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Ondrej Kuzelka
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Journal Articles
- 2024
- [j15]Yuanhong Wang
, Juhua Pu, Yuyi Wang
, Ondrej Kuzelka:
Lifted algorithms for symmetric weighted first-order model sampling. Artif. Intell. 331: 104114 (2024) - [j14]Peter Jung, Giuseppe Marra, Ondrej Kuzelka
:
Quantified neural Markov logic networks. Int. J. Approx. Reason. 171: 109172 (2024) - 2023
- [j13]Timothy van Bremen, Ondrej Kuzelka:
Lifted inference with tree axioms. Artif. Intell. 324: 103997 (2023) - [j12]Nitesh Kumar, Ondrej Kuzelka, Luc De Raedt:
First-Order Context-Specific Likelihood Weighting in Hybrid Probabilistic Logic Programs. J. Artif. Intell. Res. 77: 683-735 (2023) - 2022
- [j11]Nitesh Kumar
, Ondrej Kuzelka
, Luc De Raedt
:
Learning Distributional Programs for Relational Autocompletion. Theory Pract. Log. Program. 22(1): 81-114 (2022) - 2021
- [j10]Ondrej Kuzelka
:
Weighted First-Order Model Counting in the Two-Variable Fragment With Counting Quantifiers. J. Artif. Intell. Res. 70: 1281-1307 (2021) - [j9]Gustav Sourek
, Filip Zelezný, Ondrej Kuzelka
:
Beyond graph neural networks with lifted relational neural networks. Mach. Learn. 110(7): 1695-1738 (2021) - 2018
- [j8]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: 69-100 (2018) - 2016
- [j7]Radomír Cernoch, Ondrej Kuzelka
, Filip Zelezný
:
Polynomial and Extensible Solutions in Lock-Chart Solving. Appl. Artif. Intell. 30(10): 923-941 (2016) - 2015
- [j6]Matej Holec, Ondrej Kuzelka
, Filip Zelezný
:
Novel gene sets improve set-level classification of prokaryotic gene expression data. BMC Bioinform. 16: 348:1-348:8 (2015) - 2014
- [j5]Ondrej Kuzelka
, Andrea Szabóová, Filip Zelezný
:
A method for reduction of examples in relational learning. J. Intell. Inf. Syst. 42(2): 255-281 (2014) - 2013
- [j4]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): 144-165 (2013) - 2012
- [j3]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): S3 (2012) - 2011
- [j2]Ondrej Kuzelka
, Filip Zelezný
:
Block-wise construction of tree-like relational features with monotone reducibility and redundancy. Mach. Learn. 83(2): 163-192 (2011) - 2008
- [j1]Ondrej Kuzelka, Filip Zelezný:
A Restarted Strategy for Efficient Subsumption Testing. Fundam. Informaticae 89(1): 95-109 (2008)
Conference and Workshop Papers
- 2024
- [c56]Ondrej Kuzelka:
Model Counting and Sampling in First-Order Logic (Abstract of Invited Talk). Description Logics 2024 - 2023
- [c55]Jan Tóth, Ondrej Kuzelka:
Lifted Inference with Linear Order Axiom. AAAI 2023: 12295-12304 - [c54]Martin Svatos, Peter Jung, Jan Tóth, Yuyi Wang, Ondrej Kuzelka:
On Discovering Interesting Combinatorial Integer Sequences. IJCAI 2023: 3338-3346 - [c53]Ondrej Kuzelka:
Counting and Sampling Models in First-Order Logic. IJCAI 2023: 7020-7025 - [c52]Yuanhong Wang
, Juhua Pu, Yuyi Wang, Ondrej Kuzelka:
On Exact Sampling in the Two-Variable Fragment of First-Order Logic. LICS 2023: 1-13 - 2022
- [c51]Yuanhong Wang
, Timothy van Bremen, Yuyi Wang, Ondrej Kuzelka:
Domain-Lifted Sampling for Universal Two-Variable Logic and Extensions. AAAI 2022: 10070-10079 - 2021
- [c50]Nitesh Kumar, Ondrej Kuzelka:
Context-Specific Likelihood Weighting. AISTATS 2021: 2125-2133 - [c49]Gustav Sourek, Filip Zelezný, Ondrej Kuzelka:
Lossless Compression of Structured Convolutional Models via Lifting. ICLR 2021 - [c48]Yuanhong Wang
, Timothy van Bremen, Juhua Pu, Yuyi Wang, Ondrej Kuzelka:
Fast Algorithms for Relational Marginal Polytopes. IJCAI 2021: 4266-4274 - [c47]Jáchym Barvínek, Timothy van Bremen, Yuyi Wang, Filip Zelezný, Ondrej Kuzelka
:
Automatic Conjecturing of P-Recursions Using Lifted Inference. ILP 2021: 17-25 - [c46]Timothy van Bremen, Ondrej Kuzelka:
Lifted Inference with Tree Axioms. KR 2021: 599-608 - [c45]Giuseppe Marra, Ondrej Kuzelka:
Neural markov logic networks. UAI 2021: 908-917 - [c44]Timothy van Bremen, Ondrej Kuzelka:
Faster lifting for two-variable logic using cell graphs. UAI 2021: 1393-1402 - 2020
- [c43]Ondrej Kuzelka, Yuyi Wang:
Domain-Liftability of Relational Marginal Polytopes. AISTATS 2020: 2284-2292 - [c42]Martin Svatos, Steven Schockaert, Jesse Davis
, Ondrej Kuzelka
:
STRiKE: Rule-Driven Relational Learning Using Stratified k-Entailment. ECAI 2020: 1515-1522 - [c41]Timothy van Bremen, Ondrej Kuzelka:
Approximate Weighted First-Order Model Counting: Exploiting Fast Approximate Model Counters and Symmetry. IJCAI 2020: 4252-4258 - [c40]Ondrej Kuzelka, Vyacheslav Kungurtsev, Yuyi Wang:
Lifted Weight Learning of Markov Logic Networks (Revisited One More Time). PGM 2020: 269-280 - [c39]Ondrej Kuzelka:
Complex Markov Logic Networks: Expressivity and Liftability. UAI 2020: 729-738 - 2019
- [c38]Ondrej Kuzelka, Vyacheslav Kungurtsev:
Lifted Weight Learning of Markov Logic Networks Revisited. AISTATS 2019: 1753-1761 - [c37]Arcchit Jain, Tal Friedman, Ondrej Kuzelka, Guy Van den Broeck, Luc De Raedt
:
Scalable Rule Learning in Probabilistic Knowledge Bases. AKBC 2019 - [c36]Gustav Sourek, Filip Zelezný, Ondrej Kuzelka:
Scaling up relational templated neural models. NeSy@IJCAI 2019 - [c35]Ondrej Kuzelka, Jesse Davis:
Markov Logic Networks for Knowledge Base Completion: A Theoretical Analysis Under the MCAR Assumption. UAI 2019: 1138-1148 - 2018
- [c34]Ondrej Kuzelka, Yuyi Wang, Jesse Davis, Steven Schockaert:
Relational Marginal Problems: Theory and Estimation. AAAI 2018: 6384-6391 - [c33]Thomas Ager, Ondrej Kuzelka
, Steven Schockaert:
Modelling Salient Features as Directions in Fine-Tuned Semantic Spaces. CoNLL 2018: 530-540 - [c32]Víctor Gutiérrez-Basulto, Jean Christoph Jung
, Ondrej Kuzelka:
Quantified Markov Logic Networks. KR 2018: 602-612 - [c31]Ondrej Kuzelka
, Yuyi Wang, Steven Schockaert:
VC-Dimension Based Generalization Bounds for Relational Learning. ECML/PKDD (2) 2018: 259-275 - [c30]Ondrej Kuzelka, Yuyi Wang, Jesse Davis, Steven Schockaert:
