ADA research group

Publications

2024

Julian Dierkes, Emma Cramer, Holger Hoos and Sebastian Trimpe.
Combining Automated Optimisation of Hyperparameters and Reward Shape.
Reinforcement Learning Journal, 3:1441–1466, 2024.
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Matthias König, Xiyue Zhang, Holger H Hoos, Marta Kwiatkowska and Jan N van Rijn.
Automated Design of Linear Bounding Functions for Sigmoidal Nonlinearities in Neural Networks.
In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. 2024.
[ PDF ∙ BibTeX ]
Matt van den Nieuwenhuijzen, Carola Doerr, Henry Gouk and Jan N. van Rijn.
Selecting Pre-trained Models for Transfer Learning with Data-centric Meta-features.
In AutoML Conference 2024 (Workshop Track). 2024.
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Laurens Arp, Holger Hoos, Peter van Bodegom, Alistair Francis, James Wheeler, Dean van Laar and Mitra Baratchi.
Training-free thick cloud removal for Sentinel-2 imagery using value propagation interpolation.
ISPRS Journal of Photogrammetry and Remote Sensing, 216:168–184, 2024.
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Hadar Shavit and Holger H. Hoos.
Revisiting SATZilla Features in 2024.
In Proceedings of the 27th International Conference on Theory and Applications of Satisfiability Testing (SAT), 1–10. 2024.
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Andreas Paraskeva, Joao Pedro Reis, Suzan Verberne and Jan N. van Rijn.
Resource-constrained Neural Architecture Search on Language Models: A Case Study.
In 2nd Workshop on Advancing Neural Network Training: Computational Efficiency, Scalability, and Resource Optimization (WANT@ICML 2024). 2024.
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Annelot W. Bosman, Anna L. Münz, Holger H. Hoos and Jan N. van Rijn.
A Preliminary Study to Examining Per-Class Performance Bias via Robustness Distributions.
In The 7th International Symposium on AI Verification (SAIV) co-located with the 36th International Conference on Computer Aided Verification (CAV 2024). 2024.
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Julia Wąsala, Suzanne Marselis, Laurens Arp, Holger Hoos, Nicolas Longépé and Mitra Baratchi.
AutoSR4EO: An AutoML Approach to Super-Resolution for Earth Observation Images.
Remote Sensing, 2024.
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Matthias König, Annelot W Bosman, Holger H Hoos and Jan N van Rijn.
Critically Assessing the State of the Art in Neural Network Verification.
Journal of Machine Learning Research, 25(12):1–53, 2024.
[ PDF ∙ BibTeX ]
Matthias König, Holger H Hoos and Jan N van Rijn.
Accelerating Adversarially Robust Model Selection for Deep Neural Networks via Racing.
In Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI-24). 2024.
[ PDF ∙ BibTeX ]

2023

Reyhan Aydoğan, Tim Baarslag, Katsuhide Fujita, Holger H. Hoos, Catholijn M. Jonker, Yasser Mohammad and Bram M. Renting.
The 13th International Automated Negotiating Agent Competition Challenges and Results.
In Rafik Hadfi, Reyhan Aydoğan, Takayuki Ito and Ryuta Arisaka, editors, Recent Advances in Agent-Based Negotiation: Applications and Competition Challenges, Studies in Computational Intelligence, 87–101. Singapore, 2023. Springer Nature.
[ PDF ∙ BibTeX ]
Thomas M. Moerland, Matthias Müller-Brockhausen, Zhao Yang, Andrius Bernatavicius, Koen Ponse, Tom Kouwenhoven, Andreas Sauter, Michiel van der Meer, Bram Renting and Aske Plaat.
EduGym: An Environment Suite for Reinforcement Learning Education.
November 2023. arXiv:2311.10590 [cs, stat].
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Zhou Zhou, Mitra Baratchi, Gangquan Si, Holger H. Hoos and Gang Huang.
Adaptive error bounded piecewise linear approximation for time-series representation.
Engineering Applications of Artificial Intelligence, 126:106892, 2023.
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Lincen Yang, Mitra Baratchi and Matthijs van Leeuwen.
Unsupervised discretization by two-dimensional mdl-based histogram.
Machine Learning, 2023.
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Thalea Schlender, Markus Viljanen, Jan N. van Rijn, Felix Mohr, Willie J.G.M. Peijnenburg, Holger H. Hoos, Emiel Rorije and Albert Wong.
The Bigger Fish: A Comparison of Meta-Learning QSAR Models on Low-Resourced Aquatic Toxicity Regression Tasks.
Environmental Science & Technology, 57(46):17818–17830, 2023.
[ PDF ∙ BibTeX ]
Samira Rezaei and Mitra Baratchi.
AutoML to generalize strong gravitational lens modeling problem.
In Neuro-Explicit AI and Expert-informed Machine Learning for Engineering and Physical Sciences Workshop colocated with ECML-PKDD 2023. 2023.
Maedeh Nasri, Mitra Baratchi, Yung-Ting Tsou, Sarah Giest, Alexander Koutamanis and Carolien Rieffe.
A novel metric to measure spatio-temporal proximity: a case study analyzing children’s social network in schoolyards.
Applied Network Science, 8(1):50–67, 2023.
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Maedeh Nasri, Zhizhou Fang, Mitra Baratchi, Gwenn Englebienne, Shenghui Wang, Alexander Koutamanis and Carolien Rieffe.
A GNN-based Architecture for Group Detection from spatio-temporal Trajectory Data.
In Proceedings of the 21th International Symposium on Intelligent Data Analysis (IDA 2023). 2023.
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Charles Moussa, Yash J. Patel, Vedran Dunjko, Thomas Bäck and van Rijn Jan N.
Hyperparameter Importance and Optimization of Quantum Neural Networks Across Small Datasets.
Machine Learning, 2023. accepted.
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Felix Mohr and Jan Nicolaas van Rijn.
Fast and Informative Model Selection using Learning Curve Cross-Validation.
IEEE Transactions on Pattern Analysis & Machine Intelligence, 45(8):9669–9680, 2023.
Mike Huisman, Aske Plaat and Jan N. van Rijn.
Subspace Adaptation Prior for Few-Shot Learning.
Machine Learning, 2023.
[ PDF ∙ BibTeX ]
Mike Huisman, Thomas M. Moerland, Aske Plaat and Jan N. van Rijn.
Are LSTMs good few-shot learners?.
Machine Learning, 112(11):4635–4662, 2023.
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Mike Huisman, Aske Plaat and Jan N. van Rijn.
Understanding transfer learning and gradient-based meta-learning techniques.
Machine Learning, 2023.
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Adva Eichengreen, Martin van Rooijen, Lisa-Maria van Klaveren, Maedeh Nasri, Yung-Ting Tsou, Alexander Koutamanis, Mitra Baratchi and Carolien Rieffe.
The impact of loose-parts-play on schoolyard social participation of children with and without disabilities: A case study.
Child: Care, Health and Development, 2023.
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Adva Eichengreen, Yung-Ting Tsou, Maedeh Nasri, Lisa-Maria van Klaveren, Boya Li, Alexander Koutamanis, Mitra Baratchi, Els Blijd-Hoogewys, Joost Kok and Carolien Rieffe.
Social connectedness at the playground before and after COVID-19 school closure.
Journal of Applied Developmental Psychology, 87:101562, 2023.
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Alex Serban, Koen van der Blom, Holger Hoos and Joost Visser.
Software engineering practices for machine learning - Adoption, effects, and team assessment.
Journal of Systems and Software, pages 111907, 2023.
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Lennart Purucker, Lennart Schneider, Marie Anastacio, Joeran Beel, Bernd Bischl and Holger Hoos.
Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML.
In Second Conference on Automated Machine Learning (Main Track). 2023.
[ PDF ∙ BibTeX ]
Annelot W. Bosman, Holger H. Hoos and Jan N. van Rijn.
A Preliminary Study of Critical Robustness Distributions in Neural Network Verification.
6th Workshop on Formal Methods for ML-Enabled Autonomous Systems (FoMLAS) co-located with the 35th International Conference on Computer Aided Verification (CAV 2023), 2023.
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Hadar Shavit, Filip Jatelnicki, Pol Mor-Puigventós and Wojtek Kowalczyk.
From Xception to NEXcepTion: New Design Decisions and Neural Architecture Search.
In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2023). 2023.
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Jonathan Heins, Jakob Bossek, Janina Pohl, Moritz Seiler, Heike Trautmann and Pascal Kerschke.
A Study on the Effects of Normalized TSP Features for Automated Algorithm Selection.
Theoretical Computer Science, 940:123–145, 2023.
[ PDF ∙ BibTeX ]
Matthias König, Annelot W Bosman, Holger H Hoos and Jan N van Rijn.
Critically Assessing the State of the Art in CPU-based Local Robustness Verification.
In Proceedings of the Workshop on Artificial Intelligence Safety 2023 (SafeAI 2023) co-located with the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI2023). 2023. This paper won the SafeAI 2023 Best Paper Award.
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2022

