Ondrej Kuzelka

112 papers A* 20A 13B 12Misc 1Journal 48Unranked 17

Year	Rank	Type	Title / Venue / Authors
2026	B	`conf`	Bridging Weighted First Order Model Counting and Graph Polynomials. CSL Qipeng Kuang, Ondrej Kuzelka, Yuanhong Wang, Yuyi Wang
2026	A*	`conf`	Tractable Weighted First-Order Model Counting with Bounded Treewidth Binary Evidence. AAAI Václav Kula, Qipeng Kuang, Yuyi Wang, Yuanhong Wang, Ondrej Kuzelka
2025	A	`conf`	Faster Lifting for Ordered Domains with Predecessor Relations. ECAI Kuncheng Zou, Jiahao Mai, Yonggang Zhang, Yuyi Wang, Ondrej Kuzelka, Yuanhong Wang, Yi Chang
2025	J	`jnl`	Faster Lifting for Ordered Domains with Predecessor Relations. CoRR Kuncheng Zou, Jiahao Mai, Yonggang Zhang, Yuyi Wang, Ondrej Kuzelka, Yuanhong Wang, Yi Chang
2025	A*	`conf`	Model Enumeration of Two-Variable Logic with Quadratic Delay Complexity. LICS Qiaolan Meng, Juhua Pu, Hongting Niu, Yuyi Wang, Yuanhong Wang, Ondrej Kuzelka
2025	J	`jnl`	Model Enumeration of Two-Variable Logic with Quadratic Delay Complexity. CoRR Qiaolan Meng, Juhua Pu, Hongting Niu, Yuyi Wang, Yuanhong Wang, Ondrej Kuzelka
2025	J	`jnl`	Tractable Weighted First-Order Model Counting with Bounded Treewidth Binary Evidence. CoRR Václav Kula, Qipeng Kuang, Yuyi Wang, Yuanhong Wang, Ondrej Kuzelka
2025	J	`jnl`	Weighted First Order Model Counting for Two-variable Logic with Axioms on Two Relations. CoRR Qipeng Kuang, Václav Kula, Ondrej Kuzelka, Yuanhong Wang, Yuyi Wang
2024	A	`conf`	A More Practical Algorithm for Weighted First-Order Model Counting with Linear Order Axiom. ECAI Qiaolan Meng, Jan Tóth, Yuanhong Wang, Yuyi Wang, Ondrej Kuzelka
2024	J	`jnl`	Bridging Weighted First Order Model Counting and Graph Polynomials. CoRR Qipeng Kuang, Ondrej Kuzelka, Yuanhong Wang, Yuyi Wang
2024	A*	`conf`	Complexity of Weighted First-Order Model Counting in the Two-Variable Fragment with Counting Quantifiers: A Bound to Beat. KR Jan Tóth, Ondrej Kuzelka
2024	J	`jnl`	Complexity of Weighted First-Order Model Counting in the Two-Variable Fragment with Counting Quantifiers: A Bound to Beat. CoRR Jan Tóth, Ondrej Kuzelka
2024	A*	`conf`	Faster Repeated Evasion Attacks in Tree Ensembles. NeurIPS Lorenzo Cascioli, Laurens Devos, Ondrej Kuzelka, Jesse Davis
2024	J	`jnl`	Faster Repeated Evasion Attacks in Tree Ensembles. CoRR Lorenzo Cascioli, Laurens Devos, Ondrej Kuzelka, Jesse Davis
2024	J	`jnl`	Lifted algorithms for symmetric weighted first-order model sampling. Artif. Intell. Yuanhong Wang, Juhua Pu, Yuyi Wang, Ondrej Kuzelka
2024	—	`conf`	Model Counting and Sampling in First-Order Logic (Abstract of Invited Talk). Description Logics Ondrej Kuzelka
2024	J	`jnl`	Quantified neural Markov logic networks. Int. J. Approx. Reason. Peter Jung, Giuseppe Marra, Ondrej Kuzelka
2023	A*	`conf`	Counting and Sampling Models in First-Order Logic. IJCAI Ondrej Kuzelka
2023	J	`jnl`	First-Order Context-Specific Likelihood Weighting in Hybrid Probabilistic Logic Programs. J. Artif. Intell. Res. Nitesh Kumar, Ondrej Kuzelka, Luc De Raedt
2023	J	`jnl`	Lifted Algorithms for Symmetric Weighted First-Order Model Sampling. CoRR Yuanhong Wang, Juhua Pu, Yuyi Wang, Ondrej Kuzelka
