Lukás Bajer

According to our database, Lukás Bajer authored at least 29 papers between 2010 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Malicious Internet Entity Detection Using Local Graph Inference.

Simon Mandlík, Tomás Pevný, Václav Smídl, Lukás Bajer

IEEE Trans. Inf. Forensics Secur., 2024

Balancing performance and complexity with adaptive graph coarsening.

Marek Dedic, Lukás Bajer, Pavel Procházka, Martin Holena

Proceedings of the Second Tiny Papers Track at ICLR 2024, 2024

2022

GNN-Based Malicious Network Entities Identification In Large-Scale Network Data.

Stepan Dvorak, Pavel Procházka, Lukás Bajer

Proceedings of the 2022 IEEE/IFIP Network Operations and Management Symposium, 2022

2021

Learning Explainable Representations of Malware Behavior.

Paul Prasse, Jan Brabec, Jan Kohout, Martin Kopp, Lukás Bajer, Tobias Scheffer

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track, 2021

2020

Loss Functions for Clustering in Multi-instance Learning.

Marek Dedic, Tomás Pevný, Lukás Bajer, Martin Holena

Proceedings of the 20th Conference Information Technologies, 2020

2019

Gaussian Process Surrogate Models for the CMA Evolution Strategy.

Lukás Bajer, Zbynek Pitra, Jakub Repický, Martin Holena

Evol. Comput., 2019

Gaussian process surrogate models for the CMA-ES.

Lukás Bajer, Zbynek Pitra, Jakub Repický, Martin Holena

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019

2017

Adaptive Generation-Based Evolution Control for Gaussian Process Surrogate Models.

Jakub Repický, Lukás Bajer, Zbynek Pitra, Martin Holena

Proceedings of the 17th Conference on Information Technologies, 2017

Adaptive Doubly Trained Evolution Control for the Covariance Matrix Adaptation Evolution Strategy.

Zbynek Pitra, Lukás Bajer, Jakub Repický, Martin Holena

Proceedings of the 17th Conference on Information Technologies, 2017

Comparison of ordinal and metric gaussian process regression as surrogate models for CMA evolution strategy.

Zbynek Pitra, Lukás Bajer, Jakub Repický, Martin Holena

Proceedings of the Genetic and Evolutionary Computation Conference, 2017

Overview of surrogate-model versions of covariance matrix adaptation evolution strategy.

Zbynek Pitra, Lukás Bajer, Jakub Repický, Martin Holena

Proceedings of the Genetic and Evolutionary Computation Conference, 2017

Ordinal versus metric gaussian process regression in surrogate modelling for CMA evolution strategy.

Zbynek Pitra, Lukás Bajer, Jakub Repický, Martin Holena

Proceedings of the Genetic and Evolutionary Computation Conference, 2017

2016

Doubly Trained Evolution Control for the Surrogate CMA-ES.

Zbynek Pitra, Lukás Bajer, Martin Holena

Proceedings of the Parallel Problem Solving from Nature - PPSN XIV, 2016

Traditional Gaussian Process Surrogates in the BBOB Framework.

Jakub Repický, Lukás Bajer, Martin Holena

Proceedings of the 16th ITAT Conference Information Technologies, 2016

Testing Gaussian Process Surrogates on CEC'2013 Multi-Modal Benchmark.

Nikita Orekhov, Lukás Bajer, Martin Holena

Proceedings of the 16th ITAT Conference Information Technologies, 2016

2015

Using Copulas in Data Mining Based on the Observational Calculus.

Martin Holena, Lukás Bajer, Martin Scavnicky

IEEE Trans. Knowl. Data Eng., 2015

Comparing SVM, Gaussian Process and Random Forest Surrogate Models for the CMA-ES.

Zbynek Pitra, Lukás Bajer, Martin Holena

Proceedings of the Proceedings ITAT 2015: Information Technologies, 2015

Investigation of Gaussian Processes in the Context of Black-Box Evolutionary Optimization.

Andrej Kudinov, Lukás Bajer, Zbynek Pitra, Martin Holena

Proceedings of the Proceedings ITAT 2015: Information Technologies, 2015

Model Guided Sampling Optimization for Low-dimensional Problems.

Lukás Bajer, Martin Holena

Proceedings of the ICAART 2015, 2015

Investigation of Gaussian Processes and Random Forests as Surrogate Models for Evolutionary Black-Box Optimization.

Lukás Bajer, Zbynek Pitra, Martin Holena

Proceedings of the Genetic and Evolutionary Computation Conference, 2015

Benchmarking Gaussian Processes and Random Forests Surrogate Models on the BBOB Noiseless Testbed.

Lukás Bajer, Zbynek Pitra, Martin Holena

Proceedings of the Genetic and Evolutionary Computation Conference, 2015

2014

Two Gaussian Approaches to Black-Box Optomization.

Lukás Bajer, Martin Holena

CoRR, 2014

2013

Surrogate Model for Mixed-Variables Evolutionary Optimization Based on GLM and RBF Networks.

Lukás Bajer, Martin Holena

Proceedings of the SOFSEM 2013: Theory and Practice of Computer Science, 2013

Model guided sampling optimization with gaussian processes for expensive black-box optimization.

Lukás Bajer, Viktor Charypar, Martin Holena

Proceedings of the Genetic and Evolutionary Computation Conference, 2013

2012

RBF-based surrogate model for evolutionary optimization.

Lukás Bajer, Martin Holena

Proceedings of the Conference on Theory and Practice of Information Technologies, 2012

Surrogate modeling in the evolutionary optimization of catalytic materials.

Martin Holena, David Linke, Lukás Bajer

Proceedings of the Genetic and Evolutionary Computation Conference, 2012

2011

Case study: constraint handling in evolutionary optimization of catalytic materials.

Martin Holena, David Linke, Lukás Bajer

Proceedings of the 13th Annual Genetic and Evolutionary Computation Conference, 2011

2010

Surrogate Model for Continuous and Discrete Genetic Optimization Based on RBF Networks.

Lukás Bajer, Martin Holena

Proceedings of the Intelligent Data Engineering and Automated Learning, 2010

Neural Networks as Surrogate Models for Measurements in Optimization Algorithms.

Martin Holena, David Linke, Uwe Rodemerck, Lukás Bajer

Proceedings of the Analytical and Stochastic Modeling Techniques and Applications, 2010