Bram M. Renting, Holger H. Hoos and Catholijn M. Jonker.
Automated Configuration and Usage of Strategy Portfolios for Mixed-Motive Bargaining.
In Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, AAMAS '22, 1101–1109. Richland, SC, May 2022. International Foundation for Autonomous Agents and Multiagent Systems.
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Ihsan Ullah, Dustin Carrión-Ojeda, Sergio Escalera, Isabelle Guyon, Mike Huisman, Felix Mohr, Jan Nicolaas van Rijn, Haozhe Sun, Joaquin Vanschoren and Phan Anh Vu.
Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification.
In Advances in Neural Information Processing Systems, volume 35, 3232–3247. Curran Associates, Inc., 2022.
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Jaco Tetteroo, Mitra Baratchi and Holger H. Hoos.
Automated Machine Learning for COVID-19 Forecasting.
IEEE Access, 10:94718–94737, 2022.
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Nuno Cesar de Sa, Mitra Baratchi, Vincent Buitenhuis, Perry Cornelissen and Peter M. van Bodegom.
AutoML for estimating grass height from ETM+/OLI data from field measurements at a nature reserve.
GIScience & Remote Sensing, 59(1):2164–2183, 2022.
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Hossein A. Rahmani, Mohammad Aliannejadi, Mitra Baratchi and Fabio Crestani.
A Systematic Analysis on the Impact of Contextual Information on Point-of-Interest Recommendation.
ACM Transactions on Information Systems, 2022.
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Maedeh Nasri, Yung-Ting Tsou, Alexander Koutamanis, Mitra Baratchi, Sarah Giest, Dennis Reidsma and Carolien Rieffe.
A Novel Data-driven Approach to Examine Children's Movements and Social Behaviour in Schoolyard Environments.
Children, 2022.
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Charles Moussa, Jan N van Rijn, Thomas Bäck and Vedran Dunjko.
Hyperparameter importance of quantum neural networks across small datasets.
In Discovery Science: 25th International Conference, volume 13601 of Lecture Notes in Computer Science, 32–46. Springer, 2022.
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Felix Mohr, Tom J Viering, Marco Loog and Jan N van Rijn.
LCDB 1.0: An extensive learning curves database for classification tasks.
In Machine Learning and Knowledge Discovery in Databases (ECML PKDD), volume 13717 of Lecture Notes in Computer Science, 3–19. Springer, 2022.
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Rodi Laanen, Maedeh Nasri, Richard van Dijk, Mitra Baratchi, Alexander Koutamanis and Carolien Rieffe.
Automated classification of pre-defined movement patterns: A comparison between GNSS and UWB technology.
In Online Proceedings of BNAIC/BeNeLearn. 2022.
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Marios Kefalas, Bas van Stein, Mitra Baratchi, Asteris Apostolidis and Thomas Bäck.
An end-to-end pipeline for uncertainty quantification and remaining useful life estimation: An application on aircraft engines.
In Proceedings of PHM Society European Conference, volume 7, 245–260. 2022.
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Mike Huisman, Aske Plaat and Jan Nicolaas van Rijn.
Stateless neural meta-learning using second-order gradients.
Machine Learning, 111(9):3227–3244, 2022.
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Adrian El Baz, Ihsan Ullah, Edesio Alcobaça, André C. P. L. F. de Carvalho, Hong Chen, Fabio Ferreira, Henry Gouk, Chaoyu Guan, Isabelle Guyon, Timothy M. Hospedales, Shell Hu, Mike Huisman, Frank Hutter, Zhengying Liu, Felix Mohr, Ekrem Öztürk, Jan Nicolaas van Rijn, Haozhe Sun, Xin Wang and Wenwu Zhu.
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification.
In Douwe Kiela, Marco Ciccone and Barbara Caputo, editors, NeurIPS 2021 Competitions and Demonstrations Track, volume 176 of Proceedings of Machine Learning Research, 80–96. PMLR, 2022.
Pavel Brazdil, Jan Nicolaas van Rijn, Henry Gouk and Felix Mohr.
Advances in Metalearning: ECML/PKDD Workshop on Meta-Knowledge Transfer.
In ECMLPKDD Workshop on Meta-Knowledge Transfer, volume 191 of Proceedings of Machine Learning Research, 1–7. PMLR, 23 Sep 2022.
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P Brazdil, Jan Nicolaas van Rijn, Carlos Soares and Joaquin Vanschoren. Metalearning: Applications to Automated Machine Learning and Data Mining. Springer, 2nd edition, 2022.
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Jakob Bossek and Frank Neumann.
Exploring the Feature Space of TSP Instances Using Quality Diversity.
In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO '22, 186–194. New York, NY, USA, 2022. Association for Computing Machinery.
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Lena Clever, Janina Susanne Pohl, Jakob Bossek, Pascal Kerschke and Heike Trautmann.
Process-Oriented Stream Classification Pipeline: A Literature Review.
Applied Sciences, 12(18):9094, 2022.
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Jonathan Heins, Jeroen Rook, Lennart Schäpermeier, Pascal Kerschke, Jakob Bossek and Heike Trautmann.
BBE: Basin-Based Evaluation oftextasciitilde Multimodal Multi-objective Optimization Problems.
In Günter Rudolph, Anna V. Kononova, Hernán Aguirre, Pascal Kerschke, Gabriela Ochoa and Tea Tušar, editors, Proceedings of the Parallel Problem Solving from Nature textendash PPSN XVII, Lecture Notes in Computer Science, 192–206. Cham, 2022. Springer International Publishing.
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Adel Nikfarjam, Aneta Neumann, Jakob Bossek and Frank Neumann.
Co-Evolutionary Diversity Optimisation fortextasciitilde thetextasciitilde Traveling Thief Problem.
In Günter Rudolph, Anna V. Kononova, Hernán Aguirre, Pascal Kerschke, Gabriela Ochoa and Tea Tušar, editors, Parallel Problem Solving from Nature textendash PPSN XVII, Lecture Notes in Computer Science, 237–249. Cham, 2022. Springer International Publishing.
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Jeroen Rook, Heike Trautmann, Jakob Bossek and Christian Grimme.
On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems.
In Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO '22, 356–359. New York, NY, USA, 2022. Association for Computing Machinery.
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Matthias König, Holger H Hoos and Jan N van Rijn.
Speeding up neural network robustness verification via algorithm configuration and an optimised mixed integer linear programming solver portfolio.
Machine Learning, pages 1–20, 2022.
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Anna L.D. Latour, Behrouz Babaki, Daniël Fokkinga, Marie Anastacio, Holger H. Hoos and Siegfried Nijssen.
Exact stochastic constraint optimisation with applications in network analysis.
Artificial Intelligence, 304:103650, 2022.
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Laurens Arp, Mitra Baratchi and Holger Hoos.
VPint: value propagation-based spatial interpolation.
Data Mining and Knowledge Discovery, 36:, 2022.
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Damir Pulatov, Marie Anastacio, Lars Kotthoff and Holger Hoos.
Opening the Black Box: Automated Software Analysis for Algorithm Selection.
In First Conference on Automated Machine Learning (Main Track). 2022.
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Anna L. D. Latour, Behrouz Babaki, Daniël Fokkinga, Marie Anastacio, Holger H. Hoos and Siegfried Nijssen.
Stochastic Constraint Optimisation with Applications in Network Analysis (extended abstract).
In Workshop on Counting and Sampling 2022, in conjunction with FLoC 2022 and SAT 2022. aug 2022.
[ PDF ∙ BibTeX ]

2021

Bram M. Renting, Holger H. Hoos and Catholijn M. Jonker.
Automated Configuration and Usage of Strategy Portfolios for Bargaining.
In Cooperative AI workshop @ NeurIPS 2021 (non-archival). arXiv, December 2021.
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Chitsutha Soomlek, Jan Nicolaas van Rijn and Marcello M. Bonsangue.
Automatic Human-Like Detection of Code Smells.
In Carlos Soares and Lu\'ıs Torgo, editors, Discovery Science, volume 12986 of Lecture Notes in Computer Science, 19–28. Springer, 2021.
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Florian Pfisterer, Jan Nicolaas van Rijn, Philipp Probst, Andreas Müller and Bernd Bischl.
Learning Multiple Defaults for Machine Learning Algorithms.
In GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference. 2021.
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Felix Mohr and Jan Nicolaas van Rijn.
Towards Model Selection using Learning Curve Cross-Validation.
In 8th ICML Workshop on Automated Machine Learning (AutoML). 2021.
Mike Huisman, Jan Nicolaas van Rijn and Aske Plaat.
A preliminary study on the feature representations of transfer learning and gradient-based meta-learning techniques.
In Fifth Workshop on Meta-Learning at the Conference on Neural Information Processing Systems. 2021.
Pieter Gijsbers, Florian Pfisterer, Jan Nicolaas van Rijn, Bernd Bischl and Joaquin Vanschoren.
Meta-learning for symbolic hyperparameter defaults.
In GECCO '21: Genetic and Evolutionary Computation Conference, 151–152. ACM, 2021.
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Isabelle Guyon, Jan Nicolaas van Rijn, Sébastien Treguer and Joaquin Vanschoren, editors. AAAI Workshop on Meta-Learning and MetaDL Challenge. Volume 140 of Proceedings of Machine Learning Research. PMLR, 2021.
B. Bischl, G. Casalicchio, M. Feurer, F. Hutter, M. Lang, R. G. Mantovani, J. N. van Rijn and J. Vanschoren.
OpenML Benchmarking Suites.
In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks, NIPS'21. 2021.
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Holger H Hoos, Frank Hutter and Kevin Leyton-Brown.
Automated Configuration and Selection of SAT Solvers.
In Handbook of Satisfiability, pages 481–507. IOS Press, 2021.
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Koen van der Blom, Alex Serban, Holger Hoos and Joost Visser.
AutoML Adoption in ML Software.
In 8th ICML Workshop on Automated Machine Learning. 2021.
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Bruno Veloso, Luciano Caroprese, Matthias König, Sónia Teixeira, Giuseppe Manco, Holger H Hoos and Jo~ao Gama.
Hyper-Parameter Optimization for Latent Spaces in Dynamic Recommender Systems.
In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. 2021.
[ PDF ∙ BibTeX ∙ GitHub ∙ poster ]
Nelly Rosaura Palacios Salinas, Mitra Baratchi, Jan N. van Rijn and Andreas Vollrath.
Automated Machine Learning for Satellite Data: Integrating Remote Sensing Pre-trained Models into AutoML Systems.
In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. 2021.
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Gilles Ottervanger, Mitra Baratchi and Holger H Hoos.
MultiETSC: automated machine learning for early time series classification.
Data Mining and Knowledge Discovery, 35(6):2602–2654, 2021.
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Matthias König, Holger H Hoos and Jan N van Rijn.
Speeding Up Neural Network Verification via Automated Algorithm Configuration.
In ICLR Workshop on Security and Safety in Machine Learning Systems. 2021.
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Wessel A. van Eeden, Chuan Luo, Albert M. van Hemert, Ingrid V.E. Carlier, Brenda W. Penninx, Klaas J. Wardenaar, Holger Hoos and Erik J. Giltay.
Predicting the 9-year course of mood and anxiety disorders with automated machine learning: A comparison between auto-sklearn, naïve Bayes classifier, and traditional logistic regression.
Psychiatry Research, 299:113823, 2021.
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Andreas Dengel, Oren Etzioni, Nicole DeCario, Holger Hoos, Fei-Fei Li, Junichi Tsujii and Paolo Traverso. Next Big Challenges in Core AI Technology, pages 90–115. Springer International Publishing, Cham, 2021.
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Alex Serban, Koen van der Blom, Holger Hoos and Joost Visser.
Practices for Engineering Trustworthy Machine Learning Applications.
In IEEE/ACM 1st Workshop on AI Engineering-Software Engineering for AI (WAIN), 97–100. IEEE, 2021.
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Nuno César de Sá, Mitra Baratchi, Leon T. Hauser and Peter van Bodegom.
Exploring the Impact of Noise on Hybrid Inversion of PROSAIL RTM on Sentinel-2 Data.
Remote Sensing, 2021.
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Sjonnie Boonstra, Koen van der Blom, Hèrm Hofmeyer and Michael T. M. Emmerich.
Hybridization of an evolutionary algorithm and simulations of co-evolutionary design processes for early-stage building spatial design optimization.
Automation in Construction, 124:103522, 2021.
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Mike Huisman, Jan N. van Rijn and Aske Plaat.
A Survey of Deep Meta-Learning.
Artificial Intelligence Review, pages 1–59, 2021.
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Matthias Feurer, Jan N. van Rijn, Arlind Kadra, Pieter Gijsbers, Neeratyoy Mallik, Sahithya Ravi, Andreas Müller, Joaquin Vanschoren and Frank Hutter.
OpenML-Python: an extensible Python API for OpenML.
Journal of Machine Learning Research, 22(100):1–5, 2021.
[ PDF ∙ BibTeX ∙ citations ] Highly cited (68.80 citations/year, 258 overall)
Marie Anastacio.
Greybox Algorithm Configuration.
In Zhi-Hua Zhou, editor, Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, Virtual Event / Montreal, Canada, 19-27 August 2021, 4875–4876. ijcai.org, 2021.
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Théo Matricon, Marie Anastacio, Nathanaël Fijalkow, Laurent Simon and Holger H. Hoos.
Statistical Comparison of Algorithm Performance Through Instance Selection.
In Laurent D. Michel, editor, 27th International Conference on Principles and Practice of Constraint Programming, CP 2021, Montpellier, France (Virtual Conference), October 25-29, 2021, volume 210 of LIPIcs, 43:1–43:21. Schloss Dagstuhl - Leibniz-Zentrum f"ur Informatik, 2021.
[ PDF ∙ BibTeX ∙ GitHub ]

