Fast estimation of first-order clause coverage through randomization and maximum likelihood. - Ondrej Kuzelka; Filip Zelezný (ICML, 2008)

A Restarted Strategy for Efficient Subsumption Testing. - Ondrej Kuzelka; Filip Zelezný (Fundam. Informaticae, 2008)

Block-wise construction of acyclic relational features with monotone irreducibility and relevancy properties. - Ondrej Kuzelka; Filip Zelezný (ICML, 2009)

Formulating Template Consistency in Inductive Logic Programming as a Constraint Satisfaction Problem. - Roman Barták; Ondrej Kuzelka; Filip Zelezný (Abstraction, Reformulation, and Approximation, 2010)

Using Constraint Satisfaction for Learning Hypotheses in Inductive Logic Programming. - Roman Barták; Ondrej Kuzelka; Filip Zelezný (FLAIRS, 2010)

Seeing the World through Homomorphism: An Experimental Study on Reducibility of Examples. - Ondrej Kuzelka; Filip Zelezný (ILP, 2010)

Taming the Complexity of Inductive Logic Programming. - Filip Zelezný; Ondrej Kuzelka (SOFSEM, 2010)

Gaussian logic and its applications in bioinformatics. - Ondrej Kuzelka; Andrea Szabóová; Filip Zelezný (BCB, 2011)

Prediction of DNA-Binding Propensity of Proteins by the Ball-Histogram Method. - Andrea Szabóová; Ondrej Kuzelka; Sergio Morales E.; Filip Zelezný; Jakub Tolar (ISBRA, 2011)

Gaussian Logic for Predictive Classification. - Ondrej Kuzelka; Andrea Szabóová; Matej Holec; Filip Zelezný (ECML/PKDD (2), 2011)

Block-wise construction of tree-like relational features with monotone reducibility and redundancy. - Ondrej Kuzelka; Filip Zelezný (Mach. Learn., 2011)

Extending the ball-histogram method with continuous distributions and an application to prediction of DNA-binding proteins. - Ondrej Kuzelka; Andrea Szabóová; Filip Zelezný (BIBM, 2012)

Prediction of antimicrobial activity of peptides using relational machine learning. - Andrea Szabóová; Ondrej Kuzelka; Filip Zelezný (BIBM Workshops, 2012)

Relational Learning with Polynomials. - Ondrej Kuzelka; Andrea Szabóová; Filip Zelezný (ICTAI, 2012)

Bounded Least General Generalization. - Ondrej Kuzelka; Andrea Szabóová; Filip Zelezný (ILP, 2012)

Reducing Examples in Relational Learning with Bounded-Treewidth Hypotheses. - Ondrej Kuzelka; Andrea Szabóová; Filip Zelezný (NFMCP, 2012)

Prediction of DNA-binding propensity of proteins by the ball-histogram method using automatic template search. - Andrea Szabóová; Ondrej Kuzelka; Filip Zelezný; Jakub Tolar (BMC Bioinform., 2012)

Predicting Top-k Trends on Twitter using Graphlets and Time Features. - Gustav Sourek; Ondrej Kuzelka; Filip Zelezný (ILP (Late Breaking Papers), 2013)

Formulating the template ILP consistency problem as a constraint satisfaction problem. - Roman Barták; Radomír Cernoch; Ondrej Kuzelka; Filip Zelezný (Constraints An Int. J., 2013)

A method for reduction of examples in relational learning. - Ondrej Kuzelka; Andrea Szabóová; Filip Zelezný (J. Intell. Inf. Syst., 2014)

Learning to Detect Network Intrusion from a Few Labeled Events and Background Traffic. - Gustav Sourek; Ondrej Kuzelka; Filip Zelezný (AIMS, 2015)

Constructing Markov Logic Networks from First-Order Default Rules. - Ondrej Kuzelka; Jesse Davis; Steven Schockaert (ILP, 2015)

Mine 'Em All: A Note on Mining All Graphs. - Ondrej Kuzelka; Jan Ramon (ILP, 2015)

A Note on Restricted Forms of LGG. - Ondrej Kuzelka; Jan Ramon (ILP (Late Breaking Papers), 2015)

Lifted Relational Neural Networks. - Gustav Sourek; Vojtech Aschenbrenner; Filip Zelezný; Ondrej Kuzelka (CoCo@NIPS, 2015)

Encoding Markov logic networks in Possibilistic Logic. - Ondrej Kuzelka; Jesse Davis; Steven Schockaert (UAI, 2015)

Novel gene sets improve set-level classification of prokaryotic gene expression data. - Matej Holec; Ondrej Kuzelka; Filip Zelezný (BMC Bioinform., 2015)

Interpretable Encoding of Densities Using Possibilistic Logic. - Ondrej Kuzelka; Jesse Davis; Steven Schockaert (ECAI, 2016)

