelezný, Matej Holec, 2011, ECML/PKDD.

Generalization Bounds for Knowledge Graph Embedding (Trained by Maximum Likelihood)

Ondrej Kuzelka, Ondřej Kuželka, 2019 .

Markov Logic Networks for Knowledge Base Completion: A Theoretical Analysis Under the MCAR Assumption

Jesse Davis, Ondrej Kuzelka, Jesse Davis, 2019, UAI.

Constructing Markov Logic Networks from First-Order Default Rules

Steven Schockaert, Jesse Davis, Ondrej Kuzelka, 2015, ILP.

Neural Markov Logic Networks pdf

Ondrej Kuzelka, Giuseppe Marra, G. Marra, 2019, UAI.

Stacked Structure Learning for Lifted Relational Neural Networks

Steven Schockaert, Ondrej Kuzelka, Filip Zelezný, 2017, ILP.

STRiKE: Rule-Driven Relational Learning Using Stratified k-Entailment

Steven Schockaert, Jesse Davis, Ondrej Kuzelka, 2020, ECAI.

Induction of Interpretable Possibilistic Logic Theories from Relational Data

Steven Schockaert, Jesse Davis, Ondrej Kuzelka, 2017, IJCAI.

Formulating the template ILP consistency problem as a constraint satisfaction problem

Roman Barták, Ondrej Kuzelka, Filip Zelezný, 2013, Constraints.

Using Constraint Satisfaction for Learning Hypotheses in Inductive Logic Programming

Roman Barták, Ondrej Kuzelka, Filip Zelezný, 2010, FLAIRS.

Formulating Template Consistency in Inductive Logic Programming as a Constraint Satisfaction Problem

Roman Barták, Ondrej Kuzelka, Filip Zelezný, 2010, Abstraction, Reformulation, and Approximation.

Prediction of DNA-binding proteins from relational features

Filip Železný, Jakub Tolar, Andrea Szabóová, 2012, Proteome Science.

Cultural influences on personalized e-learning systems

Scotty D. Craig, Kenneth A. Kaufman, Susanne P. Lajoie, 2012 .

Context-Specific Likelihood Weighting

Ondrej Kuzelka, Nitesh Kumar, Nitesh Kumar, 2021, AISTATS.

Prediction of antimicrobial activity of peptides using relational machine learning

Ondrej Kuzelka, Filip Zelezný, Andrea Szabóová, 2012, 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops.

Learning with Molecules beyond Graph Neural Networks pdf

Gustav Sourek, Filip Zelezny, Ondrej Kuzelka, 2020, ArXiv.

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

Yuyi Wang, Yuanhong Wang, Juhua Pu, 2023, 2023 38th Annual ACM/IEEE Symposium on Logic in Computer Science (LICS).

Lifted Inference with Tree Axioms

Ondrej Kuzelka, Timothy van Bremen, Ondřej Kuželka, 2021, KR.

Faster lifting for two-variable logic using cell graphs

Timothy van Bremen, Ondřej Kuželka, Ondřej Kuželka, 2021, UAI.

VC-Dimension Based Generalization Bounds for Relational Learning

Steven Schockaert, Ondrej Kuzelka, Yuyi Wang, 2018, ECML/PKDD.

Lifted Relational Neural Networks pdf

Ondrej Kuzelka, Filip Zelezný, Gustav Sourek, 2015, CoCo@NIPS.

Learning Possibilistic Logic Theories from Default Rules pdf

Steven Schockaert, Jesse Davis, Ondrej Kuzelka, 2016, IJCAI.

Scalable Rule Learning in Probabilistic Knowledge Bases

Luc De Raedt, Guy Van den Broeck, Ondrej Kuzelka, 2019, AKBC.

Domain-Liftability of Relational Marginal Polytopes pdf

Ondrej Kuzelka, Yuyi Wang, Yuyi Wang, 2020, AISTATS.

Quantified Markov Logic Networks

Jean Christoph Jung, Ondrej Kuzelka, Víctor Gutiérrez-Basulto, 2018, KR.

Markov Logic Networks with Statistical Quantifiers pdf

Jean Christoph Jung, Ondrej Kuzelka, Víctor Gutiérrez-Basulto, 2018, ArXiv.

A I ] 2 5 A pr 2 01 8 Relational Marginal Problems : Theory and Estimation

Ondrej Kuzelka, Ondřej Kuželka, 2018 .

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

Ondrej Kuzelka, Timothy van Bremen, Ondřej Kuželka, 2020, IJCAI.

Modelling Salient Features as Directions in Fine-Tuned Semantic Spaces

Steven Schockaert, Ondrej Kuzelka, Thomas Ager, 2018, CoNLL.

A method for reduction of examples in relational learning

Ondrej Kuzelka, Filip Zelezný, Andrea Szabóová, 2014, Journal of Intelligent Information Systems.

PAC-Reasoning in Relational Domains

S. Schockaert, Jesse Davis, Yuyi Wang, 2018, UAI.

Lifted Weight Learning of Markov Logic Networks (Revisited One More Time)

V. Kungurtsev, Yuyi Wang, Ondřej Kuželka, 2020, PGM.

Novel gene sets improve set-level classification of prokaryotic gene expression data

Ondrej Kuzelka, Filip Zelezný, Matej Holec, 2015, BMC Bioinformatics.

Extending the ball-histogram method with continuous distributions and an application to prediction of DNA-binding proteins

Ondrej Kuzelka, Filip Zelezný, Andrea Szabóová, 2012, 2012 IEEE International Conference on Bioinformatics and Biomedicine.

Prediction of DNA-binding propensity of proteins by the ball-histogram method using automatic template search

Ondrej Kuzelka, Filip Zelezný, Jakub Tolar, 2011, BMC Bioinformatics.

Mining Frequent Spatial Docking Patterns in Zinc Finger-DNA Complexes

F. Zelezný, J. Tolar, Ondrej Kuzelka, 2010 .

Learning Predictive Categories Using Lifted Relational Neural Networks

Steven Schockaert, Suresh Manandhar, Ondrej Kuzelka, 2016, ILP.