2020

Bram M. Renting, Holger H. Hoos and Catholijn M. Jonker.
Automated Configuration of Negotiation Strategies.
In Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems, AAMAS '20, 1116–1124. Auckland, May 2020. International Foundation for Autonomous Agents and Multiagent Systems.
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Anna L. D. Latour, Behrouz Babaki, Daniël Fokkinga, Marie Anastacio, Holger H. Hoos and Siegfried Nijssen.
Stochastic Constraint Optimisation with Applications in Network Analysis (extended abstract).
In International Workshop on Model Counting (MCW), in conjunction with SAT 2020. jul 2020.
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Chuan Luo, Holger Hoos and Shaowei Cai.
PbO-CCSAT: Boosting Local Search for Satisfiability Using Programming by Optimisation.
In Thomas Bäck, Mike Preuss, André Deutz, Hao Wang, Carola Doerr, Michael Emmerich and Heike Trautmann, editors, Parallel Problem Solving from Nature – PPSN XVI, 373–389. Cham, 2020. Springer International Publishing.
Z. Akata, D. Balliet, M. de Rijke, F. Dignum, V. Dignum, G. Eiben, A. Fokkens, D. Grossi, K. Hindriks, H. Hoos, H. Hung, C. Jonker, C. Monz, M. Neerincx, F. Oliehoek, H. Prakken, S. Schlobach, L. van der Gaag, F. van Harmelen, H. van Hoof, B. van Riemsdijk, A. van Wynsberghe, R. Verbrugge, B. Verheij, P. Vossen and M. Welling.
A Research Agenda for Hybrid Intelligence: Augmenting Human Intellect With Collaborative, Adaptive, Responsible, and Explainable Artificial Intelligence.
Computer, 53(8):18–28, 2020.
[ PDF ∙ BibTeX ∙ citations ] Highly cited (18.53 citations/year, 88 overall)
Yasha Pushak and Holger H. Hoos.
Golden Parameter Search: Exploiting Structure to Quickly Configure Parameters in Parallel.
In Proceedings of the 2020 Genetic and Evolutionary Computation Conference, GECCO '20, 245–253. New York, NY, USA, 2020. Association for Computing Machinery.
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Yasha Pushak and Holger H. Hoos.
Advanced Statistical Analysis of Empirical Performance Scaling.
In Proceedings of the 2020 Genetic and Evolutionary Computation Conference, GECCO '20, 236–244. New York, NY, USA, 2020. Association for Computing Machinery.
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Lindsey Burggraaff, Eelke B. Lenselink, Willem Jespers, Jesper van Engelen, Brandon J. Bongers, Marina Gorostiola González, Rongfang Liu, Holger H. Hoos, Herman W. T. van Vlijmen, Adriaan P. IJzerman and Gerard J. P. van Westen.
Successive Statistical and Structure-Based Modeling to Identify Chemically Novel Kinase Inhibitors.
Journal of Chemical Information and Modeling, 60(9):4283–4295, 2020. PMID: 32343143.
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Yasha Pushak, Zongxu Mu and Holger H Hoos.
Empirical scaling analyzer: An automated system for empirical analysis of performance scaling.
AI Communications, pages 1–19, 2020.
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Sam Bayless, Nodir Kodirov, Syed M. Iqbal, Ivan Beschastnikh, Holger H. Hoos and Alan J. Hu.
Scalable constraint-based virtual data center allocation.
Artificial Intelligence, 278:103196, 2020.
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Jan H. van Staalduinen, Jaco Tetteroo, Daniela Gawehns and Mitra Baratchi.
An Intelligent Tree Planning Approach Using Location-based Social Networks Data.
In Pre-proceedings of BNAIC/BeneLearn 2020, 284–297. 2020.
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Matthias König, Holger H Hoos and Jan N van Rijn.
Towards Algorithm-Agnostic Uncertainty Estimation: Predicting Classification Error in an Automated Machine Learning Setting.
In ICML Workshop on Automated Machine Learning. 2020.
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Koen van der Blom, Timo M. Deist, Tea Tušar, Mariapia Marchi, Yusuke Nojima, Akira Oyama, Vanessa Volz and Boris Naujoks.
Towards Realistic Optimization Benchmarks: A Questionnaire on the Properties of Real-World Problems.
In Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, GECCO '20, 293–294. New York, NY, USA, 2020. ACM.
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Jeroen G. Rook, Anna L. D. Latour, Siegfried Nijssen and Holger H. Hoos.
Better Caching for Better Model Counting.
In International Workshop on Model Counting (MCW), in conjunction with SAT 2020. jul 2020.
[ PDF ∙ BibTeX ]
Laurens Arp, Dyon van Vreumingen, Daniela Gawehns and Mitra Baratchi.
Dynamic macro scale traffic flow optimisation using crowd-sourced urban movement data.
In 2020 21st IEEE International Conference on Mobile Data Management (MDM), 168–177. IEEE, 2020.
[ PDF ∙ BibTeX ∙ citations ]
Joao Mendes-Moreira and Mitra Baratchi.
Reconciling predictions in the regression setting: an application to bus travel time prediction.
In International Symposium on Intelligent Data Analysis, 313–325. Springer, 2020.
[ PDF ∙ BibTeX ]
Alex Serban, Koen van der Blom, Holger Hoos and Joost Visser.
Adoption and Effects of Software Engineering Best Practices in Machine Learning.
In Proceedings of the 14th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), ESEM '20. New York, NY, USA, 2020. ACM.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (10.53 citations/year, 50 overall)
Marie Anastacio and Holger Hoos.
Model-Based Algorithm Configuration with Default-Guided Probabilistic Sampling.
In Proceedings of the 16th International Conference on Parallel Problem Solving from Nature (PPSN-20). 2020.
[ PDF ∙ BibTeX ]
Marie Anastacio and Holger Hoos.
Combining Sequential Model-Based Algorithm Configuration with Default-Guided Probabilistic Sampling.
In Genetic and Evolutionary Computation Conference Companion (GECCO '20 Companion), July 8–12, 2020, Cancún, Mexico. ACM, 2020.
[ PDF ∙ BibTeX ]
Jeannette Shijie Ma, Marcello A Gómez Maureira and Jan N van Rijn.
Eating Sound Dataset for 20 Food Types and Sound Classification Using Convolutional Neural Networks.
In Companion Publication of the 2020 International Conference on Multimodal Interaction, 348–351. 2020.
[ PDF ∙ BibTeX ]
Sjonnie Boonstra, Koen van der Blom, Hèrm Hofmeyer and Michael T. M. Emmerich.
Conceptual Structural System Layouts via Design Response Grammars and Evolutionary Algorithms.
Automation in Construction, 116:103009, 2020.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Hossein A Rahmani, Mohammad Aliannejadi, Mitra Baratchi and Fabio Crestani.
Joint Geographical and Temporal Modeling based on Matrix Factorization for Point-of-Interest Recommendation.
In Proceedings of 42d European Conference on Information Retrieval. 2020.
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2019