Learning Possibilistic Logic Theories from Default Rules. - Ondrej Kuzelka; Jesse Davis; Steven Schockaert (IJCAI, 2016)

Bounds for Learning from Evolutionary-Related Data in the Realizable Case. - Ondrej Kuzelka; Yuyi Wang 001; Jan Ramon (IJCAI, 2016)

Learning Predictive Categories Using Lifted Relational Neural Networks. - Gustav Sourek; Suresh Manandhar; Filip Zelezný; Steven Schockaert; Ondrej Kuzelka (ILP, 2016)

Inducing Symbolic Rules from Entity Embeddings using Auto-encoders. - Thomas Ager; Ondrej Kuzelka; Steven Schockaert (NeSy@HLAI, 2016)

Polynomial and Extensible Solutions in Lock-Chart Solving. - Radomír Cernoch; Ondrej Kuzelka; Filip Zelezný (Appl. Artif. Intell., 2016)

Stratified Knowledge Bases as Interpretable Probabilistic Models (Extended Abstract). - Ondrej Kuzelka; Jesse Davis; Steven Schockaert (CoRR, 2016)

Induction of Interpretable Possibilistic Logic Theories from Relational Data. - Ondrej Kuzelka; Jesse Davis; Steven Schockaert (IJCAI, 2017)

Stacked Structure Learning for Lifted Relational Neural Networks. - Gustav Sourek; Martin Svatos; Filip Zelezný; Steven Schockaert; Ondrej Kuzelka (ILP, 2017)

Pruning Hypothesis Spaces Using Learned Domain Theories. - Martin Svatos; Gustav Sourek; Filip Zelezný; Steven Schockaert; Ondrej Kuzelka (ILP, 2017)

Relational Marginal Problems: Theory and Estimation. - Ondrej Kuzelka; Yuyi Wang 001; Jesse Davis; Steven Schockaert (AAAI, 2018)

Modelling Salient Features as Directions in Fine-Tuned Semantic Spaces. - Thomas Ager; Ondrej Kuzelka; Steven Schockaert (CoNLL, 2018)

Quantified Markov Logic Networks. - Víctor Gutiérrez-Basulto; Jean Christoph Jung; Ondrej Kuzelka (KR, 2018)

VC-Dimension Based Generalization Bounds for Relational Learning. - Ondrej Kuzelka; Yuyi Wang 001; Steven Schockaert (ECML/PKDD (2), 2018)

PAC-Reasoning in Relational Domains. - Ondrej Kuzelka; Yuyi Wang 001; Jesse Davis; Steven Schockaert (UAI, 2018)

Markov Logic Networks with Statistical Quantifiers. - Víctor Gutiérrez-Basulto; Jean Christoph Jung; Ondrej Kuzelka (CoRR, 2018)

Lifted Relational Neural Networks: Efficient Learning of Latent Relational Structures. - Gustav Sourek; Vojtech Aschenbrenner; Filip Zelezný; Steven Schockaert; Ondrej Kuzelka (J. Artif. Intell. Res., 2018)

Lifted Weight Learning of Markov Logic Networks Revisited. - Ondrej Kuzelka; Vyacheslav Kungurtsev (AISTATS, 2019)

Scalable Rule Learning in Probabilistic Knowledge Bases. - Arcchit Jain; Tal Friedman; Ondrej Kuzelka; Guy Van den Broeck; Luc De Raedt (AKBC, 2019)

Scaling up relational templated neural models. - Gustav Sourek; Filip Zelezný; Ondrej Kuzelka (NeSy@IJCAI, 2019)

Markov Logic Networks for Knowledge Base Completion: A Theoretical Analysis Under the MCAR Assumption. - Ondrej Kuzelka; Jesse Davis (UAI, 2019)

Domain-Liftability of Relational Marginal Polytopes. - Ondrej Kuzelka; Yuyi Wang 001 (AISTATS, 2020)

STRiKE: Rule-Driven Relational Learning Using Stratified k-Entailment. - Martin Svatos; Steven Schockaert; Jesse Davis; Ondrej Kuzelka (ECAI, 2020)

Approximate Weighted First-Order Model Counting: Exploiting Fast Approximate Model Counters and Symmetry. - Timothy van Bremen; Ondrej Kuzelka (IJCAI, 2020)

Lifted Weight Learning of Markov Logic Networks (Revisited One More Time). - Ondrej Kuzelka; Vyacheslav Kungurtsev; Yuyi Wang 001 (PGM, 2020)

Complex Markov Logic Networks: Expressivity and Liftability. - Ondrej Kuzelka (UAI, 2020)

Markov Logic Networks with Complex Weights: Expressivity, Liftability and Fourier Transforms. - Ondrej Kuzelka (CoRR, 2020)

Lifted Inference in 2-Variable Markov Logic Networks with Function and Cardinality Constraints Using Discrete Fourier Transform. - Ondrej Kuzelka (CoRR, 2020)

