Niki van Stein

Niki van Stein

Assistant Professor in Explainable AI for ML and Optimization

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

projects

CIMPLO

Cross-Industry Predictive Maintenance Optimization Platform

XAIPre

eXplainable AI for Predictive Maintenance

publications

Fitness landscape analysis of nk landscapes and vehicle routing problems by expanded barrier trees

Published in EVOLVE-A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV: International Conference held at Leiden University, July 10-13, 2013, 2013

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Recommended citation: Bas Van, Michael Emmerich, Zhiwei Yang, "Fitness landscape analysis of nk landscapes and vehicle routing problems by expanded barrier trees." EVOLVE-A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV: International Conference held at Leiden University, July 10-13, 2013, 2013.

Optimally weighted cluster kriging for big data regression

Published in Advances in Intelligent Data Analysis XIV: 14th International Symposium, IDA 2015, Saint Etienne. France, October 22-24, 2015. Proceedings, 2015

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Recommended citation: Bas Stein, Hao Wang, Wojtek Kowalczyk, Thomas B{\"a}ck, Michael Emmerich, "Optimally weighted cluster kriging for big data regression." Advances in Intelligent Data Analysis XIV: 14th International Symposium, IDA 2015, Saint Etienne. France, October 22-24, 2015. Proceedings, 2015.

An incremental algorithm for repairing training sets with missing values

Published in Information Processing and Management of Uncertainty in Knowledge-Based Systems: 16th International Conference, IPMU 2016, Eindhoven, The Netherlands, June 20-24, 2016, Proceedings, Part II 16, 2016

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Recommended citation: Bas Van, Wojtek Kowalczyk, "An incremental algorithm for repairing training sets with missing values." Information Processing and Management of Uncertainty in Knowledge-Based Systems: 16th International Conference, IPMU 2016, Eindhoven, The Netherlands, June 20-24, 2016, Proceedings, Part II 16, 2016.

Analysis and visualization of missing value patterns

Published in Information Processing and Management of Uncertainty in Knowledge-Based Systems: 16th International Conference, IPMU 2016, Eindhoven, The Netherlands, June 20-24, 2016, Proceedings, Part II 16, 2016

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Recommended citation: Bas Stein, Wojtek Kowalczyk, Thomas B{\"a}ck, "Analysis and visualization of missing value patterns." Information Processing and Management of Uncertainty in Knowledge-Based Systems: 16th International Conference, IPMU 2016, Eindhoven, The Netherlands, June 20-24, 2016, Proceedings, Part II 16, 2016.

Fuzzy clustering for optimally weighted cluster kriging

Published in 2016 IEEE international conference on fuzzy systems (FUZZ-IEEE), 2016

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Recommended citation: Bas Stein, Hao Wang, Wojtek Kowalczyk, Michael Emmerich, Thomas B{\"a}ck, "Fuzzy clustering for optimally weighted cluster kriging." 2016 IEEE international conference on fuzzy systems (FUZZ-IEEE), 2016.

Towards data driven process control in manufacturing car body parts

Published in 2016 International Conference on Computational Science and Computational Intelligence (CSCI), 2016

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Recommended citation: Bas Van, Matthijs Van, Hao Wang, Stephan Purr, Sebastian Kreissl, Josef Meinhardt, Thomas B{\"a}ck, "Towards data driven process control in manufacturing car body parts." 2016 International Conference on Computational Science and Computational Intelligence (CSCI), 2016.

A multi-method simulation of a high-frequency bus line

Published in 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 2017

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Recommended citation: Thierry Spek, Bas Stein, Marcel Holst, Thomas B{\"a}ck, "A multi-method simulation of a high-frequency bus line." 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 2017.

Algorithm configuration data mining for cma evolution strategies

Published in Proceedings of the Genetic and Evolutionary Computation Conference, 2017

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Recommended citation: Sander Rijn, Hao Wang, Bas Stein, Thomas B{\"a}ck, "Algorithm configuration data mining for cma evolution strategies." Proceedings of the Genetic and Evolutionary Computation Conference, 2017.

A novel uncertainty quantification method for efficient global optimization

Published in Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications: 17th International Conference, IPMU 2018, C'adiz, Spain, June 11-15, 2018, Proceedings, Part III 17, 2018

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Recommended citation: Bas Stein, Hao Wang, Wojtek Kowalczyk, Thomas B{\"a}ck, "A novel uncertainty quantification method for efficient global optimization." Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications: 17th International Conference, IPMU 2018, C'adiz, Spain, June 11-15, 2018, Proceedings, Part III 17, 2018.