Anna Louise D. Latour, Behrouz Babaki and Siegfried Nijssen.
Stochastic Constraint Propagation for Mining Probabilistic Networks.
In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19, 1137–1145. International Joint Conferences on Artificial Intelligence Organization, 7 2019.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Marius Lindauer, Jan N van Rijn and Lars Kotthoff.
The algorithm selection competitions 2015 and 2017.
Artificial Intelligence, 272:86–100, 2019.
[ PDF ∙ BibTeX ]
Noureddin Sadawi, Ivan Olier, Joaquin Vanschoren, Jan N Van Rijn, Jeremy Besnard, Richard Bickerton, Crina Grosan, Larisa Soldatova and Ross D King.
Multi-task learning with a natural metric for quantitative structure activity relationship learning.
Journal of Cheminformatics, 11(1):68, 2019.
[ PDF ∙ BibTeX ∙ citations ]
Abhinav Sharma, Jan N van Rijn, Frank Hutter and Andreas Müller.
Hyperparameter Importance for Image Classification by Residual Neural Networks.
In International Conference on Discovery Science, 112–126. Springer, 2019.
[ PDF ∙ BibTeX ∙ citations ]
Koen van der Blom, Sjonnie Boonstra, Hèrm Hofmeyer and Michael T. M. Emmerich.
Analysing Optimisation Data for Multicriteria Building Spatial Design.
In Kalyanmoy Deb, Erik Goodman, Carlos A. Coello Coello, Kathrin Klamroth, Kaisa Miettinen, Sanaz Mostaghim and Patrick Reed, editors, Evolutionary Multi-Criterion Optimization, 671–682. Cham, 2019. Springer International Publishing.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Sjonnie Boonstra, Koen van der Blom, Hérm Hofmeyer and Michael T. M. Emmerich.
Co-Evolutionary Design Processes Applied to Building Spatial Design Optimization.
In Advances in Structural and Multidisciplinary Optimization. Proceedings of the 13th World Congress of Structural and Multidisciplinary Optimization (WCSMO13), 110–115. 2019.
[ PDF ∙ BibTeX ∙ citations ]
Can Wang, Thomas Bäck, Holger H. Hoos, Mitra Baratchi, Steffen Limmer and Markus Olhofer.
Automated Machine Learning for Short-term Electric Load Forecasting.
In IEEE Symposium Series on Computational Intelligence, SSCI 2019, Xiamen, China, December 6-9, 2019, 314–321. IEEE, 2019.
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Bernd Meijerink, Mitra Baratchi and Geert Heijenk.
Design & analysis of a distributed routing algorithm towards Internet-wide geocast.
Computer Communications, 146:201 – 218, 2019.
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Juliane Adrian, Martyn Amos, Mitra Baratchi, Mira Beermann, Nikolai Bode, Maik Boltes, Alessandro Corbetta, Guillaume Dezecache, John Drury, Zhijian Fu and others.
A glossary for research on human crowd dynamics.
Collective Dynamics, 4(A19):1–13, 2019.
[ PDF ∙ BibTeX ∙ citations ] Highly cited (8.87 citations/year, 51 overall)
Hossein A Rahmani, Mohammad Aliannejadi, Sajad Ahmadian, Mitra Baratchi, Mohsen Afsharchi and Fabio Crestani.
LGLMF: local geographical based logistic matrix factorization model for POI recommendation.
In proceedings of The 15th Asia Information Retrieval Societies Conference. 2019.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Hossein A. Rahmani, Mohammad Aliannejadi, Rasoul Mirzaei Zadeh, Mitra Baratchi, Mohsen Afsharchi and Fabio Crestani.
Category-Aware Location Embedding for Point-of-Interest Recommendation.
In Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval, ICTIR 19, 173–176. New York, NY, USA, 2019. Association for Computing Machinery.
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Hannes Schwarz, Lars Kotthoff, Holger Hoos, Wolf Fichtner and Valentin Bertsch.
Improving the computational efficiency of stochastic programs using automated algorithm configuration: an application to decentralized energy systems.
Annals of Operations Research, pages 1–22, 01 2019.
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F. Schut, J. N. van Rijn and H. Hoos.
Towards Automated Technical Analysis forForeign Exchange Data.
In Automating Data Science @ ECML/PKDD. 2019.
[ PDF ∙ BibTeX ∙ citations ]
Tijl De Bie, Luc De Raedt, Holger H. Hoos and Padhraic Smyth.
Automating Data Science (Dagstuhl Seminar 18401).
Dagstuhl Reports, 8(9):154–181, 2019.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Lars Kotthoff, Chris Thornton, Holger H. Hoos, Frank Hutter and Kevin Leyton-Brown. Auto-WEKA: Automatic Model Selection and Hyperparameter Optimization in WEKA. The Springer Series on Challenges in Machine Learning. Springer, 2019.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (118.09 citations/year, 679 overall)
Chuan Luo, Holger H. Hoos, Shaowei Cai, Qingwei Lin, Hongyu Zhang and Dongmei Zhang.
Local Search with Efficient Automatic Configuration for Minimum Vertex Cover.
In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10-16, 2019, 1297–1304. 2019.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Camille Pageau, Aymeric Blot, Holger Hoos, Marie-Eléonore Kessaci and Laetitia Jourdan.
A Dynamic Algorithm Framework to Automatically Design a Multi-Objective Local Search.
In ROADEF 2019. Le Havre, France, February 2019.
[ view publication ∙ PDF ∙ BibTeX ]
Jesper E. van Engelen and Holger H. Hoos.
A survey on semi-supervised learning.
Machine Learning, Nov 2019.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (153.04 citations/year, 880 overall)
Camille Pageau, Aymeric Blot, Holger H. Hoos, Marie-Éléonore Kessaci-Marmion and Laetitia Jourdan.
Configuration of a Dynamic MOLS Algorithm for Bi-objective Flowshop Scheduling.
In Proceedings of the 10th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2019), (12 manuscript pages). 2019.
[ PDF ∙ BibTeX ∙ citations ]
Pascal Kerschke, Holger H Hoos, Frank Neumann and Heike Trautmann.
Automated Algorithm Selection: Survey and Perspectives.
Evolutionary Computation, 27:3–45, 2019.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (45.04 citations/year, 259 overall)
Aymeric Blot, Marie-Éléonore Kessaci-Marmion, Laetitia Jourdan and Holger H. Hoos.
Automatic Configuration of Multi-Objective Local Search Algorithms for Permutation Problems.
Evolutionary Computation, pages (24 manuscript pages), 2019.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Marie Anastacio, Chuan Luo and Holger Hoos.
Exploitation of Default Parameter Values in Automated Algorithm Configuration.
In Workshop Data Science meets Optimisation (DSO), IJCAI 2019. aug 2019.
[ PDF ∙ BibTeX ∙ citations ]
Daniël Fokkinga, Anna Louise D. Latour, Marie Anastacio, Siegfried Nijssen and Holger Hoos.
Programming a Stochastic Constraint Optimisation Algorithm, by Optimisation.
In Workshop Data Science meets Optimisation (DSO), IJCAI 2019. aug 2019.
[ PDF ∙ BibTeX ∙ citations ]

2018

Salisu Mamman Abdulrahman, Pavel Brazdil, Jan N van Rijn and J Joaquin Vanschoren.
Speeding up algorithm selection using average ranking and active testing by introducing runtime.
Machine Learning, 107(1):79–108, 2018.
[ PDF ∙ BibTeX ∙ citations ]
Jan N van Rijn, Frank W Takes and Jonathan K Vis.
Computing and Predicting Winning Hands in the Trick-Taking Game of Klaverjas.
In Benelux Conference on Artificial Intelligence, 106–120. Springer, 2018.
[ PDF ∙ BibTeX ]
Jan N. van Rijn and Frank Hutter.
Hyperparameter Importance Across Datasets.
In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2367–2376. 2018.
[ PDF ∙ BibTeX ]
Jan N. van Rijn, Geoffrey Holmes, Bernhard Pfahringer and Joaquin Vanschoren.
The online performance estimation framework: heterogeneous ensemble learning for data streams.
Machine Learning, 107(1):149–176, 2018.
[ PDF ∙ BibTeX ]
Jan N. van Rijn, Florian Pfisterer, Janek Thomas, Andreas Muller, Bernd Bischl and J. Vanschoren.
Meta learning for defaults: symbolic defaults.
In Neural Information Processing Workshop on Meta-Learning. 2018.
[ PDF ∙ BibTeX ]
Zongxu Mu, Jérémie Dubois-Lacoste, Holger H. Hoos and Thomas Stützle.
On the Empirical Scaling of Running Time for Finding Optimal Solutions to the TSP.
Journal of Heuristics, 6:879–898, 2018.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Aymeric Blot, Holger H. Hoos, Marie-Éléonore Kessaci-Marmion and Laetitia Jourdan.
Automatic Configuration of Multi-objective Optimization Algorithms. Impact of Correlation between Objectives.
In Proceedings of the 30th International Conference on Tools with Artificial Intelligence (ICTAI 2018), (8 manuscript pages). 2018.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Julieta Martinez, Shobhit Zakhmi, Holger H. Hoos and James J. Little.
LSQ++: Lower Running Time and Higher Recall in Multi-codebook Quantization.
In Proceedings of the 15th European Conference on Computer Vision (ECCV 2018), 508–523. 2018.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Yasha Pushak and Holger H. Hoos.
Algorithm Configuration Landscapes: More Benign than Expected?.
In Proceedings of the 15th International Conference on Parallel Problem Solving from Nature (PPSN-18), 271–283. 2018. This paper won the PPSN-18 Best Paper Award.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Holger H. Hoos, Tomáš Peitl, Friedrich Slivovsky and Stefan Szeider.
Portfolio-Based Algorithm Selection for Circuit QBFs.
In John Hooker, editor, Proceedings of the 24th International Conference on Principles and Practice of Constraint Programming (CP 2018), 195–209. Springer International Publishing, 2018.
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Nodir Kodirov, Sam Bayless, Fabian Ruffy, Ivan Beschastnikh, Holger H. Hoos and Alan J. Hu.
VNF chain abstraction for cloud service providers (poster abstract).
In Proceedings of the 2018 Symposium on Architectures for Networking and Communications Systems (ANCS-18), 165–166. 2018.
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Nodir Kodirov, Sam Bayless, Fabian Ruffy, Ivan Beschastnikh, Holger H. Hoos and Alan J. Hu.
VNF chain allocation and management at data center scale.
In Proceedings of the 2018 Symposium on Architectures for Networking and Communications Systems (ANCS-18), 125–140. 2018.
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Lars Kotthoff, Alexandre Fréchette, Tomasz P. Michalak, Talal Rahwan, Holger H. Hoos and Kevin Leyton-Brown.
Quantifying Algorithmic Improvements over Time.
In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI-18), 5165–5171. 2018.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Marius Lindauer, Holger Hoos, Frank Hutter and Kevin Leyton-Brown. Selection and Configuration of Parallel Portfolios, pages 583–615. Springer International Publishing, 2018.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Katharina Eggensperger, Marius Thomas Lindauer, Holger H. Hoos, Frank Hutter and Kevin Leyton-Brown.
Efficient Benchmarking of Algorithm Configuration Procedures via Model-Based Surrogates.
Machine Learning, 107:15–41, 2018.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (7.70 citations/year, 52 overall)
Pascal Kerschke, Lars Kotthoff, Jakob Bossek, Holger H. Hoos and Heike Trautmann.
Leveraging TSP Solver Complementarity through Machine Learning.
Evolutionary Computation, 26:597–620, 2018.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (7.85 citations/year, 53 overall)
Benjamin Strang, Peter van der Putten, Jan N. van Rijn and Frank Hutter.
Don't Rule Out Simple Models Prematurely: A Large Scale Benchmark Comparing Linear and Non-linear Classifiers in OpenML.
In International Symposium on Intelligent Data Analysis, 303–315. 2018.
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Cristian Chilipirea, Ciprian Dobre, Mitra Baratchi and Maarten van Steen.
Identifying Movements in Noisy Crowd Analytics Data.
In 19th IEEE International Conference on Mobile Data Management, 515–520. 2018.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Cristian Chilipirea, Ciprian Dobre, Mitra Baratchi and Maarten van Steen.
Identifying Stops and Moves in WiFi Tracking Data.
Sensors, 18(11):4039, 2018.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Andrea F. Bocchese, Chris Fawcett, Mauro Vallati, Alfonso Emilio Gerevini and Holger H. Hoos.
Performance robustness of AI planners in the 2014 International Planning Competition.
AI Communications, 31(6):445–463, 2018.