2023	A*	`conf`	Lifted Inference with Linear Order Axiom. AAAI Jan Tóth, Ondrej Kuzelka
2023	—	`ch.`	Lifted Relational Neural Networks: From Graphs to Deep Relational Learning. Compendium of Neurosymbolic Artificial Intelligence Gustav Sír, Filip Zelezný, Ondrej Kuzelka
2023	J	`jnl`	Lifted inference with tree axioms. Artif. Intell. Timothy van Bremen, Ondrej Kuzelka
2023	A*	`conf`	On Discovering Interesting Combinatorial Integer Sequences. IJCAI Martin Svatos, Peter Jung, Jan Tóth, Yuyi Wang, Ondrej Kuzelka
2023	J	`jnl`	On Discovering Interesting Combinatorial Integer Sequences. CoRR Martin Svatos, Peter Jung, Jan Tóth, Yuyi Wang, Ondrej Kuzelka
2023	A*	`conf`	On Exact Sampling in the Two-Variable Fragment of First-Order Logic. LICS Yuanhong Wang, Juhua Pu, Yuyi Wang, Ondrej Kuzelka
2023	J	`jnl`	On Exact Sampling in the Two-Variable Fragment of First-Order Logic. CoRR Yuanhong Wang, Juhua Pu, Yuyi Wang, Ondrej Kuzelka
2022	A*	`conf`	Domain-Lifted Sampling for Universal Two-Variable Logic and Extensions. AAAI Yuanhong Wang, Timothy van Bremen, Yuyi Wang, Ondrej Kuzelka
2022	J	`jnl`	First-Order Context-Specific Likelihood Weighting in Hybrid Probabilistic Logic Programs. CoRR Nitesh Kumar, Ondrej Kuzelka, Luc De Raedt
2022	J	`jnl`	Learning Distributional Programs for Relational Autocompletion. Theory Pract. Log. Program. Nitesh Kumar, Ondrej Kuzelka, Luc De Raedt
2022	J	`jnl`	Lifted Inference with Linear Order Axiom. CoRR Jan Tóth, Ondrej Kuzelka
2021	B	`conf`	Automatic Conjecturing of P-Recursions Using Lifted Inference. ILP Jáchym Barvínek, Timothy van Bremen, Yuyi Wang, Filip Zelezný, Ondrej Kuzelka
2021	J	`jnl`	Beyond graph neural networks with lifted relational neural networks. Mach. Learn. Gustav Sourek, Filip Zelezný, Ondrej Kuzelka
2021	A	`conf`	Context-Specific Likelihood Weighting. AISTATS Nitesh Kumar, Ondrej Kuzelka
2021	J	`jnl`	Context-Specific Likelihood Weighting. CoRR Nitesh Kumar, Ondrej Kuzelka
2021	A*	`conf`	Fast Algorithms for Relational Marginal Polytopes. IJCAI Yuanhong Wang, Timothy van Bremen, Juhua Pu, Yuyi Wang, Ondrej Kuzelka
2021	A	`conf`	Faster lifting for two-variable logic using cell graphs. UAI Timothy van Bremen, Ondrej Kuzelka
2021	A*	`conf`	Lifted Inference with Tree Axioms. KR Timothy van Bremen, Ondrej Kuzelka
2021	A*	`conf`	Lossless Compression of Structured Convolutional Models via Lifting. ICLR Gustav Sourek, Filip Zelezný, Ondrej Kuzelka
2021	A	`conf`	Neural markov logic networks. UAI Giuseppe Marra, Ondrej Kuzelka
2021	J	`jnl`	Weighted First-Order Model Counting in the Two-Variable Fragment With Counting Quantifiers. J. Artif. Intell. Res. Ondrej Kuzelka
2020	A*	`conf`	Approximate Weighted First-Order Model Counting: Exploiting Fast Approximate Model Counters and Symmetry. IJCAI Timothy van Bremen, Ondrej Kuzelka
2020	J	`jnl`	Approximate Weighted First-Order Model Counting: Exploiting Fast Approximate Model Counters and Symmetry. CoRR Timothy van Bremen, Ondrej Kuzelka
2020	J	`jnl`	Beyond Graph Neural Networks with Lifted Relational Neural Networks. CoRR Gustav Sourek, Filip Zelezný, Ondrej Kuzelka