PAC-Reasoning in Relational Domains. UAI 2018: 927-936 - 2017
- [c29]Ondrej Kuzelka, Jesse Davis, Steven Schockaert:
Induction of Interpretable Possibilistic Logic Theories from Relational Data. IJCAI 2017: 1153-1159 - [c28]Gustav Sourek
, Martin Svatos, Filip Zelezný
, Steven Schockaert, Ondrej Kuzelka
:
Stacked Structure Learning for Lifted Relational Neural Networks. ILP 2017: 140-151 - [c27]Martin Svatos
, Gustav Sourek
, Filip Zelezný
, Steven Schockaert, Ondrej Kuzelka
:
Pruning Hypothesis Spaces Using Learned Domain Theories. ILP 2017: 152-168 - 2016
- [c26]Ondrej Kuzelka
, Jesse Davis
, Steven Schockaert:
Interpretable Encoding of Densities Using Possibilistic Logic. ECAI 2016: 1239-1247 - [c25]Ondrej Kuzelka, Jesse Davis, Steven Schockaert:
Learning Possibilistic Logic Theories from Default Rules. IJCAI 2016: 1167-1173 - [c24]Ondrej Kuzelka, Yuyi Wang, Jan Ramon:
Bounds for Learning from Evolutionary-Related Data in the Realizable Case. IJCAI 2016: 1655-1661 - [c23]Gustav Sourek
, Suresh Manandhar, Filip Zelezný
, Steven Schockaert, Ondrej Kuzelka
:
Learning Predictive Categories Using Lifted Relational Neural Networks. ILP 2016: 108-119 - [c22]Thomas Ager, Ondrej Kuzelka, Steven Schockaert:
Inducing Symbolic Rules from Entity Embeddings using Auto-encoders. NeSy@HLAI 2016 - 2015
- [c21]Gustav Sourek
, Ondrej Kuzelka
, Filip Zelezný
:
Learning to Detect Network Intrusion from a Few Labeled Events and Background Traffic. AIMS 2015: 73-86 - [c20]Ondrej Kuzelka, Jan Ramon:
A Note on Restricted Forms of LGG. ILP (Late Breaking Papers) 2015: 62-68 - [c19]Ondrej Kuzelka
, Jesse Davis
, Steven Schockaert:
Constructing Markov Logic Networks from First-Order Default Rules. ILP 2015: 91-105 - [c18]Ondrej Kuzelka
, Jan Ramon:
Mine 'Em All: A Note on Mining All Graphs. ILP 2015: 106-121 - [c17]Gustav Sourek, Vojtech Aschenbrenner, Filip Zelezný, Ondrej Kuzelka:
Lifted Relational Neural Networks. CoCo@NIPS 2015 - [c16]Ondrej Kuzelka, Jesse Davis, Steven Schockaert:
Encoding Markov logic networks in Possibilistic Logic. UAI 2015: 454-463 - 2013
- [c15]Gustav Sourek, Ondrej Kuzelka, Filip Zelezný:
Predicting Top-k Trends on Twitter using Graphlets and Time Features. ILP (Late Breaking Papers) 2013: 52-57 - 2012
- [c14]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: 1-4 - [c13]Andrea Szabóová, Ondrej Kuzelka
, Filip Zelezný
:
Prediction of antimicrobial activity of peptides using relational machine learning. BIBM Workshops 2012: 575-580 - [c12]Ondrej Kuzelka
, Andrea Szabóová, Filip Zelezný
:
Relational Learning with Polynomials. ICTAI 2012: 1145-1150 - [c11]Ondrej Kuzelka
, Andrea Szabóová, Filip Zelezný
:
Bounded Least General Generalization. ILP 2012: 116-129 - [c10]Ondrej Kuzelka
, Andrea Szabóová, Filip Zelezný
:
Reducing Examples in Relational Learning with Bounded-Treewidth Hypotheses. NFMCP 2012: 17-32 - 2011
- [c9]Ondrej Kuzelka
