Ondrej Kuzelka

Publication Activity (10 Years)

Years Active: 2008-2024
Publications (10 Years): 75

Top Topics

Hypothesis Space

Relational Learning

Markov Logic Networks

Top Venues

Publications

Lorenzo Cascioli, Laurens Devos, Ondrej Kuzelka, Jesse Davis
Faster Repeated Evasion Attacks in Tree Ensembles. CoRR (2024)
Yuanhong Wang, Juhua Pu, Yuyi Wang, Ondrej Kuzelka
On Exact Sampling in the Two-Variable Fragment of First-Order Logic. LICS (2023)
Yuanhong Wang, Juhua Pu, Yuyi Wang, Ondrej Kuzelka
Lifted Algorithms for Symmetric Weighted First-Order Model Sampling. CoRR (2023)
Jan Tóth, Ondrej Kuzelka
Lifted Inference with Linear Order Axiom. AAAI (2023)
Timothy van Bremen, Ondrej Kuzelka
Lifted inference with tree axioms. Artif. Intell. 324 (2023)
Nitesh Kumar, Ondrej Kuzelka, Luc De Raedt
First-Order Context-Specific Likelihood Weighting in Hybrid Probabilistic Logic Programs. J. Artif. Intell. Res. 77 (2023)
Martin Svatos, Peter Jung, Jan Tóth, Yuyi Wang, Ondrej Kuzelka
On Discovering Interesting Combinatorial Integer Sequences. CoRR (2023)
Martin Svatos, Peter Jung, Jan Tóth, Yuyi Wang, Ondrej Kuzelka
On Discovering Interesting Combinatorial Integer Sequences. IJCAI (2023)
Yuanhong Wang, Juhua Pu, Yuyi Wang, Ondrej Kuzelka
On Exact Sampling in the Two-Variable Fragment of First-Order Logic. CoRR (2023)
Ondrej Kuzelka
Counting and Sampling Models in First-Order Logic. IJCAI (2023)
Nitesh Kumar, Ondrej Kuzelka, Luc De Raedt
Learning Distributional Programs for Relational Autocompletion. Theory Pract. Log. Program. 22 (1) (2022)
Jan Tóth, Ondrej Kuzelka
Lifted Inference with Linear Order Axiom. CoRR (2022)
Yuanhong Wang, Timothy van Bremen, Yuyi Wang, Ondrej Kuzelka
Domain-Lifted Sampling for Universal Two-Variable Logic and Extensions. AAAI (2022)
Nitesh Kumar, Ondrej Kuzelka, Luc De Raedt
First-Order Context-Specific Likelihood Weighting in Hybrid Probabilistic Logic Programs. CoRR (2022)
Yuanhong Wang, Timothy van Bremen, Juhua Pu, Yuyi Wang, Ondrej Kuzelka
Fast Algorithms for Relational Marginal Polytopes. IJCAI (2021)
Timothy van Bremen, Ondrej Kuzelka
Faster lifting for two-variable logic using cell graphs. UAI (2021)
Nitesh Kumar, Ondrej Kuzelka
Context-Specific Likelihood Weighting. AISTATS (2021)
Jáchym Barvínek, Timothy van Bremen, Yuyi Wang, Filip Zelezný, Ondrej Kuzelka
Automatic Conjecturing of P-Recursions Using Lifted Inference. ILP (2021)
Gustav Sourek, Filip Zelezný, Ondrej Kuzelka
Lossless Compression of Structured Convolutional Models via Lifting. ICLR (2021)
Timothy van Bremen, Ondrej Kuzelka
Lifted Inference with Tree Axioms. KR (2021)
Giuseppe Marra, Ondrej Kuzelka
Neural markov logic networks. UAI (2021)
Ondrej Kuzelka
Weighted First-Order Model Counting in the Two-Variable Fragment With Counting Quantifiers. J. Artif. Intell. Res. 70 (2021)
Gustav Sourek, Filip Zelezný, Ondrej Kuzelka
Beyond graph neural networks with lifted relational neural networks. Mach. Learn. 110 (7) (2021)
Nitesh Kumar, Ondrej Kuzelka
Context-Specific Likelihood Weighting. CoRR (2021)
Gustav Sourek, Filip Zelezný, Ondrej Kuzelka