2020	A	`conf`	Complex Markov Logic Networks: Expressivity and Liftability. UAI Ondrej Kuzelka
2020	A	`conf`	Domain-Liftability of Relational Marginal Polytopes. AISTATS Ondrej Kuzelka, Yuyi Wang
2020	J	`jnl`	Domain-Liftability of Relational Marginal Polytopes. CoRR Ondrej Kuzelka, Yuyi Wang
2020	J	`jnl`	Learning Distributional Programs for Relational Autocompletion. CoRR Nitesh Kumar, Ondrej Kuzelka, Luc De Raedt
2020	J	`jnl`	Learning with Molecules beyond Graph Neural Networks. CoRR Gustav Sourek, Filip Zelezný, Ondrej Kuzelka
2020	J	`jnl`	Lifted Inference in 2-Variable Markov Logic Networks with Function and Cardinality Constraints Using Discrete Fourier Transform. CoRR Ondrej Kuzelka
2020	—	`conf`	Lifted Weight Learning of Markov Logic Networks (Revisited One More Time). PGM Ondrej Kuzelka, Vyacheslav Kungurtsev, Yuyi Wang
2020	J	`jnl`	Markov Logic Networks with Complex Weights: Expressivity, Liftability and Fourier Transforms. CoRR Ondrej Kuzelka
2020	A	`conf`	STRiKE: Rule-Driven Relational Learning Using Stratified k-Entailment. ECAI Martin Svatos, Steven Schockaert, Jesse Davis, Ondrej Kuzelka
2020	J	`jnl`	Weighted First-Order Model Counting in the Two-Variable Fragment With Counting Quantifiers. CoRR Ondrej Kuzelka
2019	A	`conf`	Lifted Weight Learning of Markov Logic Networks Revisited. AISTATS Ondrej Kuzelka, Vyacheslav Kungurtsev
2019	J	`jnl`	Lifted Weight Learning of Markov Logic Networks Revisited. CoRR Ondrej Kuzelka, Vyacheslav Kungurtsev
2019	A	`conf`	Markov Logic Networks for Knowledge Base Completion: A Theoretical Analysis Under the MCAR Assumption. UAI Ondrej Kuzelka, Jesse Davis
2019	J	`jnl`	Neural Markov Logic Networks. CoRR Giuseppe Marra, Ondrej Kuzelka
2019	—	`conf`	Scalable Rule Learning in Probabilistic Knowledge Bases. AKBC Arcchit Jain, Tal Friedman, Ondrej Kuzelka, Guy Van den Broeck, Luc De Raedt
2019	—	`conf`	Scaling up relational templated neural models. NeSy@IJCAI Gustav Sourek, Filip Zelezný, Ondrej Kuzelka
2018	J	`jnl`	Lifted Relational Neural Networks: Efficient Learning of Latent Relational Structures. J. Artif. Intell. Res. Gustav Sourek, Vojtech Aschenbrenner, Filip Zelezný, Steven Schockaert, Ondrej Kuzelka
2018	J	`jnl`	Markov Logic Networks with Statistical Quantifiers. CoRR Víctor Gutiérrez-Basulto, Jean Christoph Jung, Ondrej Kuzelka
2018	B	`conf`	Modelling Salient Features as Directions in Fine-Tuned Semantic Spaces. CoNLL Thomas Ager, Ondrej Kuzelka, Steven Schockaert
2018	A	`conf`	PAC-Reasoning in Relational Domains. UAI Ondrej Kuzelka, Yuyi Wang, Jesse Davis, Steven Schockaert
2018	J	`jnl`	PAC-Reasoning in Relational Domains. CoRR Ondrej Kuzelka, Yuyi Wang, Jesse Davis, Steven Schockaert
2018	A*	`conf`	Quantified Markov Logic Networks. KR Víctor Gutiérrez-Basulto, Jean Christoph Jung, Ondrej Kuzelka
2018	A*	`conf`	Relational Marginal Problems: Theory and Estimation. AAAI Ondrej Kuzelka, Yuyi Wang, Jesse Davis, Steven Schockaert
2018	—	`conf`	VC-Dimension Based Generalization Bounds for Relational Learning. ECML/PKDD (2) Ondrej Kuzelka, Yuyi Wang, Steven Schockaert