2017

Chris Cameron, Holger H. Hoos, Kevin Leyton-Brown and Frank Hutter.
OASC-2017: *Zilla Submission.
In Marius Lindauer, Jan N. van Rijn and Lars Kotthoff, editors, Proceedings of the Open Algorithm Selection Challenge, volume 79 of Proceedings of Machine Learning Research, 15–18. Brussels, Belgium, 11–12 Sep 2017. PMLR.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Sam Bayless, Nodir Kodirov, Ivan Beschastnikh, Holger H. Hoos and Alan J. Hu.
Scalable Constraint-based Virtual Data Center Allocation.
In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI-17), 546–554. 2017.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Chris Fawcett, Lars Kotthoff and Holger H. Hoos.
Hot-Rodding the Browser Engine: Automatic Configuration of JavaScript Compilers.
CoRR, 2017.
[ view publication ∙ PDF ∙ BibTeX ]
Leslie Pérez Cáceres, Manuel López-Ibánez, Holger Hoos and Thomas Stützle.
An Experimental Study of Adaptive Capping in irace.
In Proceedings of the 11th International Conference on Learning and Intelligent Optimization (LION 11), volume 10556 of Lecture Notes in Computer Science, 235–250. Springer, 2017.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Aymeric Blot, Alexis Pernet, Laetitia Jourdan, Marie-Éléonore Kessaci-Marmion and Holger H. Hoos.
Automatically Configuring Multi-objective Local Search Using Multi-objective Optimisation.
In Proceedings of the 9th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2017), volume 10173 of Lecture Notes in Computer Science, 61–76. Springer, 2017.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Marius Lindauer, Holger H. Hoos, Kevin Leyton-Brown and Torsten Schaub.
Automatic construction of parallel portfolios via algorithm configuration.
Artificial Intelligence, 244:272–290, 2017.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Mattia Rizzini, Chris Fawcett, Mauro Vallati, Alfonso Emilio Gerevini and Holger H. Hoos.
Static and Dynamic Portfolio Methods for Optimal Planning: An Empirical Analysis.
International Journal on Artificial Intelligence Tools, 26(1):1–27, 2017.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Frank Hutter, Marius Lindauer, Adrian Balint, Sam Bayless, Holger H. Hoos and Kevin Leyton-Brown.
The Configurable SAT Solver Challenge (CSSC).
Artificial Intelligence, 243:1–25, 2017.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (8.90 citations/year, 69 overall)
Holger H. Hoos, Frank Neumann and Heike Trautmann.
Automated Algorithm Selection and Configuration (Dagstuhl Seminar 16412).
Dagstuhl Reports, 6(10):33–74, 2017.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Lars Kotthoff, Chris Thornton, Holger H. Hoos, Frank Hutter and Kevin Leyton-Brown.
Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA.
Journal of Machine Learning Research, 18:25:1–25:5, 2017.
[ view publication ∙ PDF ∙ BibTeX ]
Andre Biedenkapp, Marius Thomas Lindauer, Katharina Eggensperger, Frank Hutter, Chris Fawcett and Holger H. Hoos.
Efficient Parameter Importance Analysis via Ablation with Surrogates.
In Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI-17), 773–779. AAAI Press, 2017.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Marius Lindauer, Frank Hutter, Holger H. Hoos and Torsten Schaub.
AutoFolio: An Automatically Configured Algorithm Selector (Extended Abstract).
In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI-17), 5025–5029. 2017.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (16.00 citations/year, 124 overall)
Mitra Baratchi, Geert Heijenk and Maarten van Steen.
Spaceprint: A Mobility-based Fingerprinting Scheme for Spaces.
In Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 102:1–4. 2017.
[ view publication ∙ PDF ∙ BibTeX ]
Tristan Brugman, Mitra Baratchi, Geert Heijenk and Maarten van Steen.
Inferring the Social-Connectedness of Locations from Mobility Data.
In Social Informatics, 443–457. 2017.
[ view publication ∙ PDF ∙ BibTeX ]
Mozhdeh Gholibeigi, Nora Sarrionandia, Morteza Karimzadeh, Mitra Baratchi, Hans van den Berg and Geert Heijenk.
Reliable Vehicular Broadcast Using 5G Device-to-device Communication.
In 2017 10th IFIP Wireless and Mobile Networking Conference (WMNC), 1–8. 2017.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Bernd Meijerink, Mitra Baratchi and Geert Heijenk.
Evaluation of Geocast Routing Trees on Random and Actual Networks.
In Wired/Wireless Internet Communications, 127–142. 2017.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Andreea-Cristina Petre, Cristian Chilipirea, Mitra Baratchi, Ciprian Dobre and Maarten van Steen.
WiFi Tracking of Pedestrian Behavior.
In Smart Sensors Networks, pages 309 – 337. Academic Press, 2017.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]

2016

Ashiqur R. KhudaBukhsh, Lin Xu, Holger H. Hoos and Kevin Leyton-Brown.
SATenstein: Automatically building local search SAT solvers from components.
Artificial Intelligence, 232:20–42, 2016.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (23.54 citations/year, 206 overall)
Bernd Bischl, Pascal Kerschke, Lars Kotthoff, Marius Thomas Lindauer, Yuri Malitsky, Alexandre Fréchette, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown, Kevin Tierney and Joaquin Vanschoren.
ASlib: A benchmark library for algorithm selection.
Artificial Intelligence, 237:41–58, 2016.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (22.97 citations/year, 201 overall)
Chris Fawcett and Holger H. Hoos.
Analysing differences between algorithm configurations through ablation.
Journal of Heuristics, 22(4):431–458, 2016.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (11.09 citations/year, 97 overall)
Alexandre Fréchette, Lars Kotthoff, Tomasz P. Michalak, Talal Rahwan, Holger H. Hoos and Kevin Leyton-Brown.
Using the Shapley Value to Analyze Algorithm Portfolios.
In Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI-16), 3397–3403. AAAI Press, 2016.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Julieta Martinez, Joris Clement, Holger H. Hoos and James J. Little.
Revisiting Additive Quantization.
In Proceedings of the 14th European Conference on Computer Vision (ECCV-16), volume 9906 of Lecture Notes in Computer Science, 137–153. Springer, 2016.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (5.94 citations/year, 52 overall)
Julieta Martinez, Holger H. Hoos and James J. Little.
Solving Multi-codebook Quantization in the GPU.
In Gang Hua and Hervé Jégou, editors, Proceedings of the ECCV-16 Workshop on Web-scale Vision and Social Media, volume 9913 of Lecture Notes in Computer Science, 638–650. Springer, 2016.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Holger H. Hoos.
Taming the Complexity Monster or: How I learned to Stop Worrying and Love Hard Problems.
In Proceedings of the 2016 on Genetic and Evolutionary Computation Conference, Denver, CO, USA, July 20 - 24, 2016, 3–4. ACM, 2016.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Sam Bayless, Holger H. Hoos and Alan J. Hu.
Scalable, high-quality, SAT-based multi-layer escape routing.
In Proceedings of the 35th International Conference on Computer-Aided Design (ICCAD-16), 22. ACM, 2016.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Chris Cameron, Holger H. Hoos and Kevin Leyton-Brown.
Bias in Algorithm Portfolio Performance Evaluation.
In Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI-15), 712–719. IJCAI/AAAI Press, 2016.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Aymeric Blot, Holger H. Hoos, Laetitia Jourdan, Marie-Éléonore Kessaci-Marmion and Heike Trautmann.
MO-ParamILS: A Multi-objective Automatic Algorithm Configuration Framework.
In Proceedings of the 10th International Conference on Learning and Intelligent Optimization (LION 10), volume 10079 of Lecture Notes in Computer Science, 32–47. Springer, 2016.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (5.71 citations/year, 50 overall)
Zongxu Mu, Holger H. Hoos and Thomas Stützle.
The Impact of Automated Algorithm Configuration on the Scaling Behaviour of State-of-the-Art Inexact TSP Solvers.
In Proceedings of the 10th International Conference on Learning and Intelligent Optimization (LION 10), volume 10079 of Lecture Notes in Computer Science, 157–172. Springer, 2016.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Lin Xu, Ashiqur R. KhudaBukhsh, Holger H. Hoos and Kevin Leyton-Brown.
Quantifying the Similarity of Algorithm Configurations.
In Proceedings of the 10th International Conference on Learning and Intelligent Optimization (LION 10), volume 10079 of Lecture Notes in Computer Science, 203–217. Springer, 2016.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Holger H. Hoos, Frank Neumann and Heike Trautmann.
Automated Algorithm Selection and Configuration (Dagstuhl Seminar 16412).
Dagstuhl Reports, 6(10):33–74, 2016.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Mitra Baratchi, Lennart Teunissen, Peter Ebben, Wouter Teeuw, Jan Laarhuis and Maarten van Steen.
Towards Decisive Garments for Heat Stress Risk Detection.
In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct, 1095–1100. 2016.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Mozhdeh Gholibeigi, Mitra Baratchi, Hans van den Berg and Geert Heijenk.
Towards Reliable Multi-hop Broadcast in VANETs: An Analytical Approach.
In 2016 IEEE Vehicular Networking Conference (VNC), 1–8. 2016.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Bernd Meijerink, Mitra Baratchi and Geert Heijenk.
An Efficient Geographical Addressing Scheme for the Internet.
In Wired/Wireless Internet Communications, 78–90. 2016.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Sarwar Morshed, Mitra Baratchi and Geert Heijenk.
Traffic-adaptive Duty Cycle Adaptation in TR-MAC Protocol for Wireless Sensor Networks.
In 2016 Wireless Days (WD), 1–6. 2016.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Sarwar Morshed, Mitra Baratchi, Pranab. K. Mandal and Geert Heijenk.
A Multi-channel Multiple Access Scheme Using Frequency Offsets-Modelling and analysis.
In 2016 IEEE 12th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), 1–7. 2016.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]