Beyond Graph Neural Networks with Lifted Relational Neural Networks. CoRR (2020)
Gustav Sourek, Filip Zelezný, Ondrej Kuzelka
Learning with Molecules beyond Graph Neural Networks. CoRR (2020)
Timothy van Bremen, Ondrej Kuzelka
Approximate Weighted First-Order Model Counting: Exploiting Fast Approximate Model Counters and Symmetry. CoRR (2020)
Martin Svatos, Steven Schockaert, Jesse Davis, Ondrej Kuzelka
STRiKE: Rule-Driven Relational Learning Using Stratified k-Entailment. ECAI (2020)
Ondrej Kuzelka, Yuyi Wang
Domain-Liftability of Relational Marginal Polytopes. CoRR (2020)
Ondrej Kuzelka
Lifted Inference in 2-Variable Markov Logic Networks with Function and Cardinality Constraints Using Discrete Fourier Transform. CoRR (2020)
Ondrej Kuzelka
Markov Logic Networks with Complex Weights: Expressivity, Liftability and Fourier Transforms. CoRR (2020)
Ondrej Kuzelka
Weighted First-Order Model Counting in the Two-Variable Fragment With Counting Quantifiers. CoRR (2020)
Ondrej Kuzelka
Complex Markov Logic Networks: Expressivity and Liftability. UAI (2020)
Nitesh Kumar, Ondrej Kuzelka, Luc De Raedt
Learning Distributional Programs for Relational Autocompletion. CoRR (2020)
Ondrej Kuzelka, Vyacheslav Kungurtsev, Yuyi Wang
Lifted Weight Learning of Markov Logic Networks (Revisited One More Time). PGM (2020)
Timothy van Bremen, Ondrej Kuzelka
Approximate Weighted First-Order Model Counting: Exploiting Fast Approximate Model Counters and Symmetry. IJCAI (2020)
Ondrej Kuzelka, Yuyi Wang
Domain-Liftability of Relational Marginal Polytopes. AISTATS (2020)
Giuseppe Marra, Ondrej Kuzelka
Neural Markov Logic Networks. CoRR (2019)
Ondrej Kuzelka, Vyacheslav Kungurtsev
Lifted Weight Learning of Markov Logic Networks Revisited. AISTATS (2019)
Ondrej Kuzelka, Vyacheslav Kungurtsev
Lifted Weight Learning of Markov Logic Networks Revisited. CoRR (2019)
Gustav Sourek, Filip Zelezný, Ondrej Kuzelka
Scaling up relational templated neural models. NeSy@IJCAI (2019)
Arcchit Jain, Tal Friedman, Ondrej Kuzelka, Guy Van den Broeck, Luc De Raedt
Scalable Rule Learning in Probabilistic Knowledge Bases. AKBC (2019)
Ondrej Kuzelka, Jesse Davis
Markov Logic Networks for Knowledge Base Completion: A Theoretical Analysis Under the MCAR Assumption. UAI (2019)
Ondrej Kuzelka, Yuyi Wang, Jesse Davis, Steven Schockaert
PAC-Reasoning in Relational Domains. UAI (2018)
Víctor Gutiérrez-Basulto, Jean Christoph Jung, Ondrej Kuzelka
Markov Logic Networks with Statistical Quantifiers. CoRR (2018)
Ondrej Kuzelka, Yuyi Wang, Steven Schockaert
VC-Dimension Based Generalization Bounds for Relational Learning. ECML/PKDD (2) (2018)
Ondrej Kuzelka, Yuyi Wang, Steven Schockaert
VC-Dimension Based Generalization Bounds for Relational Learning. CoRR (2018)
Víctor Gutiérrez-Basulto, Jean Christoph Jung, Ondrej Kuzelka
Quantified Markov Logic Networks. KR (2018)
Ondrej Kuzelka, Yuyi Wang, Jesse Davis, Steven Schockaert
Relational Marginal Problems: Theory and Estimation. AAAI (2018)
Gustav Sourek, Vojtech Aschenbrenner, Filip Zelezný, Steven Schockaert, Ondrej Kuzelka