Learning with Molecules beyond Graph Neural Networks. - Gustav Sourek; Filip Zelezný; Ondrej Kuzelka (CoRR, 2020)

Context-Specific Likelihood Weighting. - Nitesh Kumar; Ondrej Kuzelka (AISTATS, 2021)

Lossless Compression of Structured Convolutional Models via Lifting. - Gustav Sourek; Filip Zelezný; Ondrej Kuzelka (ICLR, 2021)

Fast Algorithms for Relational Marginal Polytopes. - Yuanhong Wang; Timothy van Bremen; Juhua Pu; Yuyi Wang 001; Ondrej Kuzelka (IJCAI, 2021)

Automatic Conjecturing of P-Recursions Using Lifted Inference. - Jáchym Barvínek; Timothy van Bremen; Yuyi Wang 001; Filip Zelezný; Ondrej Kuzelka (ILP, 2021)

Faster lifting for two-variable logic using cell graphs. - Timothy van Bremen; Ondrej Kuzelka (UAI, 2021)

Neural markov logic networks. - Giuseppe Marra; Ondrej Kuzelka (UAI, 2021)

Weighted First-Order Model Counting in the Two-Variable Fragment With Counting Quantifiers. - Ondrej Kuzelka (J. Artif. Intell. Res., 2021)

Beyond graph neural networks with lifted relational neural networks. - Gustav Sourek; Filip Zelezný; Ondrej Kuzelka (Mach. Learn., 2021)

Domain-Lifted Sampling for Universal Two-Variable Logic and Extensions. - Yuanhong Wang; Timothy van Bremen; Yuyi Wang 001; Ondrej Kuzelka (AAAI, 2022)

Learning Distributional Programs for Relational Autocompletion. - Nitesh Kumar; Ondrej Kuzelka; Luc De Raedt (Theory Pract. Log. Program., 2022)

Lifted Inference with Linear Order Axiom. - Jan Tóth; Ondrej Kuzelka (AAAI, 2023)

Counting and Sampling Models in First-Order Logic. - Ondrej Kuzelka (IJCAI, 2023)

On Discovering Interesting Combinatorial Integer Sequences. - Martin Svatos; Peter Jung; Jan Tóth; Yuyi Wang 001; Ondrej Kuzelka (IJCAI, 2023)

On Exact Sampling in the Two-Variable Fragment of First-Order Logic. - Yuanhong Wang; Juhua Pu; Yuyi Wang 001; Ondrej Kuzelka (LICS, 2023)

First-Order Context-Specific Likelihood Weighting in Hybrid Probabilistic Logic Programs. - Nitesh Kumar; Ondrej Kuzelka; Luc De Raedt (J. Artif. Intell. Res., 2023)

Lifted Relational Neural Networks: From Graphs to Deep Relational Learning. - Gustav Sír; Filip Zelezný; Ondrej Kuzelka (Compendium of Neurosymbolic Artificial Intelligence, 2023)

Lifted inference with tree axioms. - Timothy van Bremen; Ondrej Kuzelka (Artif. Intell., 2023)

Model Counting and Sampling in First-Order Logic (Abstract of Invited Talk). - Ondrej Kuzelka (Description Logics, 2024)

A More Practical Algorithm for Weighted First-Order Model Counting with Linear Order Axiom. - Qiaolan Meng; Jan Tóth; Yuanhong Wang; Yuyi Wang 001; Ondrej Kuzelka (ECAI, 2024)

Complexity of Weighted First-Order Model Counting in the Two-Variable Fragment with Counting Quantifiers: A Bound to Beat. - Jan Tóth; Ondrej Kuzelka (KR, 2024)

Faster Repeated Evasion Attacks in Tree Ensembles. - Lorenzo Cascioli; Laurens Devos; Ondrej Kuzelka; Jesse Davis (NeurIPS, 2024)

Lifted algorithms for symmetric weighted first-order model sampling. - Yuanhong Wang; Juhua Pu; Yuyi Wang 001; Ondrej Kuzelka (Artif. Intell., 2024)

Bridging Weighted First Order Model Counting and Graph Polynomials. - Qipeng Kuang; Ondrej Kuzelka; Yuanhong Wang; Yuyi Wang 001 (CoRR, 2024)

Quantified neural Markov logic networks. - Peter Jung; Giuseppe Marra; Ondrej Kuzelka (Int. J. Approx. Reason., 2024)

Model Enumeration of Two-Variable Logic with Quadratic Delay Complexity. - Qiaolan Meng; Juhua Pu; Hongting Niu; Yuyi Wang 001; Yuanhong Wang; Ondrej Kuzelka (CoRR, 2025)

Faster Lifting for Ordered Domains with Predecessor Relations. - Kuncheng Zou; Jiahao Mai; Yonggang Zhang; Yuyi Wang 001; Ondrej Kuzelka; Yuanhong Wang; Yi Chang (CoRR, 2025)