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
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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
- Wasala J., Marselis S.M., Arp L.R., Hoos H.H., Longépé N. & Baratchi M. (2024), AutoSR4EO: an autoML approach to super-resolution for earth observation images, Remote Sensing 16(3): 443.
- König H.M.T., Bosman A.W., Hoos H.H. & Rijn J.N. van (2024), Critically assessing the state of the art in neural network verification, Journal of Machine Learning Research 25(12): 1-35.
- König H.M.T., Hoos H.H. & Rijn J.N. van (2024), Accelerating adversarially robust model selection for deep neural networks via racing, Proceedings of the AAAI Conference on Artificial Intelligence. 38th AAAI Conference on Artificial Intelligence (AAAI-24) 19 February 2024 - 27 February 2024. Proceedings of the AAAI Conference on Artificial Intelligence no. 38. Washington, DC, USA: AAAI Press. 21267-21275.
- König H.M.T., Bosman A.W., Hoos H.H. & Rijn J.N. van (2023), Critically assessing the state of the art in CPU-based local robustness verification. Pedroza G., Huang X., Chen X., Theodorou A., Hernandez-Orallo J., Castillo-Effen M., Mallah R. & McDermid J. (Eds.), Proceedings of the workshop on artificial intelligence safety 2023 (SafeAI 2023). SafeAI 2023: Workshop on Artificial Intelligence Safety 13 February 2023 - 14 February 2023. CEUR Workshop Proceedings no. 3381: CEUR-WS.
- Schlender T., Viljanen M., Rijn J.N. van, Mohr F., Peijnenburg W.J.G.M., Hoos H.H., Rorije E. & Wong A. (2023), The bigger fish: a comparison of meta-learning QSAR models on low-resourced aquatic toxicity regression tasks, Environmental Science and Technology 57(46): 17818-17830.
- Chu Y., Luo C., Hoos H.H. & You H.H. (2023), Improving the performance of stochastic local search for maximum vertex weight clique problem using programming by optimization, Expert Systems with Applications 213(Part B): 118913.
- Renting B.M., Hoos H.H. & Jonker C.M. (2022), Automated configuration and usage of strategy portfolios for mixed-motive bargaining. In: AAMAS '22: Proceedings of the 21st international conference on autonomous agents and multiagent systems. Richland, S.C.: International Foundation for Autonomous Agents and Multiagent Systems. 1101-1109.
- Tetteroo J., Baratchi M. & Hoos H.H. (2022), Automated machine learning for COVID-19 forecasting, IEEE Access 10: 94718-94737.
- Arp L.R., Baratchi M. & Hoos H.H. (2022), VPint: value propagation-based spatial interpolation, Data Mining and Knowledge Discovery 36(5): 1647-1678.
- König H.M.T., Hoos H.H. & Rijn J.N. van (2022), Speeding up neural network robustness verification via algorithm configuration and an optimised mixed integer linear programming solver portfolio, Machine Learning 111: 4565-4584.
- Wang C., Baratchi M., Bäck T.H.W., Hoos H.H., Limmer S. & Olhofer M. (2022), Towards time-series feature engineering in automated machine learning for multi-step-ahead forecasting. Rojas I., Pomares H., Valenzuela O., Rojas F. & Herrera L.J. (Eds.), Engineering Proceedings. The 8th International Conference on Time Series and Forecasting 27 June 2022 - 30 June 2022 no. 18: MDPI. 17.
- Latour A.L.D., Babaki B., Fokkinga D., Anastacio M.I.A., Hoos H.H. & Nijssen S.G.R. (2022), Exact stochastic constraint optimisation with applications in network analysis, Artificial Intelligence 304: 103650 (103650).
- Blom K. van der, Hoos H.H., Luo C. & Rook J.G. (2022), Sparkle: toward accessible meta-algorithmics for improving the state of the art in solving challenging problems, IEEE Transactions on Evolutionary Computation 26(6): 1351-1364.
- 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.
- 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.
- 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.