2015

Marius Thomas Lindauer, Holger H. Hoos, Frank Hutter and Torsten Schaub.
AutoFolio: An Automatically Configured Algorithm Selector.
Journal of Artificial Intelligence Research, 53:745–778, 2015.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (12.72 citations/year, 124 overall)
Holger H. Hoos and Thomas Stützle.
On the empirical time complexity of finding optimal solutions vs proving optimality for Euclidean TSP instances.
Optimization Letters, 9(6):1247–1254, 2015.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Holger H. Hoos, Roland Kaminski, Marius Thomas Lindauer and Torsten Schaub.
aspeed: Solver scheduling via answer set programming.
TPLP, 15(1):117–142, 2015.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Katharina Eggensperger, Frank Hutter, Holger H. Hoos and Kevin Leyton-Brown.
Efficient Benchmarking of Hyperparameter Optimizers via Surrogates.
In Blai Bonet and Sven Koenig, editors, Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, January 25-30, 2015, Austin, Texas, USA., 1114–1120. AAAI Press, 2015.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (10.15 citations/year, 99 overall)
Sam Bayless, Noah Bayless, Holger H. Hoos and Alan J. Hu.
SAT Modulo Monotonic Theories.
In Blai Bonet and Sven Koenig, editors, Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, January 25-30, 2015, Austin, Texas, USA., 3702–3709. AAAI Press, 2015.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Marius Lindauer, Holger H. Hoos, Frank Hutter and Torsten Schaub.
AutoFolio: Algorithm Configuration for Algorithm Selection.
In Frank Hutter, Marius Lindauer and Yuri Malitsky, editors, Algorithm Configuration, Papers from the 2015 AAAI Workshop, Austin, Texas, USA, January 26, 2015., volume WS-15-01 of AAAI Workshops. AAAI Press, 2015.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Jérémie Dubois-Lacoste, Holger H. Hoos and Thomas Stützle.
On the Empirical Scaling Behaviour of State-of-the-art Local Search Algorithms for the Euclidean TSP.
In Sara Silva and Anna Isabel Esparcia-Alcázar, editors, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2015, Madrid, Spain, July 11-15, 2015, 377–384. ACM, 2015.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Zongxu Mu and Holger H. Hoos.
Empirical Scaling Analyser: An Automated System for Empirical Analysis of Performance Scaling.
In Sara Silva and Anna Isabel Esparcia-Alcázar, editors, Genetic and Evolutionary Computation Conference, GECCO 2015, Madrid, Spain, July 11-15, 2015, Companion Material Proceedings, 771–772. ACM, 2015.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Mattia Rizzini, Chris Fawcett, Mauro Vallati, Alfonso Emilio Gerevini and Holger H. Hoos.
Portfolio Methods for Optimal Planning: An Empirical Analysis.
In 27th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2015, Vietri sul Mare, Italy, November 9-11, 2015, 494–501. IEEE Computer Society, 2015.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Zongxu Mu and Holger H. Hoos.
On the Empirical Time Complexity of Random 3-SAT at the Phase Transition.
In Qiang Yang and Michael Wooldridge, editors, Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, July 25-31, 2015, 367–373. AAAI Press, 2015.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Frank Hutter, Lin Xu, Holger Hoos and Kevin Leyton-Brown.
Algorithm Runtime Prediction: Methods and Evaluation (Extended Abstract).
In Qiang Yang and Michael Wooldridge, editors, Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, July 25-31, 2015, 4197–4201. AAAI Press, 2015.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (48.92 citations/year, 477 overall)
Marius Thomas Lindauer, Holger H. Hoos and Frank Hutter.
From Sequential Algorithm Selection to Parallel Portfolio Selection.
In Clarisse Dhaenens, Laetitia Jourdan and Marie-Eléonore Marmion, editors, Learning and Intelligent Optimization - 9th International Conference, LION 9, Lille, France, January 12-15, 2015. Revised Selected Papers, volume 8994 of Lecture Notes in Computer Science, 1–16. Springer, 2015.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Sepp Hartung and Holger H. Hoos.
Programming by Optimisation Meets Parameterised Algorithmics: A Case Study for Cluster Editing.
In Clarisse Dhaenens, Laetitia Jourdan and Marie-Eléonore Marmion, editors, Learning and Intelligent Optimization - 9th International Conference, LION 9, Lille, France, January 12-15, 2015. Revised Selected Papers, volume 8994 of Lecture Notes in Computer Science, 43–58. Springer, 2015.
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Lars Kotthoff, Pascal Kerschke, Holger Hoos and Heike Trautmann.
Improving the State of the Art in Inexact TSP Solving Using Per-Instance Algorithm Selection.
In Clarisse Dhaenens, Laetitia Jourdan and Marie-Eléonore Marmion, editors, Learning and Intelligent Optimization - 9th International Conference, LION 9, Lille, France, January 12-15, 2015. Revised Selected Papers, volume 8994 of Lecture Notes in Computer Science, 202–217. Springer, 2015.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (6.26 citations/year, 61 overall)
Frederick Tung, Julieta Martinez, Holger H. Hoos and James J. Little.
Bank of Quantization Models: A Data-Specific Approach to Learning Binary Codes for Large-Scale Retrieval Applications.
In 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015, Waikoloa, HI, USA, January 5-9, 2015, 566–571. IEEE Computer Society, 2015.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Holger H. Hoos and Thomas Stützle.
Stochastic Local Search Algorithms: An Overview.
In Janusz Kacprzyk and Witold Pedrycz, editors, Springer Handbook of Computational Intelligence, pages 1085–1105. Springer, 2015.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Frank Hutter, Marius Thomas Lindauer, Adrian Balint, Sam Bayless, Holger H. Hoos and Kevin Leyton-Brown.
The Configurable SAT Solver Challenge (CSSC).
CoRR, 2015.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (7.08 citations/year, 69 overall)
Bernd Bischl, Pascal Kerschke, Lars Kotthoff, Marius Thomas Lindauer, Yuri Malitsky, Alexandre Fréchette, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown, Kevin Tierney and Joaquin Vanschoren.
ASlib: A Benchmark Library for Algorithm Selection.
CoRR, 2015.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (20.62 citations/year, 201 overall)

2014

Frank Hutter, Lin Xu, Holger H. Hoos and Kevin Leyton-Brown.
Algorithm runtime prediction: Methods & evaluation.
Artif. Intell., 206:79–111, 2014.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (44.37 citations/year, 477 overall)
Kieran O'Neill, Adrin Jalali, Nima Aghaeepour, Holger Hoos and Ryan R. Brinkman.
Enhanced flowType/RchyOptimyx: a Bioconductor pipeline for discovery in high-dimensional cytometry data.
Bioinformatics, 30(9):1329–1330, 2014.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Kevin Leyton-Brown, Holger H. Hoos, Frank Hutter and Lin Xu.
Understanding the empirical hardness of NP-complete problems.
Commun. ACM, 57(5):98–107, 2014.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (4.65 citations/year, 50 overall)
Holger H. Hoos and Thomas Stützle.
On the empirical scaling of run-time for finding optimal solutions to the travelling salesman problem.
European Journal of Operational Research, 238(1):87–94, 2014.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Holger Hoos, Marius Thomas Lindauer and Torsten Schaub.
claspfolio 2: Advances in Algorithm Selection for Answer Set Programming.
TPLP, 14(4-5):569–585, 2014.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (7.91 citations/year, 85 overall)
Lucas Majerowicz, Ariel Shamir, Alla Sheffer and Holger H. Hoos.
Filling Your Shelves: Synthesizing Diverse Style-Preserving Artifact Arrangements.
IEEE Trans. Vis. Comput. Graph., 20(11):1507–1518, 2014.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Chris Fawcett, Mauro Vallati, Frank Hutter, Jörg Hoffmann, Holger H. Hoos and Kevin Leyton-Brown.
Improved Features for Runtime Prediction of Domain-Independent Planners.
In Steve A. Chien, Minh Binh Do, Alan Fern and Wheeler Ruml, editors, Proceedings of the Twenty-Fourth International Conference on Automated Planning and Scheduling, ICAPS 2014, Portsmouth, New Hampshire, USA, June 21-26, 2014. AAAI, 2014.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (5.02 citations/year, 54 overall)
Katharina Eggensperger, Frank Hutter, Holger H. Hoos and Kevin Leyton-Brown.
Surrogate Benchmarks for Hyperparameter Optimization.
In Joaquin Vanschoren, Pavel Brazdil, Carlos Soares and Lars Kotthoff, editors, Proceedings of the International Workshop on Meta-learning and Algorithm Selection co-located with 21st European Conference on Artificial Intelligence, MetaSel@ECAI 2014, Prague, Czech Republic, August 19, 2014., volume 1201 of CEUR Workshop Proceedings, 24–31. CEUR-WS.org, 2014.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Frank Hutter, Holger Hoos and Kevin Leyton-Brown.
An Efficient Approach for Assessing Hyperparameter Importance.
In Proceedings of the 31th International Conference on Machine Learning, ICML 2014, Beijing, China, 21-26 June 2014, volume 32 of JMLR Workshop and Conference Proceedings, 754–762. JMLR.org, 2014.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (34.23 citations/year, 368 overall)
Frank Hutter, Manuel López-Ibá~nez, Chris Fawcett, Marius Thomas Lindauer, Holger H. Hoos, Kevin Leyton-Brown and Thomas Stützle.
AClib: A Benchmark Library for Algorithm Configuration.
In Panos M. Pardalos, Mauricio G. C. Resende, Chrysafis Vogiatzis and Jose L. Walteros, editors, Learning and Intelligent Optimization - 8th International Conference, Lion 8, Gainesville, FL, USA, February 16-21, 2014. Revised Selected Papers, volume 8426 of Lecture Notes in Computer Science, 36–40. Springer, 2014.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (7.07 citations/year, 76 overall)
Daniel Geschwender, Frank Hutter, Lars Kotthoff, Yuri Malitsky, Holger H. Hoos, Kevin Leyton-Brown, D. Geschwender, F. Hutter, L. Kotthoff, Y. Malitsky, H. H. Hoos and K. Leyton-Brown.
Algorithm Configuration in the Cloud: A Feasibility Study.
In Panos M. Pardalos, Mauricio G. C. Resende, Chrysafis Vogiatzis and Jose L. Walteros, editors, Learning and Intelligent Optimization - 8th International Conference, Lion 8, Gainesville, FL, USA, February 16-21, 2014. Revised Selected Papers, volume 8426 of Lecture Notes in Computer Science, 41–46. Springer, 2014.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Sam Bayless, Dave A. D. Tompkins and Holger H. Hoos.
Evaluating Instance Generators by Configuration.
In Panos M. Pardalos, Mauricio G. C. Resende, Chrysafis Vogiatzis and Jose L. Walteros, editors, Learning and Intelligent Optimization - 8th International Conference, Lion 8, Gainesville, FL, USA, February 16-21, 2014. Revised Selected Papers, volume 8426 of Lecture Notes in Computer Science, 47–61. Springer, 2014.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Holger Hoos, Roland Kaminski, Marius Thomas Lindauer and Torsten Schaub.
Solver Scheduling via Answer Set Programming.
CoRR, 2014.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Holger Hoos, Marius Thomas Lindauer and Torsten Schaub.
claspfolio 2: Advances in Algorithm Selection for Answer Set Programming.
CoRR, 2014.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (7.91 citations/year, 85 overall)
Sam Bayless, Noah Bayless, Holger H. Hoos and Alan J. Hu.
SAT Modulo Monotonic Theories.
CoRR, 2014.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Julieta Martinez, Holger H. Hoos and James J. Little.
Stacked Quantizers for Compositional Vector Compression.
CoRR, 2014.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Mitra Baratchi, Nirvana Meratnia, Paul J. M. Havinga, Andrew K. Skidmore and Bert A. K. G. Toxopeus.
A Hierarchical Hidden semi-Markov Model for Modeling Mobility Data.
In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 401–412. 2014.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (5.40 citations/year, 58 overall)
Mitra Baratchi, Nirvana Meratnia and Paul J. M. Havinga.
Recognition of Periodic Behavioral Patterns from Streaming Mobility Data.
In Mobile and Ubiquitous Systems: Computing, Networking, and Services, 102–115. 2014.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]