Lifted Relational Neural Networks: Efficient Learning of Latent Relational Structures. J. Artif. Intell. Res. 62 (2018)
Ondrej Kuzelka, Yuyi Wang, Jesse Davis, Steven Schockaert
PAC-Reasoning in Relational Domains. CoRR (2018)
Thomas Ager, Ondrej Kuzelka, Steven Schockaert
Modelling Salient Features as Directions in Fine-Tuned Semantic Spaces. CoNLL (2018)
Ondrej Kuzelka, Jesse Davis, Steven Schockaert
Induction of Interpretable Possibilistic Logic Theories from Relational Data. CoRR (2017)
Ondrej Kuzelka, Yuyi Wang, Jesse Davis, Steven Schockaert
Relational Marginal Problems: Theory and Estimation. CoRR (2017)
Gustav Sourek, Martin Svatos, Filip Zelezný, Steven Schockaert, Ondrej Kuzelka
Stacked Structure Learning for Lifted Relational Neural Networks. CoRR (2017)
Ondrej Kuzelka, Jesse Davis, Steven Schockaert
Induction of Interpretable Possibilistic Logic Theories from Relational Data. IJCAI (2017)
Martin Svatos, Gustav Sourek, Filip Zelezný, Steven Schockaert, Ondrej Kuzelka
Pruning Hypothesis Spaces Using Learned Domain Theories. ILP (2017)
Gustav Sourek, Martin Svatos, Filip Zelezný, Steven Schockaert, Ondrej Kuzelka
Stacked Structure Learning for Lifted Relational Neural Networks. ILP (2017)
Ondrej Kuzelka, Jesse Davis, Steven Schockaert
Learning Possibilistic Logic Theories from Default Rules. IJCAI (2016)
Ondrej Kuzelka, Jesse Davis, Steven Schockaert
Learning Possibilistic Logic Theories from Default Rules. CoRR (2016)
Thomas Ager, Ondrej Kuzelka, Steven Schockaert
Inducing Symbolic Rules from Entity Embeddings using Auto-encoders. NeSy@HLAI (2016)
Gustav Sourek, Suresh Manandhar, Filip Zelezný, Steven Schockaert, Ondrej Kuzelka
Learning Predictive Categories Using Lifted Relational Neural Networks. ILP (2016)
Ondrej Kuzelka, Yuyi Wang, Jan Ramon
Bounds for Learning from Evolutionary-Related Data in the Realizable Case. IJCAI (2016)
Radomír Cernoch, Ondrej Kuzelka, Filip Zelezný
Polynomial and Extensible Solutions in Lock-Chart Solving. Appl. Artif. Intell. 30 (10) (2016)
Ondrej Kuzelka, Jesse Davis, Steven Schockaert
Stratified Knowledge Bases as Interpretable Probabilistic Models (Extended Abstract). CoRR (2016)
Ondrej Kuzelka, Jesse Davis, Steven Schockaert
Interpretable Encoding of Densities Using Possibilistic Logic. ECAI (2016)
Ondrej Kuzelka, Jesse Davis, Steven Schockaert
Encoding Markov Logic Networks in Possibilistic Logic. CoRR (2015)
Ondrej Kuzelka, Jesse Davis, Steven Schockaert
Encoding Markov logic networks in Possibilistic Logic. UAI (2015)
Ondrej Kuzelka, Jesse Davis, Steven Schockaert
Constructing Markov Logic Networks from First-Order Default Rules. ILP (2015)
Gustav Sourek, Ondrej Kuzelka, Filip Zelezný
Learning to Detect Network Intrusion from a Few Labeled Events and Background Traffic. AIMS (2015)
Gustav Sourek, Vojtech Aschenbrenner, Filip Zelezný, Ondrej Kuzelka
Lifted Relational Neural Networks. CoCo@NIPS (2015)
Ondrej Kuzelka, Jan Ramon
A Note on Restricted Forms of LGG. ILP (Late Breaking Papers) (2015)
Matej Holec, Ondrej Kuzelka, Filip Zelezný
Novel gene sets improve set-level classification of prokaryotic gene expression data. BMC Bioinform. 16 (2015)