- Lei Z., Cai S., Luo C. & Hoos H.H. (2021), Efficient local search for Pseudo Boolean Optimization. Li C.M. & Manyà F. (Eds.), Theory and applications of satisfiability testing – SAT 2021. International Conference on Theory and Applications of Satisfiability Testing SAT 2021 5 July 2021 - 9 July 2021 no. 12831. Cham: Springer. 332-348.
- Leyman P. & Hoos H.H. (2021), Smarter automatic algorithm configuration for the capacitated vehicle routing problem, 31st European conference on operational research. 31st European Conference on Operational Research 11 July 2021 - 14 July 2021.
- Veloso B., Carprese L., König H.M.T., Teixeira S., Manco G., Hoos H.H. & Gama J. (2021), Hyper-parameter optimization for latent spaces. 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. Joint European Conference on Machine Learning and Knowledge Discovery in Databasis. ECML PKDD 2021 13 September 2021 - 17 September 2021 no. 12977. Cham: Springer International Publishing. 249-264.
- König H.M.T., Hoos H.H. & Rijn J.N. van (2021), Speeding up neural network verification via automated algorithm configuration, ICLR Workshop on Security and Safety in Machine Learning Systems. Workshop Security and Safety in Machine Learning Systems 7 May 2021 - 7 May 2021.
- Serban A., Blom K. van der, Hoos H.H. & Visser J. (2021), Practices for engineering trustworthy machine learning applications, 2021 IEEE/ACM 1st Workshop on AI engineering - software engineering for AI (WAIN). 2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI (WAIN) 30 May 2021 - 31 May 2021: IEEE. 97-100.
- 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..
- Matricon T., Anastacio M.I.A., Fijalkow N., Simon L. & Hoos H.H. (2021), Statistical comparison of algorithm performance through instance selection. Michel L.D. (Ed.), 27th International conference on principles and practice of constraint programming (CP 2021). 27th International Conference on Principles and Practice of Constraint Programmin (CP 2021) 25 October 2021 - 29 October 2021 no. 210: Dagstuhl Publishing. 43:1-43:21.
- Leyman P. & Hoos H.H. (2020), Automatic algorithm configuration: instance-specific or not?. 34th Annual Conference of the Belgian Operational Research Society. ORBEL34 30 January 2020 - 31 January 2020. Annual Conference of the Belgian Operational Research Society 108-109.
- König H.M.T., Hoos H.H. & Rijn J.N. van (2020), Towards algorithm-agnostic uncertainty estimation: predicting classification error in an automated machine learning setting, ICML Workshop on automated machine learning. 7th ICML Workshop on Automated Machine Learning (AutoML 2020) 18 July 2020 - 18 July 2020.
- 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, Proceedings of the 14th ACM / IEEE international symposium on empirical software engineering and measurement (ESEM). 14th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement. ESEM '20 5 October 2020 - 9 October 2020. 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, 2019 IEEE Symposium Series on Computational Intelligence (SSCI). 2019 IEEE Symposium Series on Computational Intelligence (SSCI) 6 December 2019 - 9 December 2019: IEEE. 314-321.
- Blot A., Hoos H.H., Kessaci M.E. & Jourdan L. (2018), Automatic Configuration of Multi-objective Optimization Algorithms. Impact of Correlation between Objectives, Proceedings of the 30th International Conference on Tools with Artificial Intelligence ({ICTAI} 2018). ICTAI 2018 30th International Conference on Tools with Artificial Intelligence 5 November 2018 - 7 November 2018: IEEE.
- 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., Peitl T., Slivovsky F. & Szeider S. (2018), Portfolio-Based Algorithm Selection for Circuit QBFs. Hooker J. (Ed.), Principles and Practice of Constraint Programming - 24th International Conference, CP 2018, Lille, France, August 27-31, 2018, Proceedings. the 24th International Conference on Principles and Practice of Constraint Programming (CP 2018) 27 August 2018 - 31 August 2018 no. Programming and Software Engineering, Volume 11008: Springer International Publishing. 195-209.
- 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.