2013

Nima Aghaeepour and Holger H. Hoos.
Ensemble-based prediction of RNA secondary structures.
BMC Bioinformatics, 14:139, 2013.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Sam Bayless, Celina G. Val, Thomas Ball, Holger H. Hoos and Alan J. Hu.
Efficient modular SAT solving for IC3.
In Formal Methods in Computer-Aided Design, FMCAD 2013, Portland, OR, USA, October 20-23, 2013, 149–156. IEEE, 2013.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
James Styles and Holger Hoos.
Ordered racing protocols for automatically configuring algorithms for scaling performance.
In Christian Blum and Enrique Alba, editors, Genetic and Evolutionary Computation Conference, GECCO '13, Amsterdam, The Netherlands, July 6-10, 2013, 551–558. ACM, 2013.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Frank Hutter, Holger Hoos and Kevin Leyton-Brown.
An evaluation of sequential model-based optimization for expensive blackbox functions.
In Christian Blum and Enrique Alba, editors, Genetic and Evolutionary Computation Conference, GECCO '13, Amsterdam, The Netherlands, July 6-10, 2013, Companion Material Proceedings, 1209–1216. ACM, 2013.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (7.23 citations/year, 85 overall)
Katharina Eggensperger, Matthias Feurer, Frank Hutter, James Bergstra, Jasper Snoek, Holger Hoos and Kevin Leyton-Brown.
Towards an Empirical Foundation for Assessing Bayesian Optimization of Hyperparameters.
In NIPS workshop on Bayesian Optimization in Theory and Practice. dec 2013.
[ PDF ∙ BibTeX ∙ citations ] Highly cited (30.13 citations/year, 354 overall)
Chris Thornton, Frank Hutter, Holger H. Hoos and Kevin Leyton-Brown.
Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms.
In Inderjit S. Dhillon, Yehuda Koren, Rayid Ghani, Ted E. Senator, Paul Bradley, Rajesh Parekh, Jingrui He, Robert L. Grossman and Ramasamy Uthurusamy, editors, The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013, Chicago, IL, USA, August 11-14, 2013, 847–855. ACM, 2013.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (131.66 citations/year, 1547 overall)
Holger H. Hoos, Benjamin Kaufmann, Torsten Schaub and Marius Schneider.
Robust Benchmark Set Selection for Boolean Constraint Solvers.
In Giuseppe Nicosia and Panos M. Pardalos, editors, Learning and Intelligent Optimization - 7th International Conference, LION 7, Catania, Italy, January 7-11, 2013, Revised Selected Papers, volume 7997 of Lecture Notes in Computer Science, 138–152. Springer, 2013.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Frank Hutter, Holger H. Hoos and Kevin Leyton-Brown.
Identifying Key Algorithm Parameters and Instance Features Using Forward Selection.
In Giuseppe Nicosia and Panos M. Pardalos, editors, Learning and Intelligent Optimization - 7th International Conference, LION 7, Catania, Italy, January 7-11, 2013, Revised Selected Papers, volume 7997 of Lecture Notes in Computer Science, 364–381. Springer, 2013.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (6.13 citations/year, 72 overall)
James Styles and Holger H. Hoos.
Using Racing to Automatically Configure Algorithms for Scaling Performance.
In Giuseppe Nicosia and Panos M. Pardalos, editors, Learning and Intelligent Optimization - 7th International Conference, LION 7, Catania, Italy, January 7-11, 2013, Revised Selected Papers, volume 7997 of Lecture Notes in Computer Science, 382–388. Springer, 2013.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Mauro Vallati, Chris Fawcett, Alfonso Gerevini, Holger H. Hoos and Alessandro Saetti.
Automatic Generation of Efficient Domain-Optimized Planners from Generic Parametrized Planners.
In Malte Helmert and Gabriele Röger, editors, Proceedings of the Sixth Annual Symposium on Combinatorial Search, SOCS 2013, Leavenworth, Washington, USA, July 11-13, 2013. AAAI Press, 2013.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Craig Boutilier, Ronen I. Brafman, Holger H. Hoos and David Poole.
Reasoning With Conditional Ceteris Paribus Preference Statem.
CoRR, 2013.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (30.38 citations/year, 357 overall)
Holger H. Hoos and Thomas Stützle.
Evaluating Las Vegas Algorithms - Pitfalls and Remedies.
CoRR, 2013.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (16.68 citations/year, 196 overall)
Frank Hutter, Holger Hoos and Kevin Leyton-Brown.
Bayesian Optimization With Censored Response Data.
CoRR, 2013.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Nima Aghaeepour, Greg Finak, Holger Hoos, Tim R Mosmann, Ryan Brinkman, Raphael Gottardo, Richard H Scheuermann, FlowCAP Consortium, DREAM Consortium and others.
Critical assessment of automated flow cytometry data analysis techniques.
Nature methods, 10(3):228–238, 2013.
[ PDF ∙ BibTeX ∙ citations ] Highly cited (50.13 citations/year, 589 overall)
Siavash Aflaki, Nirvana Meratnia, Mitra Baratchi and Paul J. M. Havinga.
Evaluation of Incentives for Body Area Network-based Healthcare Systems.
In 2013 IEEE Eighth International Conference on Intelligent Sensors, Sensor Networks and Information Processing, 515–520. 2013.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Mitra Baratchi, Nirvana Meratnia, Paul J. M. Havinga, Andrew K. Skidmore and Bert A. G. Toxopeus.
Sensing Solutions for Collecting Spatio-Temporal Data for Wildlife Monitoring Applications: A Review.
Sensors, 13(5):6054–6088, 2013.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (5.28 citations/year, 62 overall)
Mitra Baratchi, Nirvana Meratnia and Paul J. M. Havinga.
Finding Frequently Visited Paths: Dealing with the Uncertainty of Spatio-temporal Mobility Data.
In 2013 IEEE Eighth International Conference on Intelligent Sensors, Sensor Networks and Information Processing, 479–484. 2013.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Mitra Baratchi, Nirvana Meratnia and Paul J. M. Havinga.
On the Use of Mobility Data for Discovery and Description of Social Ties.
In Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 1229–1236. 2013.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]

2012

Nima Aghaeepour, Pratip K. Chattopadhyay, Anuradha Ganesan, Kieran O'Neill, Habil Zare, Adrin Jalali, Holger H. Hoos, Mario Roederer and Ryan R. Brinkman.
Early immunologic correlates of HIV protection can be identified from computational analysis of complex multivariate T-cell flow cytometry assays.
Bioinformatics, 28(7):1009–1016, 2012.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (7.37 citations/year, 94 overall)
Monir Hajiaghayi, Anne Condon and Holger H. Hoos.
Analysis of energy-based algorithms for RNA secondary structure prediction.
BMC Bioinformatics, 13:22, 2012.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (3.92 citations/year, 50 overall)
Holger H. Hoos.
Programming by optimization.
Commun. ACM, 55(2):70–80, 2012.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (18.43 citations/year, 235 overall)
Lin Xu, Holger H. Hoos and Kevin Leyton-Brown.
Predicting Satisfiability at the Phase Transition.
In Jörg Hoffmann and Bart Selman, editors, Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, July 22-26, 2012, Toronto, Ontario, Canada. AAAI Press, 2012.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Holger Hoos, Roland Kaminski, Torsten Schaub and Marius Thomas Schneider.
aspeed: ASP-based Solver Scheduling.
In Agostino Dovier and V\'ıtor Santos Costa, editors, Technical Communications of the 28th International Conference on Logic Programming, ICLP 2012, September 4-8, 2012, Budapest, Hungary, volume 17 of LIPIcs, 176–187. Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, 2012.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Frank Hutter, Holger H. Hoos and Kevin Leyton-Brown.
Parallel Algorithm Configuration.
In Youssef Hamadi and Marc Schoenauer, editors, Learning and Intelligent Optimization - 6th International Conference, LION 6, Paris, France, January 16-20, 2012, Revised Selected Papers, volume 7219 of Lecture Notes in Computer Science, 55–70. Springer, 2012.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (8.31 citations/year, 106 overall)
Marius Schneider and Holger H. Hoos.
Quantifying Homogeneity of Instance Sets for Algorithm Configuration.
In Youssef Hamadi and Marc Schoenauer, editors, Learning and Intelligent Optimization - 6th International Conference, LION 6, Paris, France, January 16-20, 2012, Revised Selected Papers, volume 7219 of Lecture Notes in Computer Science, 190–204. Springer, 2012.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
James Styles, Holger H. Hoos and Martin Müller.
Automatically Configuring Algorithms for Scaling Performance.
In Youssef Hamadi and Marc Schoenauer, editors, Learning and Intelligent Optimization - 6th International Conference, LION 6, Paris, France, January 16-20, 2012, Revised Selected Papers, volume 7219 of Lecture Notes in Computer Science, 205–219. Springer, 2012.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ]
Lin Xu, Frank Hutter, Holger Hoos and Kevin Leyton-Brown.
Evaluating Component Solver Contributions to Portfolio-Based Algorithm Selectors.
In Alessandro Cimatti and Roberto Sebastiani, editors, Theory and Applications of Satisfiability Testing - SAT 2012 - 15th International Conference, Trento, Italy, June 17-20, 2012. Proceedings, volume 7317 of Lecture Notes in Computer Science, 228–241. Springer, 2012.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (9.18 citations/year, 117 overall)
Holger H. Hoos.
Automated Algorithm Configuration and Parameter Tuning.
In Youssef Hamadi, Eric Monfroy and Frédéric Saubion, editors, Autonomous Search, pages 37–71. Springer, 2012.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (16.24 citations/year, 207 overall)
Chris Thornton, Frank Hutter, Holger H. Hoos and Kevin Leyton-Brown.
Auto-WEKA: Automated Selection and Hyper-Parameter Optimization of Classification Algorithms.
CoRR, 2012.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (121.33 citations/year, 1547 overall)
Frank Hutter, Lin Xu, Holger H. Hoos and Kevin Leyton-Brown.
Algorithm Runtime Prediction: The State of the Art.
CoRR, 2012.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (37.41 citations/year, 477 overall)
Holger H. Hoos, Kevin Leyton-Brown, Torsten Schaub and Marius Schneider.
Algorithm Configuration for Portfolio-based Parallel SAT-Solving.
In Proceedings of the ECAI-12 Workshop on Combining Constraint Solving with MIning and Learning. 2012. (6 pages).
[ PDF ∙ BibTeX ∙ citations ]
Nima Aghaeepour, Adrin Jalali, Kieran O'Neill, Pratip K. Chattopadhyay, Mario Roederer, Holger H. Hoos and Ryan R. Brinkman.
RchyOptimyx: Cellular hierarchy optimization for flow cytometry.
Cytometry Part A, 81A(12):1022–1030, 2012.
[ view publication ∙ PDF ∙ BibTeX ∙ citations ] Highly cited (5.25 citations/year, 67 overall)

