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Martin Pilát
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2020 – today
- 2024
- [c50]Katerina Macková
, Martin Pilát
ProMap: Product Mapping Datasets. ECIR (2) 2024: 159-172 - [c49]Gabriela Kadlecová, Jovita Lukasik, Martin Pilát, Petra Vidnerová, Mahmoud Safari, Roman Neruda, Frank Hutter:
Surprisingly Strong Performance Prediction with Neural Graph Features. ICML 2024 - [i5]Gabriela Kadlecová, Jovita Lukasik, Martin Pilát, Petra Vidnerová, Mahmoud Safari, Roman Neruda, Frank Hutter:
Surprisingly Strong Performance Prediction with Neural Graph Features. CoRR abs/2404.16551 (2024) - 2023
- [i4]Katerina Macková, Martin Pilát:
ProMap: Datasets for Product Mapping in E-commerce. CoRR abs/2309.06882 (2023) - 2022
- [j4]Lukás Chrpa, Martin Pilát
Planning and acting in dynamic environments: identifying and avoiding dangerous situations. J. Exp. Theor. Artif. Intell. 34(6): 925-948 (2022) - [c48]Patrik Valkovic, Martin Pilát
Implementing and evaluating parallel evolutionary algorithms in modern GPU computing libraries. GECCO Companion 2022: 506-509 - [c47]Martin Pilát
Using graph neural networks as surrogate models in genetic programming. GECCO Companion 2022: 582-585 - 2021
- [c46]Martin Pilát
Training Electric Vehicle Charging Controllers with Imitation Learning. ICTAI 2021: 674-681 - [c45]Vera Kumová
Beating White-Box Defenses with Black-Box Attacks. IJCNN 2021: 1-8 - [c44]Lukás Chrpa, Martin Pilát, Jakub Med
On Eventual Applicability of Plans in Dynamic Environments with Cyclic Phenomena. KR 2021: 184-193 - [i3]Martin Pilát:
Training Electric Vehicle Charging Controllers with Imitation Learning. CoRR abs/2107.10111 (2021) - 2020
- [j3]Stepán Balcar
Heterogeneous Island Models and Their Application to Recommender Systems and Electric Vehicle Charging. Int. J. Artif. Intell. Tools 29(03n04): 2060010:1-2060010:20 (2020) - [c43]Lukás Chrpa, Jakub Gemrot
Planning and Acting with Non-Deterministic Events: Navigating between Safe States. AAAI 2020: 9802-9809
2010 – 2019
- 2019
- [c42]Lukás Chrpa, Martin Pilát, Jakub Gemrot:
Compiling Planning Problems with Non-deterministic Events into FOND Planning. RCRA/RiCeRcA@AI*IA 2019 - [c41]Matej Kocián
Using Local Convolutional Units to Defend Against Adversarial Examples. IJCNN 2019: 1-8 - [c40]David Samuel
Composing Multi-Instrumental Music with Recurrent Neural Networks. IJCNN 2019: 1-8 - 2018
- [c39]Martin Pilát
Evolving Controllers for Electric Vehicle Charging. EvoApplications 2018: 247-255 - [c38]Stepán Balcar
Heterogeneous island model with re-planning of methods. GECCO (Companion) 2018: 245-246 - [c37]Stepán Balcar
Online Parallel Portfolio Selection with Heterogeneous Island Model. ICTAI 2018: 757-764 - [c36]Martin Pilát
Evolving Ensembles of Traffic Lights Controllers. ICTAI 2018: 958-962 - [c35]Martin Pilát
Controlling the Charging of Electric Vehicles with Neural Networks. IJCNN 2018: 1-8 - [i2]Martin Pilát:
Controlling the Charging of Electric Vehicles with Neural Networks. CoRR abs/1804.05978 (2018) - [i1]Stepán Balcar, Martin Pilát:
Online Parallel Portfolio Selection with Heterogeneous Island Model. CoRR abs/1806.04528 (2018) - 2017
- [j2]Tomás Kren, Martin Pilát
Automatic Creation of Machine Learning Workflows with Strongly Typed Genetic Programming. Int. J. Artif. Intell. Tools 26(5): 1760020:1-1760020:24 (2017) - [c34]Martin Pilát
Parallel evolutionary algorithm with interleaving generations. GECCO 2017: 865-872 - [c33]Lukás Chrpa, Jakub Gemrot, Martin Pilát
Towards a Safer Planning and Execution Concept. ICTAI 2017: 972-976 - [c32]Tomás Kren, Martin Pilát
Multi-objective evolution of machine learning workflows. SSCI 2017: 1-8 - [c31]Roman Neruda
Unsupervised and Supervised Activity Analysis of Drone Sensor Data. WEA 2017: 3-11 - 2016
- [c30]Martin Pilát
General tuning of weights in MOEA/D. CEC 2016: 965-972 - [c29]Martin Pilát, Tomás Kren, Roman Neruda:
Asynchronous Evolution of Data Mining Workflow Schemes by Strongly Typed Genetic Programming. ICTAI 2016: 577-584 - [c28]Martin Pilát
Feature Extraction for Surrogate Models in Genetic Programming. PPSN 2016: 335-344 - 2015
- [c27]Jakub Smíd, Martin Pilát
Co-evolutionary genetic programming for dataset similarity induction. CEC 2015: 1160-1166 - [c26]Martin Pilát
Incorporating User Preferences in MOEA/D through the Coevolution of Weights. GECCO 2015: 727-734 - [c25]Martin Pilát
Hypervolume-Based Surrogate Model for MO-CMA-ES. ICTAI 2015: 604-611 - [c24]Tomás Kren, Martin Pilát, Klára Pesková, Roman Neruda:
Generating Workflow Graphs Using Typed Genetic Programming. MetaSel@PKDD/ECML 2015: 108-109 - [c23]Toma Ken, Martin Pilát
Evolving Workflow Graphs Using Typed Genetic Programming. SSCI 2015: 1407-1414 - [c22]Jakub Smíd, Martin Pilát
Multi-Objective Genetic Programming for Dataset Similarity Induction. SSCI 2015: 1576-1582 - 2014
- [c21]Martin Pilát
The effect of different local search algorithms on the performance of multi-objective optimizers. IEEE Congress on Evolutionary Computation 2014: 2172-2179 - [c20]Klára Pesková, Jakub Smíd, Martin Pilát, Ondrej Kazík, Roman Neruda:
Hybrid Multi-Agent System for Metalearning in Data Mining. MetaSel@ECAI 2014: 53-54 - [c19]Martin Pilát
Hypervolume-based local search in multi-objective evolutionary optimization. GECCO 2014: 637-644 - 2013
- [j1]Martin Pilát
Aggregate meta-models for evolutionary multiobjective and many-objective optimization. Neurocomputing 116: 392-402 (2013) - [c18]Martin Pilát
Surrogate model selection for evolutionary multiobjective optimization. IEEE Congress on Evolutionary Computation 2013: 1860-1867 - [c17]Martin Pilát
Multiobjectivization for classifier parameter tuning. GECCO (Companion) 2013: 97-98 - [c16]Martin Pilát
Multi-objectivization and Surrogate Modelling for Neural Network Hyper-parameters Tuning. ICIC (3) 2013: 61-66 - 2012
- [c15]Stepán Balcar
An evolutionary algorithm for 2D semi-guillotinable circular saw cutting. IEEE Congress on Evolutionary Computation 2012: 1-5 - [c14]Martin Pilát
An Evolutionary Strategy for Surrogate-Based Multiobjective Optimization. IEEE Congress on Evolutionary Computation 2012: 1-7 - [c13]Martin Pilát
A surrogate multiobjective evolutionary strategy with local search and pre-selection. GECCO (Companion) 2012: 633-634 - [c12]Ondrej Kazík, Klára Pesková
A Novel Meta Learning System and Its Application to Optimization of Computing Agents' Results. IAT 2012: 170-174 - [c11]Ondrej Kazík, Klára Pesková
Combining Parameter Space Search and Meta-learning for Data-Dependent Computational Agent Recommendation. ICMLA (2) 2012: 36-41 - [c10]Martin Pilát
Meta-learning and Model Selection in Multi-objective Evolutionary Algorithms. ICMLA (1) 2012: 433-438 - [c9]Martin Pilát
A Surrogate Based Multiobjective Evolution Strategy with Different Models for Local Search and Pre-selection. ICTAI 2012: 215-222 - 2011
- [c8]Martin Pilát
ASM-MOMA: Multiobjective memetic algorithm with aggregate surrogate model. IEEE Congress on Evolutionary Computation 2011: 1202-1208 - [c7]Martin Pilát
LAMM-MMA: multiobjective memetic algorithm with local aggregate meta-model. GECCO (Companion) 2011: 79-80 - [c6]Martin Pilát
Improving many-objective optimizers with aggregate meta-models. HIS 2011: 555-560 - [c5]Ondrej Kazík, Klára Pesková
Meta Learning in Multi-agent Systems for Data Mining. IAT 2011: 433-434 - [c4]Martin Pilát
Local Meta-models for ASM-MOMA. ICIC (3) 2011: 79-84 - [c3]Martin Pilát
Local Meta-models for ASM-MOMA. ICIC (1) 2011: 147-152 - [c2]Ondrej Kazík, Klára Pesková
Implementation of Parameter Space Search for Meta Learning in a Data-Mining Multi-agent System. ICMLA (2) 2011: 366-369 - 2010
- [c1]Martin Pilát
Combining multiobjective and single-objective genetic algorithms in heterogeneous island model. IEEE Congress on Evolutionary Computation 2010: 1-8
Coauthor Index
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