Holger Hoos
Professor of Machine learning
- Name
- Prof.dr. H.H. Hoos
- Telephone
- +31 71 527 5777
- h.h.hoos@liacs.leidenuniv.nl
- ORCID iD
- 0000-0003-0629-0099
PhD students
News
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More powerful data centre will accelerate research -
Finding unique drug structures with artificial intelligence and chemistry -
AI versus corona -
Major win for CLAIRE in 50 million euro round of “seed funding” -
‘Holland, invest in the national AI-ecosystem’ -
Science Groot funding for Leiden scientists -
‘Nothing is so special about human intelligence, that it cannot be replicated in a machine’ -
First SAILS Symposium 'The future of AI is human': a photo impression -
Gravitation Grant for combining human and artificial intelligence -
These are the five Leiden highlights of ICT.OPEN -
Free symposium reveals surprising uses of AI -
Major steps towards realising a bold vision for European Excellence in AI
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AI researchers stress importance of collaborations with UK
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CLAIRE in I/O magazine -
Holger Hoos in NRC about AI brain drain -
Francien Dechesne will give a Tegenlicht-talk at Lowlands -
Inauguration computer cluster GRACE at LIACS -
More effective data-driven policymaking -
Artificial intelligence to extend, not replace human capabilities -
Policy and infrastructure data at the 20th LCDS meeting
Research Group
Professor of Machine learning
- Wiskunde en Natuurwetenschappen
- Leiden Inst. Advanced Computer Sciences
- Wang C., Bäck T.H.W., Hoos H.H., Baratchi M., Limmer S. & Olhofer M. (2019), Automated Machine Learning for Short-term Electric Load Forecasting. In: 2019 IEEE Symposium Series on Computational Intelligence (SSCI).: IEEE. 314-321.
- Mu Z., Dubois-Lacoste J., Hoos H.H. & Stützle T. (2018), On the Empirical Scaling of Running Time for Finding Optimal Solutions to the TSP, Journal of Heuristics 24(6): 879-898.
- Pushak Y. & Hoos H.H. (2018), Algorithm Configuration Landscapes: More Benign than Expected?. In: Auger A., Fonseca C.M., Lourenço N., Machado P., Paquete L., Whitley D. (Eds.) Parallel Problem Solving from Nature - PPSN XV - 15th International Conference, Coimbra, Portugal, September 8-12, 2018, Proceedings, Part II. no. Theoretical Computer Science and General Issues, Volume 11102: Springer International Publishing. 271-283.
- Blot A., Hoos H.H., Kessaci M.E. & Jourdan L. (2018), 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).: IEEE.
- Kerschke P., Kotthoff L., Bossek J., Hoos H.H. & Trautmann H. (2018), Leveraging TSP Solver Complementarity through Machine Learning, Evolutionary Computation 26(4): 597-620.
- Martinez J., Tallavajhula S., Hoos H.H. & Little J.J. (2018), LSQ++: Lower Running Time and Higher Recall in Multi-codebook Quantization. In: Ferrari V., Hebert M., Sminchisescu C., Weiss Y. (Eds.) Proceedings of the 15th European Conference on Computer Vision ({ECCV} 2018). no. Lecture Notes in Computer Science, volume 11220 Cham: Springer. 508-523.
- Lamers W.S., Eck N.J.P. van, Waltman L.R. & Hoos H.H. (2018), Patterns in citation context: The case of the field of scientometrics. In: Proceedings of the 23rd International Conference on Science and Technology Indicators.. 1114-1122.
- Hoos H.H., Peitl T., Slivovsky F. & Szeider S. (2018), Portfolio-Based Algorithm Selection for Circuit QBFs. In: Hooker J. (Ed.) Principles and Practice of Constraint Programming - 24th International Conference, CP 2018, Lille, France, August 27-31, 2018, Proceedings. no. Programming and Software Engineering, Volume 11008: Springer International Publishing. 195-209.
- Kotthoff L., Fréchette A., Michalak T., Rahwan T., Hoos H.H. & Leyton-Brown K. (2018), Quantifying Algorithmic Improvements over Time. In: Lang J. (Ed.) Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI-18).: IJCAI. 5165-5171.
- Lindauer M., Hoos H.H., Hutter F. & Leyton-Brown K. (2018), Selection and Configuration of Parallel Portfolios. In: Hamadi Y., Sais L. (Eds.) Handbook of Parallel Constraint Reasoning. Cham: Springer International Publishing. 583-615.