- Kodirov N., Bayless S., Ruffy F., Beschastnikh I., Hoos H.H. & Hu A.J. (2018), VNF chain allocation and management at data center scale. Sierra C. (Ed.), ANCS '18 Proceedings of the 2018 Symposium on Architectures for Networking and Communications Systems. The 14th ACM/IEEE Symposium on Architectures for Networking and Communications Systems (ANCS 2018) 23 July 2018 - 24 July 2018. New York: ACM. 125-140.
- Kotthoff L., Fréchette A., Michalak T., Rahwan T., Hoos H.H. & Leyton-Brown K. (2018), Quantifying Algorithmic Improvements over Time. Lang J. (Ed.), Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI-18). 27th International Conference on Artificial Intelligence IJCAI-18 13 July 2018 - 19 July 2018: 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.
- Martinez J., Tallavajhula S., Hoos H.H. & Little J.J. (2018), LSQ++: Lower Running Time and Higher Recall in Multi-codebook Quantization. Ferrari V., Hebert M., Sminchisescu C. & Weiss Y. (Eds.), Proceedings of the 15th European Conference on Computer Vision ({ECCV} 2018). European Conference on Computer Vision ECCV 2018 8 September 2018 - 14 September 2018 no. Lecture Notes in Computer Science, volume 11220. Cham: Springer. 508-523.
- 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?. 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. PPSN: International Conference on Parallel Solving from Nature 8 September 2018 - 12 September 2018 no. Theoretical Computer Science and General Issues, Volume 11102: Springer International Publishing. 271-283.
- 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, Proceedings of the 23rd International Conference on Science and Technology Indicators. 23rd International Conference on Science and Technology Indicators 12 September 2018 - 14 September 2018 1114-1122.
- Cameron C., Hoos H.H., Leyton-Brown K. & Hutter F. (2017), OASC-2017: *Zilla Submission. Lindauer M., Rijn J.N. van & Kotthoff L. (Eds.), Proceedings Machine Learning Research. Open Algorithm Selection Challenge (OASC 2017) 11 September 2017 - 12 September 2017 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. Sierra C. (Ed.), Proceedings of the 26th International Joint Conference on Artificial Intelligence. The 26th International Joint Conference on Artificial Intelligence (IJCAI 2017) 19 August 2017 - 25 August 2017: International Joint Conferences on Artificial Inteligence. 546--554.
- Cáceres L.P., López-Ibáñez M., Hoos H.H. & Stützle T. (2017), An Experimental Study of Adaptive Capping in irace. Battiti R., Kvasov D.E. & Sergeyev Y.D. (Eds.), Learning and Intelligent Optimization. LION 2017. International Conference on Learning and Intelligent Optimization (LION11 2017) 19 June 2017 - 21 June 2017 no. Lecture Notes in Computer Science 10556. Cham: Springer. 235-250.
- Blot A., Pernet A., Jourdan L., Kessaci-Marmion M.E. & Hoos H.H. (2017), Automatically Configuring Multi-objective Local Search Using Multi-objective Optimisation. 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. 9th International Conference on Evolutionary Multi-Criterion Optimization 19 March 2017 - 22 March 2017 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.
- 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.
- 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.
- Hoos H.H., Neumann F. & Trautmann H. (2017), Automated Algorithm Selection and Configuration (Dagstuhl Seminar 16412), Dagstuhl Reports 6(10): 33-74.
- 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. Singh S.P. & Markovitch S. (Eds.), Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI-17). The 31st AAAI Conference on Artificial Intelligence (AAAI-17) 4 February 2017 - 9 February 2017. Palo Alto, CA: AAAI Press. 773-779.
- Lindauer M., Hutter F., Hoos H.H. & Schaub T. (2017), AutoFolio: An Automatically Configured Algorithm Selector (Extended Abstract). Sierra C. (Ed.), Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI-17). The 26th International Joint Conference on Artificial Intelligence (IJCAI 2017) 19 August 2017 - 25 August 2017: International Joint Conferences on Artificial Inteligence Organization. 5025-5029.
- 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, Barcelona. 12 March 2016 - 8 March 2016. [conference poster].
- Licensing of software developed based on my work at UBC, occasional consulting
- (Co-)supervision of students, joint research