Gustav Sourek, Vojtech Aschenbrenner, Filip Zelezný, Ondrej Kuzelka
Lifted Relational Neural Networks. CoRR (2015)
Ondrej Kuzelka, Jan Ramon
Mine 'Em All: A Note on Mining All Graphs. ILP (2015)
Ondrej Kuzelka, Andrea Szabóová, Filip Zelezný
A method for reduction of examples in relational learning. J. Intell. Inf. Syst. 42 (2) (2014)
Gustav Sourek, Ondrej Kuzelka, Filip Zelezný
Predicting Top-k Trends on Twitter using Graphlets and Time Features. ILP (Late Breaking Papers) (2013)
Roman Barták, Radomír Cernoch, Ondrej Kuzelka, Filip Zelezný
Formulating the template ILP consistency problem as a constraint satisfaction problem. Constraints An Int. J. 18 (2) (2013)
Ondrej Kuzelka, Andrea Szabóová, Filip Zelezný
Reducing Examples in Relational Learning with Bounded-Treewidth Hypotheses. NFMCP (2012)
Andrea Szabóová, Ondrej Kuzelka, Filip Zelezný, Jakub Tolar
Prediction of DNA-binding propensity of proteins by the ball-histogram method using automatic template search. BMC Bioinform. 13 (S-10) (2012)
Ondrej Kuzelka, Andrea Szabóová, Filip Zelezný
Bounded Least General Generalization. ILP (2012)
Ondrej Kuzelka, Andrea Szabóová, Filip Zelezný
Extending the ball-histogram method with continuous distributions and an application to prediction of DNA-binding proteins. BIBM (2012)
Ondrej Kuzelka, Andrea Szabóová, Filip Zelezný
Relational Learning with Polynomials. ICTAI (2012)
Andrea Szabóová, Ondrej Kuzelka, Filip Zelezný
Prediction of antimicrobial activity of peptides using relational machine learning. BIBM Workshops (2012)
Andrea Szabóová, Ondrej Kuzelka, Sergio Morales E., Filip Zelezný, Jakub Tolar
Prediction of DNA-Binding Propensity of Proteins by the Ball-Histogram Method. ISBRA (2011)
Ondrej Kuzelka, Andrea Szabóová, Filip Zelezný
Gaussian logic and its applications in bioinformatics. BCB (2011)
Ondrej Kuzelka, Filip Zelezný
Block-wise construction of tree-like relational features with monotone reducibility and redundancy. Mach. Learn. 83 (2) (2011)
Ondrej Kuzelka, Andrea Szabóová, Matej Holec, Filip Zelezný
Gaussian Logic for Predictive Classification. ECML/PKDD (2) (2011)
Ondrej Kuzelka, Filip Zelezný
Seeing the World through Homomorphism: An Experimental Study on Reducibility of Examples. ILP (2010)
Roman Barták, Ondrej Kuzelka, Filip Zelezný
Formulating Template Consistency in Inductive Logic Programming as a Constraint Satisfaction Problem. Abstraction, Reformulation, and Approximation (2010)
Filip Zelezný, Ondrej Kuzelka
Taming the Complexity of Inductive Logic Programming. SOFSEM (2010)
Roman Barták, Ondrej Kuzelka, Filip Zelezný
Using Constraint Satisfaction for Learning Hypotheses in Inductive Logic Programming. FLAIRS Conference (2010)
Ondrej Kuzelka, Filip Zelezný
Block-wise construction of acyclic relational features with monotone irreducibility and relevancy properties. ICML (2009)
Ondrej Kuzelka, Filip Zelezný
Fast estimation of first-order clause coverage through randomization and maximum likelihood. ICML (2008)
Ondrej Kuzelka, Filip Zelezný
A Restarted Strategy for Efficient Subsumption Testing. Fundam. Informaticae 89 (1) (2008)