Designing ships using constrained multi-objective efficient global optimization

Published in Machine Learning, Optimization, and Data Science: 4th International Conference, LOD 2018, Volterra, Italy, September 13-16, 2018, Revised Selected Papers 4, 2019

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Recommended citation: Roy Winter, Bas Stein, Matthys Dijkman, Thomas B{\"a}ck, "Designing ships using constrained multi-objective efficient global optimization." Machine Learning, Optimization, and Data Science: 4th International Conference, LOD 2018, Volterra, Italy, September 13-16, 2018, Revised Selected Papers 4, 2019.

Scalability of learning tasks on 3D CAE models using point cloud autoencoders

Published in 2019 IEEE Symposium Series on Computational Intelligence (SSCI), 2019

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Recommended citation: Thiago Rios, Patricia Wollstadt, Bas Stein, Thomas B{\"a}ck, Zhao Xu, Bernhard Sendhoff, Stefan Menzel, "Scalability of learning tasks on 3D CAE models using point cloud autoencoders." 2019 IEEE Symposium Series on Computational Intelligence (SSCI), 2019.

Back to meshes: Optimal simulation-ready mesh prototypes for autoencoder-based 3D car point clouds

Published in 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020

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Recommended citation: Thiago Rios, Jiawen Kong, Bas Stein, Thomas B{\"a}ck, Patricia Wollstadt, Bernhard Sendhoff, Stefan Menzel, "Back to meshes: Optimal simulation-ready mesh prototypes for autoencoder-based 3D car point clouds." 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020.

Feature visualization for 3D point cloud autoencoders

Published in 2020 International Joint Conference on Neural Networks (IJCNN), 2020

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Recommended citation: Thiago Rios, Bas Stein, Stefan Menzel, Thomas Back, Bernhard Sendhoff, Patricia Wollstadt, "Feature visualization for 3D point cloud autoencoders." 2020 International Joint Conference on Neural Networks (IJCNN), 2020.

Improving NSGA-III for flexible job shop scheduling using automatic configuration, smart initialization and local search

Published in Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, 2020

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Recommended citation: Yali Wang, Bas Stein, Thomas B{\"a}ck, Michael Emmerich, "Improving NSGA-III for flexible job shop scheduling using automatic configuration, smart initialization and local search." Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, 2020.

Emergence of structural bias in differential evolution

Published in Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2021

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Recommended citation: Bas Stein, Fabio Caraffini, Anna Kononova, "Emergence of structural bias in differential evolution." Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2021.

Exploiting generative models for performance predictions of 3D car designs

Published in 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 2021

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Recommended citation: Sneha Saha, Thiago Rios, Leandro Minku, Bas Stein, Patricia Wollstadt, Xin Yao, Thomas Back, Bernhard Sendhoff, Stefan Menzel, "Exploiting generative models for performance predictions of 3D car designs." 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 2021.

Optimally weighted ensembles for efficient multi-objective optimization

Published in International Conference on Machine Learning, Optimization, and Data Science. Springer, 2021

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Recommended citation: Gideon Hanse, Roy Winter, Bas Stein, Thomas B{\"a}ck, "Optimally weighted ensembles for efficient multi-objective optimization." International Conference on Machine Learning, Optimization, and Data Science. Springer, 2021.

Samo-cobra: A fast surrogate assisted constrained multi-objective optimization algorithm

Published in Evolutionary Multi-Criterion Optimization: 11th International Conference, EMO 2021, Shenzhen, China, March 28--31, 2021, Proceedings 11, 2021

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Recommended citation: Roy Winter, Bas Stein, Thomas B{\"a}ck, "Samo-cobra: A fast surrogate assisted constrained multi-objective optimization algorithm." Evolutionary Multi-Criterion Optimization: 11th International Conference, EMO 2021, Shenzhen, China, March 28--31, 2021, Proceedings 11, 2021.

Learning the characteristics of engineering optimization problems with applications in automotive crash

Published in Proceedings of the Genetic and Evolutionary Computation Conference, 2022

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Recommended citation: Fu Long, Bas Stein, Moritz Frenzel, Peter Krause, Markus Gitterle, Thomas B{\"a}ck, "Learning the characteristics of engineering optimization problems with applications in automotive crash." Proceedings of the Genetic and Evolutionary Computation Conference, 2022.

