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Journal Articles
- 2024
[j3]Simon Mandlík
, Tomás Pevný, Václav Smídl, Lukás Bajer:
Malicious Internet Entity Detection Using Local Graph Inference. IEEE Trans. Inf. Forensics Secur. 19: 3554-3566 (2024)- 2019
- [j2]Lukás Bajer, Zbynek Pitra, Jakub Repický, Martin Holena:
Gaussian Process Surrogate Models for the CMA Evolution Strategy. Evol. Comput. 27(4): 665-697 (2019) - 2015
- [j1]Martin Holena, Lukás Bajer, Martin Scavnicky:
Using Copulas in Data Mining Based on the Observational Calculus. IEEE Trans. Knowl. Data Eng. 27(10): 2851-2864 (2015)
Conference and Workshop Papers
- 2024
- [c25]Marek Dedic, Lukás Bajer, Pavel Procházka, Martin Holena:
Balancing performance and complexity with adaptive graph coarsening. Tiny Papers @ ICLR 2024 - 2022
- [c24]Stepan Dvorak, Pavel Procházka, Lukás Bajer:
GNN-Based Malicious Network Entities Identification In Large-Scale Network Data. NOMS 2022: 1-4 - 2021
- [c23]Paul Prasse, Jan Brabec, Jan Kohout, Martin Kopp, Lukás Bajer, Tobias Scheffer:
Learning Explainable Representations of Malware Behavior. ECML/PKDD (4) 2021: 53-68 - 2020
- [c22]Marek Dedic, Tomás Pevný, Lukás Bajer, Martin Holena:
Loss Functions for Clustering in Multi-instance Learning. ITAT 2020: 137-146 - 2019
- [c21]Lukás Bajer, Zbynek Pitra, Jakub Repický, Martin Holena:
Gaussian process surrogate models for the CMA-ES. GECCO (Companion) 2019: 17-18 - 2017
- [c20]Zbynek Pitra, Lukás Bajer, Jakub Repický, Martin Holena:
Ordinal versus metric gaussian process regression in surrogate modelling for CMA evolution strategy. GECCO (Companion) 2017: 177-178 - [c19]Zbynek Pitra, Lukás Bajer, Jakub Repický, Martin Holena:
Overview of surrogate-model versions of covariance matrix adaptation evolution strategy. GECCO (Companion) 2017: 1622-1629 - [c18]Zbynek Pitra, Lukás Bajer, Jakub Repický, Martin Holena:
Comparison of ordinal and metric gaussian process regression as surrogate models for CMA evolution strategy. GECCO (Companion) 2017: 1764-1771 - [c17]Zbynek Pitra, Lukás Bajer, Jakub Repický, Martin Holena:
Adaptive Doubly Trained Evolution Control for the Covariance Matrix Adaptation Evolution Strategy. ITAT 2017: 120-128 - [c16]Jakub Repický, Lukás Bajer, Zbynek Pitra, Martin Holena:
Adaptive Generation-Based Evolution Control for Gaussian Process Surrogate Models. ITAT 2017: 136-143 - 2016
- [c15]Nikita Orekhov, Lukás Bajer, Martin Holena:
Testing Gaussian Process Surrogates on CEC'2013 Multi-Modal Benchmark. ITAT 2016: 138-146 - [c14]Jakub Repický, Lukás Bajer, Martin Holena:
Traditional Gaussian Process Surrogates in the BBOB Framework. ITAT 2016: 163-171 - [c13]Zbynek Pitra, Lukás Bajer, Martin Holena:
Doubly Trained Evolution Control for the Surrogate CMA-ES. PPSN 2016: 59-68 - 2015
- [c12]Lukás Bajer, Zbynek Pitra, Martin Holena:
Benchmarking Gaussian Processes and Random Forests Surrogate Models on the BBOB Noiseless Testbed. GECCO (Companion) 2015: 1143-1150 - [c11]Lukás Bajer, Zbynek Pitra, Martin Holena:
Investigation of Gaussian Processes and Random Forests as Surrogate Models for Evolutionary Black-Box Optimization. GECCO (Companion) 2015: 1351-1352 - [c10]Lukás Bajer, Martin Holena:
Model Guided Sampling Optimization for Low-dimensional Problems. ICAART (2) 2015: 451-456 - [c9]Andrej Kudinov, Lukás Bajer, Zbynek Pitra, Martin Holena:
Investigation of Gaussian Processes in the Context of Black-Box Evolutionary Optimization. ITAT 2015: 159-166 - [c8]Zbynek Pitra, Lukás Bajer, Martin Holena:
Comparing SVM, Gaussian Process and Random Forest Surrogate Models for the CMA-ES. ITAT 2015: 186-193 - 2013
- [c7]Lukás Bajer, Viktor Charypar, Martin Holena:
Model guided sampling optimization with gaussian processes for expensive black-box optimization. GECCO (Companion) 2013: 1715-1716 - [c6]Lukás Bajer, Martin Holena:
Surrogate Model for Mixed-Variables Evolutionary Optimization Based on GLM and RBF Networks. SOFSEM 2013: 481-490 - 2012
- [c5]Martin Holena, David Linke, Lukás Bajer:
Surrogate modeling in the evolutionary optimization of catalytic materials. GECCO 2012: 1095-1102 - [c4]Lukás Bajer, Martin Holena:
RBF-based surrogate model for evolutionary optimization. ITAT 2012: 3-8 - 2011
- [c3]Martin Holena, David Linke, Lukás Bajer:
Case study: constraint handling in evolutionary optimization of catalytic materials. GECCO (Companion) 2011: 333-340 - 2010
- [c2]Martin Holena, David Linke, Uwe Rodemerck, Lukás Bajer:
Neural Networks as Surrogate Models for Measurements in Optimization Algorithms. ASMTA 2010: 351-366 - [c1]Lukás Bajer, Martin Holena:
Surrogate Model for Continuous and Discrete Genetic Optimization Based on RBF Networks. IDEAL 2010: 251-258
Editorship
- 2014
- [e1]Vera Kurková, Lukás Bajer, Vojtech Svátek:
Proceedings of the main track of the 14th Conference on Information Technologies - Applications and Theory (ITAT 2014), with selected papers from Znalosti 2014 collocated with Znalosti 2014, Demanovska Dolina - Jasna, Slovakia, September 25 - 29, 2014. CEUR Workshop Proceedings 1214, CEUR-WS.org 2014 [contents]
Informal and Other Publications
- 2024
- [i5]Simon Mandlík, Tomás Pevný, Václav Smídl, Lukás Bajer:
Malicious Internet Entity Detection Using Local Graph Inference. CoRR abs/2408.03287 (2024) - 2021
- [i4]Paul Prasse, Jan Brabec, Jan Kohout, Martin Kopp, Lukás Bajer, Tobias Scheffer:
Learning Explainable Representations of Malware Behavior. CoRR abs/2106.12328 (2021) - 2017
- [i3]Jakub Repický, Lukás Bajer, Zbynek Pitra, Martin Holena:
Adaptive Generation-Based Evolution Control for Gaussian Process Surrogate Models. CoRR abs/1709.10443 (2017) - 2015
- [i2]Lukás Bajer, Martin Holena:
Model Guided Sampling Optimization for Low-dimensional Problems. CoRR abs/1508.07741 (2015) - 2014
- [i1]Lukás Bajer, Martin Holena:
Two Gaussian Approaches to Black-Box Optomization. CoRR abs/1411.7806 (2014)
Coauthor Index
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last updated on 2024-09-14 02:05 CEST by the dblp team
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