, Andrea Szabóová, Filip Zelezný:
Gaussian logic and its applications in bioinformatics. BCB 2011: 496-498 - [c8]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: 358-367 - [c7]Ondrej Kuzelka
, Andrea Szabóová, Matej Holec, Filip Zelezný
:
Gaussian Logic for Predictive Classification. ECML/PKDD (2) 2011: 277-292 - 2010
- [c6]Roman Barták, Ondrej Kuzelka, Filip Zelezný:
Formulating Template Consistency in Inductive Logic Programming as a Constraint Satisfaction Problem. Abstraction, Reformulation, and Approximation 2010 - [c5]Roman Barták, Ondrej Kuzelka, Filip Zelezný:
Using Constraint Satisfaction for Learning Hypotheses in Inductive Logic Programming. FLAIRS 2010 - [c4]Ondrej Kuzelka
, Filip Zelezný
:
Seeing the World through Homomorphism: An Experimental Study on Reducibility of Examples. ILP 2010: 138-145 - [c3]Filip Zelezný
, Ondrej Kuzelka
:
Taming the Complexity of Inductive Logic Programming. SOFSEM 2010: 132-140 - 2009
- [c2]Ondrej Kuzelka
, Filip Zelezný:
Block-wise construction of acyclic relational features with monotone irreducibility and relevancy properties. ICML 2009: 569-576 - 2008
- [c1]Ondrej Kuzelka, Filip Zelezný:
Fast estimation of first-order clause coverage through randomization and maximum likelihood. ICML 2008: 504-511
Parts in Books or Collections
- 2023
- [p1]Gustav Sír, Filip Zelezný, Ondrej Kuzelka:
Lifted Relational Neural Networks: From Graphs to Deep Relational Learning. Compendium of Neurosymbolic Artificial Intelligence 2023: 308-336
Informal and Other Publications
- 2024
- [i29]Lorenzo Cascioli, Laurens Devos, Ondrej Kuzelka, Jesse Davis:
Faster Repeated Evasion Attacks in Tree Ensembles. CoRR abs/2402.08586 (2024) - [i28]Jan Tóth, Ondrej Kuzelka:
Complexity of Weighted First-Order Model Counting in the Two-Variable Fragment with Counting Quantifiers: A Bound to Beat. CoRR abs/2404.12905 (2024) - [i27]Qipeng Kuang, Ondrej Kuzelka, Yuanhong Wang, Yuyi Wang:
Bridging Weighted First Order Model Counting and Graph Polynomials. CoRR abs/2407.11877 (2024) - 2023
- [i26]Yuanhong Wang, Juhua Pu, Yuyi Wang, Ondrej Kuzelka:
On Exact Sampling in the Two-Variable Fragment of First-Order Logic. CoRR abs/2302.02730 (2023) - [i25]Martin Svatos, Peter Jung, Jan Tóth, Yuyi Wang, Ondrej Kuzelka:
On Discovering Interesting Combinatorial Integer Sequences. CoRR abs/2302.04606 (2023) - [i24]Yuanhong Wang, Juhua Pu, Yuyi Wang, Ondrej Kuzelka:
Lifted Algorithms for Symmetric Weighted First-Order Model Sampling. CoRR abs/2308.08828 (2023) - 2022
- [i23]Nitesh Kumar, Ondrej Kuzelka, Luc De Raedt:
First-Order Context-Specific Likelihood Weighting in Hybrid Probabilistic Logic Programs. CoRR abs/2201.11165 (2022) - [i22]Jan Tóth, Ondrej Kuzelka:
Lifted Inference with Linear Order Axiom. CoRR abs/2211.01164 (2022) - 2021
- [i21]Nitesh Kumar, Ondrej Kuzelka:
Context-Specific Likelihood Weighting. CoRR abs/2101.09791 (2021) - 2020
- [i20]Ondrej Kuzelka, Yuyi Wang:
Domain-Liftability of Relational Marginal Polytopes. CoRR abs/2001.05198 (2020) - [i19]Timothy van Bremen, Ondrej Kuzelka:
Approximate Weighted First-Order Model Counting: Exploiting Fast Approximate Model Counters and Symmetry. CoRR abs/2001.05263 (2020) - [i18]Nitesh Kumar, Ondrej Kuzelka, Luc De Raedt:
Learning Distributional Programs for Relational Autocompletion. CoRR abs/2001.08603 (2020) - [i17]Ondrej Kuzelka:
Markov Logic Networks with Complex Weights: Expressivity, Liftability and Fourier Transforms. CoRR abs/2002.10259 (2020) - [i16]Ondrej Kuzelka:
Lifted Inference in 2-Variable Markov Logic Networks with Function and Cardinality Constraints Using Discrete Fourier Transform. CoRR abs/2006.03432 (2020) - [i15]Ondrej Kuzelka:
Weighted First-Order Model Counting in the Two-Variable Fragment With Counting Quantifiers. CoRR abs/2007.05619 (2020) - [i14]Gustav Sourek, Filip Zelezný, Ondrej Kuzelka:
Beyond Graph Neural Networks with Lifted Relational Neural Networks. CoRR abs/2007.06286 (2020) - [i13]Gustav Sourek, Filip Zelezný, Ondrej Kuzelka:
Learning with Molecules beyond Graph Neural Networks. CoRR abs/2011.03488 (2020) - 2019
- [i12]Ondrej Kuzelka, Vyacheslav Kungurtsev:
Lifted Weight Learning of Markov Logic Networks Revisited. CoRR abs/1903.03099 (2019) - [i11]Giuseppe Marra, Ondrej Kuzelka:
Neural Markov Logic Networks. CoRR abs/1905.13462 (2019) - 2018
- [i10]Ondrej Kuzelka, Yuyi Wang, Jesse Davis, Steven Schockaert:
PAC-Reasoning in Relational Domains. CoRR abs/1803.05768 (2018) - [i9]Ondrej Kuzelka, Yuyi Wang, Steven Schockaert:
VC-Dimension Based Generalization Bounds for Relational Learning. CoRR abs/1804.06188 (2018) - [i8]Víctor Gutiérrez-Basulto, Jean Christoph Jung, Ondrej Kuzelka:
Markov Logic Networks with Statistical Quantifiers. CoRR abs/1807.01183 (2018) - 2017
- [i7]Ondrej Kuzelka, Jesse Davis, Steven Schockaert:
Induction of Interpretable Possibilistic Logic Theories from Relational Data. CoRR abs/1705.07095 (2017) - [i6]Ondrej Kuzelka, Yuyi Wang, Jesse Davis, Steven Schockaert:
Relational Marginal Problems: Theory and Estimation. CoRR abs/1709.05825 (2017) - [i5]Gustav Sourek, Martin Svatos, Filip Zelezný, Steven Schockaert, Ondrej Kuzelka:
Stacked Structure Learning for Lifted Relational Neural Networks. CoRR abs/1710.02221 (2017) - 2016
- [i4]Ondrej Kuzelka, Jesse Davis, Steven Schockaert:
Learning Possibilistic Logic Theories from Default Rules. CoRR abs/1604.05273 (2016) - [i3]Ondrej Kuzelka, Jesse Davis, Steven Schockaert:
Stratified Knowledge Bases as Interpretable Probabilistic Models (Extended Abstract). CoRR abs/1611.06174 (2016) - 2015
- [i2]Ondrej Kuzelka, Jesse Davis, Steven Schockaert:
Encoding Markov Logic Networks in Possibilistic Logic. CoRR abs/1506.01432 (2015) - [i1]Gustav Sourek, Vojtech Aschenbrenner, Filip Zelezný, Ondrej Kuzelka:
Lifted Relational Neural Networks. CoRR abs/1508.05128 (2015)
Coauthor Index
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