2011

Dave A. D. Tompkins, Adrian Balint and Holger H. Hoos.
Captain Jack: New Variable Selection Heuristics in Local Search for SAT.
In Proceedings of the 14th International Conference on Theory and Applications of Satisfiability Testing (SAT 2011), 302–316. 2011.
[ PDF ∙ BibTeX ∙ citations ] Highly cited (4.00 citations/year, 55 overall)
Mauro Vallati, Chris Fawcett, Alfonso Gerevini, Holger H. Hoos and Alessandro Saetti.
Automatic Generation of Efficient Domain-Optimized Planners from Generic Parametrized Planners.
In Proceedings of the 18th RCRA Workshop on Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion (RCRA 2011), 111–123. 2011.
[ PDF ∙ BibTeX ∙ citations ]
Lin Xu, Frank Hutter, Holger H. Hoos and Kevin Leyton-Brown.
Hydra-MIP: Automated Algorithm Configuration and Selection for Mixed Integer Programming.
In Proceedings of the 18th RCRA Workshop on Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion (RCRA 2011), 16–30. 2011.
[ PDF ∙ BibTeX ∙ citations ] Highly cited (7.93 citations/year, 109 overall)
Chris Fawcett, Malte Helmert, Holger Hoos, Erez Karpas, Gabriele Röger and Jendrik Seipp.
FD-Autotune: Domain-Specific Configuration using Fast Downward.
In Proceedings of the ICAPS Workshop on Planning and Learning (PAL 2011), 13–20. 2011.
[ PDF ∙ BibTeX ∙ citations ] Highly cited (4.07 citations/year, 56 overall)
Christopher Nell, Chris Fawcett, Holger H. Hoos and Kevin Leyton-Brown.
HAL: A Framework for the Automated Design and Analysis of High-Performance Algorithms.
In Proceedings of the 5th International Conference on Learning and Intelligent Optimization (LION 5), 600–615. 2011.
[ PDF ∙ BibTeX ∙ citations ]
Frank Hutter, Holger H. Hoos and Kevin Leyton-Brown.
Sequential Model-Based Optimization for General Algorithm Configuration.
In Proceedings of the 5th International Conference on Learning and Intelligent Optimization (LION 5), 507–523. 2011.
[ PDF ∙ BibTeX ∙ citations ] Highly cited (177.60 citations/year, 2442 overall)
Nima Aghaeepour, Radina Nikolic, Holger H. Hoos and Ryan R. Brinkman.
Rapid cell population identification in flow cytometry data.
Cytometry Part A, 79A(1):6–13, 2011.
[ PDF ∙ BibTeX ∙ citations ] Highly cited (15.20 citations/year, 209 overall)
Therese Biedl, Stephane Durocher, Holger H. Hoos, Shuang Luan, Jared Saia and Maxwell Young.
A Note on Improving the Performance of Approximation Algorithms for Radiation Therapy.
Information Processing Letters, 111(7):326–333, 2011.
[ PDF ∙ BibTeX ∙ citations ]

2010

Frank Hutter, Holger H. Hoos and Kevin Leyton-Brown.
Tradeoffs in the empirical evaluation of competing algorithm designs.
Annals of Mathematics and Artificial Intelligence, 60(1-2):65–89, 2010.
[ PDF ∙ BibTeX ∙ citations ]
Mirela Andronescu, Anne Condon, Holger H. Hoos, David H. Mathews and Kevin P. Murphy.
Computational approaches for RNA energy parameter estimation.
RNA, 16(12):2304–2318, 2010.
[ PDF ∙ BibTeX ∙ citations ] Highly cited (9.29 citations/year, 137 overall)
Lin Xu, Holger H. Hoos and Kevin Leyton-Brown.
Hydra: Automatically Configuring Algorithms for Portfolio-Based Selection.
In Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI 2010), 210–216. 2010.
[ PDF ∙ BibTeX ∙ citations ] Highly cited (14.17 citations/year, 209 overall)
Dave A.D. Tompkins and Holger H. Hoos.
Dynamic Scoring Functions with Variable Expressions: New SLS Methods for Solving SAT.
In Proceedings of the 13th International Conference on Theory and Applications of Satisfiability Testing (SAT 2010), 278–292. 2010.
[ PDF ∙ BibTeX ∙ citations ]
Frank Hutter, Holger H. Hoos and Kevin Leyton-Brown.
Automated Configuration of Mixed Integer Programming Solvers.
In Proceedings of the 7th International Conference on the Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (CPAIOR 2010), 186–202. 2010.
[ PDF ∙ BibTeX ∙ citations ] Highly cited (14.24 citations/year, 210 overall)
Frank Hutter, Thomas Bartz-Beielstein, Holger H. Hoos, Kevin Leyton-Brown and Kevin P. Murphy.
Sequential Model-Based Parameter Optimisation: An Experimental Investigation of Automated and interactive Approaches.
In T. Bartz-Beielstein, M. Chiarandini, L. Paquete and M. Preuss, editors, Empirical Methods for the Analysis of Optimization Algorithms, chapter 15, pages 363–414. Springer-Verlag, Berlin/Heidelberg, Germany, 2010.
[ PDF ∙ BibTeX ∙ citations ]
Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown and Kevin Murphy.
Time-Bounded Sequential Parameter Optimization.
In Proceedings of the 4th International Conference on Learning and Intelligent Optimization (LION 4), 281–298. 2010.
[ PDF ∙ BibTeX ∙ citations ] Highly cited (6.10 citations/year, 90 overall)

2009

Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown and Thomas Stützle.
ParamILS: An Automatic Algorithm Configuration Framework.
Journal of Artificial Intelligence Research, 36:267–306, 2009.
[ PDF ∙ BibTeX ∙ citations ] Highly cited (71.05 citations/year, 1119 overall)
Ashiqur R. KhudaBukhsh, Lin Xu, Holger H. Hoos and Kevin Leyton-Brown.
SATenstein: Automatically Building Local Search SAT Solvers From Components.
In Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI 2009), 517–524. 2009.
[ PDF ∙ BibTeX ∙ citations ] Highly cited (13.08 citations/year, 206 overall)
Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown and Kevin Murphy.
An Experimental Investigation of Model-Based Parameter Optimisation: SPO and Beyond.
In Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation (GECCO 2009), 271–278. 2009.
[ PDF ∙ BibTeX ∙ citations ] Highly cited (7.49 citations/year, 118 overall)

2008

Holger H. Hoos.
Computer-Aided Design of High-Performance Algorithms.
Technical Report TR-2008-16, Department of Computer Science, University of British Columbia, December 2008.
[ PDF ∙ BibTeX ∙ citations ]
Marco Chiarandini, Chris Fawcett and Holger H. Hoos.
A Modular Multiphase Heuristic Solver for Post Enrollment Course Timetabling (Extended Abstract).
In Proceedings of the 7th International Conference on the Practice and Theory of Automated Timetabling (PATAT 2008). 2008.
[ PDF ∙ BibTeX ∙ citations ]
Mirela Andronescu, Vera Bereg, Holger H. Hoos and Anne Condon.
RNA STRAND: The RNA Secondary Structure and Statistical Analysis Database.
BMC Bioinformatics, 9:340, 2008.
[ PDF ∙ BibTeX ∙ citations ] Highly cited (19.22 citations/year, 322 overall)
Sanja Rogic, Ben Montpetit, Holger H. Hoos, Alan K. Mackworth, B.F. Francis Ouellette and Philip Hieter.
Correlation between the secondary structure of pre-mRNA and efficiency of splicing in Saccharomyces cerevisiae.
BMC Genomics, 9:355, 2008.
[ PDF ∙ BibTeX ∙ citations ] Highly cited (3.22 citations/year, 54 overall)
Lin Xu, Frank Hutter, Holger H. Hoos and Kevin Leyton-Brown.
SATzilla: Portfolio-based Algorithm Selection for SAT.
Journal of Artificial Intelligence Research, 32:565–606, 2008. This paper won the 2010 IJCAI-JAIR Best Paper Prize, and the portfolio-based SAT solvers described in it won 3 gold medals, 1 silver and 1 bronze in the 2007 SAT competition. This article is a significantly expanded version of our earlier CP-07 paper with improved methodology and algorithms. The same methodology underlies a later version of SATzilla, which won 3 gold and 2 silver medals in the 2009 SAT competition.
[ PDF ∙ BibTeX ∙ citations ] Highly cited (59.46 citations/year, 996 overall)
Mauro Brunato, Holger H. Hoos and Roberto Battiti.
On Effectively Finding Maximal Quasi-Cliques in Graphs.
In Proceedings of the 2nd Learning and Intelligent Optimization Conference (LION 2), 41–55. 2008.
[ PDF ∙ BibTeX ∙ citations ] Highly cited (6.81 citations/year, 114 overall)

Before 2008

For earlier publications, please see Holger's Google Scholar profile or UBC publication page.