2018	J	`jnl`	VC-Dimension Based Generalization Bounds for Relational Learning. CoRR Ondrej Kuzelka, Yuyi Wang, Steven Schockaert
2017	A*	`conf`	Induction of Interpretable Possibilistic Logic Theories from Relational Data. IJCAI Ondrej Kuzelka, Jesse Davis, Steven Schockaert
2017	J	`jnl`	Induction of Interpretable Possibilistic Logic Theories from Relational Data. CoRR Ondrej Kuzelka, Jesse Davis, Steven Schockaert
2017	B	`conf`	Pruning Hypothesis Spaces Using Learned Domain Theories. ILP Martin Svatos, Gustav Sourek, Filip Zelezný, Steven Schockaert, Ondrej Kuzelka
2017	J	`jnl`	Relational Marginal Problems: Theory and Estimation. CoRR Ondrej Kuzelka, Yuyi Wang, Jesse Davis, Steven Schockaert
2017	B	`conf`	Stacked Structure Learning for Lifted Relational Neural Networks. ILP Gustav Sourek, Martin Svatos, Filip Zelezný, Steven Schockaert, Ondrej Kuzelka
2017	J	`jnl`	Stacked Structure Learning for Lifted Relational Neural Networks. CoRR Gustav Sourek, Martin Svatos, Filip Zelezný, Steven Schockaert, Ondrej Kuzelka
2016	A*	`conf`	Bounds for Learning from Evolutionary-Related Data in the Realizable Case. IJCAI Ondrej Kuzelka, Yuyi Wang, Jan Ramon
2016	—	`conf`	Inducing Symbolic Rules from Entity Embeddings using Auto-encoders. NeSy@HLAI Thomas Ager, Ondrej Kuzelka, Steven Schockaert
2016	A	`conf`	Interpretable Encoding of Densities Using Possibilistic Logic. ECAI Ondrej Kuzelka, Jesse Davis, Steven Schockaert
2016	A*	`conf`	Learning Possibilistic Logic Theories from Default Rules. IJCAI Ondrej Kuzelka, Jesse Davis, Steven Schockaert
2016	J	`jnl`	Learning Possibilistic Logic Theories from Default Rules. CoRR Ondrej Kuzelka, Jesse Davis, Steven Schockaert
2016	B	`conf`	Learning Predictive Categories Using Lifted Relational Neural Networks. ILP Gustav Sourek, Suresh Manandhar, Filip Zelezný, Steven Schockaert, Ondrej Kuzelka
2016	J	`jnl`	Polynomial and Extensible Solutions in Lock-Chart Solving. Appl. Artif. Intell. Radomír Cernoch, Ondrej Kuzelka, Filip Zelezný
2016	J	`jnl`	Stratified Knowledge Bases as Interpretable Probabilistic Models (Extended Abstract). CoRR Ondrej Kuzelka, Jesse Davis, Steven Schockaert
2015	—	`conf`	A Note on Restricted Forms of LGG. ILP (Late Breaking Papers) Ondrej Kuzelka, Jan Ramon
2015	B	`conf`	Constructing Markov Logic Networks from First-Order Default Rules. ILP Ondrej Kuzelka, Jesse Davis, Steven Schockaert
2015	J	`jnl`	Encoding Markov Logic Networks in Possibilistic Logic. CoRR Ondrej Kuzelka, Jesse Davis, Steven Schockaert
2015	A	`conf`	Encoding Markov logic networks in Possibilistic Logic. UAI Ondrej Kuzelka, Jesse Davis, Steven Schockaert
2015	—	`conf`	Learning to Detect Network Intrusion from a Few Labeled Events and Background Traffic. AIMS Gustav Sourek, Ondrej Kuzelka, Filip Zelezný
2015	—	`conf`	Lifted Relational Neural Networks. CoCo@NIPS Gustav Sourek, Vojtech Aschenbrenner, Filip Zelezný, Ondrej Kuzelka
2015	J	`jnl`	Lifted Relational Neural Networks. CoRR Gustav Sourek, Vojtech Aschenbrenner, Filip Zelezný, Ondrej Kuzelka
2015	B	`conf`	Mine 'Em All: A Note on Mining All Graphs. ILP Ondrej Kuzelka, Jan Ramon