Using structural bias to analyse the behaviour of modular CMA-ES

Published in Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2022

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Recommended citation: Diederick Vermetten, Fabio Caraffini, Bas Stein, Anna Kononova, "Using structural bias to analyse the behaviour of modular CMA-ES." Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2022.

BBOB Instance Analysis: Landscape Properties and Algorithm Performance Across Problem Instances

Published in International Conference on the Applications of Evolutionary Computation (Part of EvoStar), 2023

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Recommended citation: Fu Long, Diederick Vermetten, Bas Stein, Anna Kononova, "BBOB Instance Analysis: Landscape Properties and Algorithm Performance Across Problem Instances." International Conference on the Applications of Evolutionary Computation (Part of EvoStar), 2023.

A Functional Analysis Approach to Symbolic Regression

Published in Preprint on arXiv preprint arXiv:2402.06299, 2024

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Recommended citation: Kirill Antonov, Roman Kalkreuth, Kaifeng Yang, Thomas B{\"a}ck, Niki Stein, Anna Kononova, "A Functional Analysis Approach to Symbolic Regression." Preprint on arXiv preprint arXiv:2402.06299, 2024.

talks

Optimally Weighted Cluster Kriging

Published:

In business and academia we are continuously trying to model and analyze complex processes in order to gain insight and optimize. One of the most popular modeling algorithms is Kriging, or Gaussian Processes. A major bottleneck with Kriging is the amount of processing time of at least O(n3) and memory required O(n2) when applying this algorithm on medium to big data sets. With big data sets, that are more and more available these days, Kriging is not computationally feasible. As a solution to this problem we introduce a hybrid approach in which a number of Kriging models built on disjoint subsets of the data are properly weighted for the predictions. The proposed model is both in processing time and memory much more efficient than standard Global Kriging and performs equally well in terms of accuracy. The proposed algorithm is better scalable, and well suited for parallelization.

Datamining & Apps

Published:

Invited lecture at the Data Science course, LIACS, Leiden University

Deep Learning, A broad introduction

Published:

Invited talk for the Summer school for Early Stage Researchers. This summer school takes place within the EU project Experience-based Computation: Learning to Optimise (ECOLE), which investigates novel synergies between nature-inspired optimisation and machine learning to address key challenges faced by the European industry.

AI for Expensive Optimization Problems in Industry

Published:

The optimization of real-world engineering problems can be a challenging task, due to the limited understanding of problem characteristics and the high computational cost of objectives and constraints. This study proposes an AI-assisted optimization pipeline that addresses these challenges by using proxy functions in order to select and optimize an optimization algorithm and its hyper-parameters. It thereby significantly accelerates the optimization process on the real (expensive) problem. To obtain such proxy functions Exploratory Landscape Analysis (ELA) features are used to characterize the problem’s landscape. The ELA features are then used to identify an artificial function that replicates the original problem’s properties.

teaching

Data Science course

Course, Bsc Computer Science, Leiden University, 2022

Data Science places data mining, machine learning and statistics in context, both experimentally and socially. If you want to correctly deploy data mining techniques, you must be able to translate a (broadly formulated) question by a customer or a co-worker into an experimental set-up, to make the right choices for the methods you use, and to be able to process the data in the right form to apply those methods. After performing your experiments, you should not only be able to evaluate the results but also interpret and translate it back to the original question (e.g. by visualization). Socially, data science is of great importance because the media simplify many data-driven results and statistical research, often making mistakes. Thus, a lot of nonsense comes down on us and it is up to you, the data scientists of the future, to recognize, explain and correct that nonsense. This course is a combination of lectures and practical sessions, in which you take a hands-on approach to solving real-world data science problems.

Master Class

Course, Msc Computer Science, Leiden University, 2022

The Master Class for Computer Science students takes place every second week and is mandatory for all students who are in their second year, i.e., in their research year (Specialisation courses and the Master’s Thesis Research Project). The Master Class aims at stimulating active interaction of students with their classmates, discussing open problems, issues, etc., and helping students to stay on track. Each student is asked to give a brief presentation in the Master Class about their Master’s Thesis Research Project. About the research topic and goals, the status and (expected) results. In addition, we will discuss topics such as the structure of a Master’s Thesis, writing scientific publications, presenting a scientific paper, time management and other soft skills. In addition to these there are a number of guest lectures from companies, PhD students and entrepeneurs to prepare you for the job market after you graduate.