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
Holger Hoos is Professor of Machine Learning at LIACS. His research interests span artificial intelligence, empirical algorithmics, bioinformatics and computer music.
More information about Holger Hoos
PhD Candidates
News
-
ESA grant to improve the Earth's 'digital twin' -
CLAIRE wins prestigious Artificial Intelligence prize -
CLAIRE opens four new offices and celebrates with roadshows -
Semi-supervised learning paper selected as 2020 NatureSpringer Research Highlight -
Holger Hoos appointed ACM Fellow -
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
-
AI researchers stress importance of collaborations with UK
-
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
Research Group
Holger founded the ADA Research Group in 2017, after being appointed Professor of Machine Learning at the Leiden Institute of Advanced Computer Science (LIACS). He is also an Adjunct Professor of Computer Science at the University of British Columbia (Canada), where he holds an additional appointment as Faculty Associate at the Peter Wall Institute for Advanced Studies. He is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) and past president of the Canadian Association for Artificial Intelligence / Association pour l'intelligence artificielle au Canada (CAIAC). Holger completed his PhD in 1998 at TU Darmstadt (Germany), where he previously studied computer science, mathematics and biochemistry.
Holger's research interests span artificial intelligence, empirical algorithmics, bioinformatics and computer music. He is known for his work on machine learning and optimisation methods for the automated design of high-performance algorithms and for his work on stochastic local search. Based on a broad view of machine learning, he has developed - and vigorously pursues - the paradigm of programming by optimisation (PbO); he is also one of the originators of the concept of automated machine learning (AutoML). Holger has a penchant for work at the boundaries between computing science and other disciplines, and much of his work is inspired by real-world applications.
In 2018, together with Morten Irgens (Oslo Metropolitan University) and Philipp Slusallek (German Research Center for Artificial Intelligence), Holger launched CLAIRE, an initiative by the European AI community that seeks to strengthen European excellence in AI research and innovation. CLAIRE promotes excellence across all of AI, for all of Europe, with a human-centred focus and aims to achieve an impact similar to that of CERN. The initiative has attracted major media coverage in many European countries and garnered broad support by more than 1000 AI experts, more than one hundred fellows of various scientific AI associations, many editors of scientific AI journals, national AI societies, top AI institutes and key stakeholders in industry and other organisations (for details, see claire-ai.org).
Professor of Machine Learning
- Science
- Leiden Inst of Advanced Computer Science
- Ottervanger G.B., Baratchi M. & Hoos H.H. (2021), MultiETSC: automated machine learning for early time series classification, Data Mining and Knowledge Discovery 35: 2602–2654.
- Eeden W.A. van, Luo C., Hemert A.M. van, Carlier I.V.E., Penninx B.W., Wardenaar K.J., Hoos H.H. & Giltay E.J. (2021), 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.
- Bontempi G., Chavarriaga R., De Canck H., Girardi E., Hoos H.H., Kilbane-Dawe I., Ball T., Nowé A., Sousa J., Bacciu D., Aldinucci M., De Domenico M., Saffiotti A. & Maratea M. (2021), The CLAIRE COVID-19 initiative: approach, experiences and recommendations, Ethics and Information Technology 23(Suppl 1): 127-133.
- Hoos H.H., Hutter F. & Leyton-Brown K. (2021), Automated configuration and selection of SAT solvers. In: Biere A., Heule M., Maaren H. van & Walsh T. (Eds.) Handbook of satisfiability. Frontiers in Artificial Intelligence and Applications no. 336: IOS Press. 481-507.
- Blom K. van der, Serban A.C., Hoos H.H. & Visser J.M.W. (2021), AutoML adoption in ML software. In: 8th ICML Workshop on automated machine learning..
- Lei Z., Cai S., Luo C. & Hoos H.H. (2021), Efficient local search for Pseudo Boolean Optimization. In: Li C.M. & Manyà F. (Eds.) Theory and applications of satisfiability testing – SAT 2021. no. 12831 Cham: Springer. 332-348.
- Veloso B., Carprese L., König H.M.T., Teixeira S., Manco G., Hoos H.H. & Gama J. (2021), Hyper-parameter optimization for latent spaces. In: Oliver N., Pérez-Cruz F., Kramer S., Read J. & Lozano J.A. (Eds.) Machine Learning and Knowledge Discovery in Databases. Research Track. ECML PKDD 2021. no. 12977 Cham: Springer International Publishing. 249-264.
- Dengel A., Etzioni O., DeCario N., Hoos H.H., Li F.F., Tsujii J. & Traverso P. (2021), Next big challenges in core AI technology. In: Braunschweig B. & Ghallab M. (Eds.) Reflections on artificial intelligence for humanity. Lecture Notes in Computer Science no. 12600 Cham: Springer. 90-115.
- Serban A., Blom K. van der, Hoos H.H. & Visser J. (2021), Practices for engineering trustworthy machine learning applications. In: 2021 IEEE/ACM 1st Workshop on AI engineering - software engineering for AI (WAIN).: IEEE. 97-100.
- Leyman P. & Hoos H.H. (2021), Smarter automatic algorithm configuration for the capacitated vehicle routing problem. In: 31st European conference on operational research..
- König H.M.T., Hoos H.H. & Rijn J.N. van (2021), Speeding up neural network verification via automated algorithm configuration. In: ICLR Workshop on Security and Safety in Machine Learning Systems..
- Matricon T., Anastacio M.I.A., Fijalkow N., Simon L. & Hoos H.H. (2021), Statistical comparison of algorithm performance through instance selection. In: Michel L.D. (Ed.) 27th International conference on principles and practice of constraint programming (CP 2021). no. 210: Dagstuhl Publishing. 43:1-43:21.
- Leyman P. & Hoos H.H. (2020), Automatic algorithm configuration: Instance-specific or not? Annual Conference of the Belgian Operational Research Society. 108-109.
- Serban A., Blom K. van der, Hoos H.H. & Visser J.M.W. (2020), 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). New York, NY: ACM. 1-12.
- 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