2015	J	`jnl`	Novel gene sets improve set-level classification of prokaryotic gene expression data. BMC Bioinform. Matej Holec, Ondrej Kuzelka, Filip Zelezný
2014	J	`jnl`	A method for reduction of examples in relational learning. J. Intell. Inf. Syst. Ondrej Kuzelka, Andrea Szabóová, Filip Zelezný
2013	J	`jnl`	Formulating the template ILP consistency problem as a constraint satisfaction problem. Constraints An Int. J. Roman Barták, Radomír Cernoch, Ondrej Kuzelka, Filip Zelezný
2013	—	`conf`	Predicting Top-k Trends on Twitter using Graphlets and Time Features. ILP (Late Breaking Papers) Gustav Sourek, Ondrej Kuzelka, Filip Zelezný
2012	B	`conf`	Bounded Least General Generalization. ILP Ondrej Kuzelka, Andrea Szabóová, Filip Zelezný
2012	—	`conf`	Extending the ball-histogram method with continuous distributions and an application to prediction of DNA-binding proteins. BIBM Ondrej Kuzelka, Andrea Szabóová, Filip Zelezný
2012	J	`jnl`	Prediction of DNA-binding propensity of proteins by the ball-histogram method using automatic template search. BMC Bioinform. Andrea Szabóová, Ondrej Kuzelka, Filip Zelezný, Jakub Tolar
2012	—	`conf`	Prediction of antimicrobial activity of peptides using relational machine learning. BIBM Workshops Andrea Szabóová, Ondrej Kuzelka, Filip Zelezný
2012	—	`conf`	Reducing Examples in Relational Learning with Bounded-Treewidth Hypotheses. NFMCP Ondrej Kuzelka, Andrea Szabóová, Filip Zelezný
2012	B	`conf`	Relational Learning with Polynomials. ICTAI Ondrej Kuzelka, Andrea Szabóová, Filip Zelezný
2011	J	`jnl`	Block-wise construction of tree-like relational features with monotone reducibility and redundancy. Mach. Learn. Ondrej Kuzelka, Filip Zelezný
2011	—	`conf`	Gaussian Logic for Predictive Classification. ECML/PKDD (2) Ondrej Kuzelka, Andrea Szabóová, Matej Holec, Filip Zelezný
2011	—	`conf`	Gaussian logic and its applications in bioinformatics. BCB Ondrej Kuzelka, Andrea Szabóová, Filip Zelezný
2011	—	`conf`	Prediction of DNA-Binding Propensity of Proteins by the Ball-Histogram Method. ISBRA Andrea Szabóová, Ondrej Kuzelka, Sergio Morales E., Filip Zelezný, Jakub Tolar
2010	—	`conf`	Formulating Template Consistency in Inductive Logic Programming as a Constraint Satisfaction Problem. Abstraction, Reformulation, and Approximation Roman Barták, Ondrej Kuzelka, Filip Zelezný
2010	B	`conf`	Seeing the World through Homomorphism: An Experimental Study on Reducibility of Examples. ILP Ondrej Kuzelka, Filip Zelezný
2010	B	`conf`	Taming the Complexity of Inductive Logic Programming. SOFSEM Filip Zelezný, Ondrej Kuzelka
2010	Misc	`conf`	Using Constraint Satisfaction for Learning Hypotheses in Inductive Logic Programming. FLAIRS Roman Barták, Ondrej Kuzelka, Filip Zelezný
2009	A*	`conf`	Block-wise construction of acyclic relational features with monotone irreducibility and relevancy properties. ICML Ondrej Kuzelka, Filip Zelezný
2008	J	`jnl`	A Restarted Strategy for Efficient Subsumption Testing. Fundam. Informaticae Ondrej Kuzelka, Filip Zelezný
2008	A*	`conf`	Fast estimation of first-order clause coverage through randomization and maximum likelihood. ICML Ondrej Kuzelka, Filip Zelezný