- Kodirov N., Bayless S., Ruffy F., Beschastnikh I., Hoos H.H. & Hu A.J. (2018), VNF chain allocation and management at data center scale. In: Sierra C. (Ed.) ANCS '18 Proceedings of the 2018 Symposium on Architectures for Networking and Communications Systems. New York: ACM. 125-140.
- Eggensperger K., Lindauer M., Hoos H.H., Hutter F. & Leyton-Brown K. (2018), Efficient Benchmarking of Algorithm Configuration Procedures via Model-Based Surrogates, Machine Learning 107(1): 15-41.
- Hoos H.H. (27 October 2017), Beyond programming: the quest for machine intelligence (Inaugural address. Leiden Institute of Advanced Computer Science (LIACS), Science, Leiden). Leiden: Universiteit Leiden.
- Rizzini M., Fawcett C., Vallati M., Gerevini A.E. & Hoos H.H. (2017), Static and Dynamic Portfolio Methods for Optimal Planning: An Empirical Analysis, International Journal on Artificial Intelligence Tools 26(1): 1-27.
- Cáceres L.P., López-Ibáñez M., Hoos H.H. & Stützle T. (2017), An Experimental Study of Adaptive Capping in irace. In: Battiti R., Kvasov D.E., Sergeyev Y.D. (Eds.) Learning and Intelligent Optimization. LION 2017. no. Lecture Notes in Computer Science 10556 Cham: Springer. 235-250.
- Lindauer M., Hutter F., Hoos H.H. & Schaub T. (2017), AutoFolio: An Automatically Configured Algorithm Selector (Extended Abstract). In: Sierra C. (Ed.) Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI-17).: International Joint Conferences on Artificial Inteligence Organization. 5025-5029.
- Hoos H.H., Neumann F. & Trautmann H. (2017), Automated Algorithm Selection and Configuration (Dagstuhl Seminar 16412), Dagstuhl Reports 6(10): 33-74.
- Blot A., Pernet A., Jourdan L., Kessaci-Marmion M.E. & Hoos H.H. (2017), Automatically Configuring Multi-objective Local Search Using Multi-objective Optimisation. In: Trautmann H., Rudolph G., Klamroth K., Schütze O., Wiecek M., Jin Y., Grimme C. (Eds.) Evolutionary Multi-Criterion Optimization, 9th International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings. no. 10173 Cham: Springer. 61-76.
- Lindauer M., Hoos H.H., Leyton-Brown K. & Schaub T. (2017), Automatic construction of parallel portfolios via algorithm configuration, Artificial Intelligence 244: 272-290.
- Kotthoff L., Thornton C., Hoos H.H., Hutter F. & Leyton-Brown K. (2017), Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA, Journal of Machine Learning Research 18(25): 1-5.
- Biedenkapp A., Lindauer M.T., Eggensperger K., Hutter F., Fawcett C. & Hoos H.H. (2017), Efficient Parameter Importance Analysis via Ablation with Surrogates. In: Singh S.P., Markovitch S. (Eds.) Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI-17). Palo Alto, CA: AAAI Press. 773-779.
- Fawcett C., Kotthoff L. & Hoos H.H. (2017), Hot-Rodding the Browser Engine: Automatic Configuration of JavaScript Compilers. International Symposium on Code Generation and Optimization 12 3 2016-8 3 2016 [conference poster].
- Cameron C., Hoos H.H., Leyton-Brown K. & Hutter F. (2017), OASC-2017: *Zilla Submission. In: Lindauer M., Rijn J.N. van, Kotthoff L. (Eds.) Proceedings Machine Learning Research. no. 79: PMLR. 15-18.
- Bayless S., Kodirov N., Beschastnikh I., Hoos H.H. & Hu A.J. (2017), Scalable Constraint-based Virtual Data Center Allocation. In: Sierra C. (Ed.) Proceedings of the 26th International Joint Conference on Artificial Intelligence.: International Joint Conferences on Artificial Inteligence. 546--554.
- Hutter F., Lindauer M., Balint A., Bayless S., Hoos H.H. & Leyton-Brown K. (2017), The Configurable SAT Solver Challenge (CSSC), Artificial Intelligence 243: 1-25.
- Licensing of software developed based on my work at UBC, occasional consulting
- (Co-)supervision of students, joint research