[{"citation":{"ama":"Schede E, Brandt J, Tornede A, et al. A Survey of Methods for Automated Algorithm Configuration. <i>arXiv:220201651</i>. Published online 2022.","chicago":"Schede, Elias, Jasmin Brandt, Alexander Tornede, Marcel Dominik Wever, Viktor Bengs, Eyke Hüllermeier, and Kevin Tierney. “A Survey of Methods for Automated Algorithm Configuration.” <i>ArXiv:2202.01651</i>, 2022.","ieee":"E. Schede <i>et al.</i>, “A Survey of Methods for Automated Algorithm Configuration,” <i>arXiv:2202.01651</i>. 2022.","short":"E. Schede, J. Brandt, A. Tornede, M.D. Wever, V. Bengs, E. Hüllermeier, K. Tierney, ArXiv:2202.01651 (2022).","mla":"Schede, Elias, et al. “A Survey of Methods for Automated Algorithm Configuration.” <i>ArXiv:2202.01651</i>, 2022.","bibtex":"@article{Schede_Brandt_Tornede_Wever_Bengs_Hüllermeier_Tierney_2022, title={A Survey of Methods for Automated Algorithm Configuration}, journal={arXiv:2202.01651}, author={Schede, Elias and Brandt, Jasmin and Tornede, Alexander and Wever, Marcel Dominik and Bengs, Viktor and Hüllermeier, Eyke and Tierney, Kevin}, year={2022} }","apa":"Schede, E., Brandt, J., Tornede, A., Wever, M. D., Bengs, V., Hüllermeier, E., &#38; Tierney, K. (2022). A Survey of Methods for Automated Algorithm Configuration. In <i>arXiv:2202.01651</i>."},"year":"2022","title":"A Survey of Methods for Automated Algorithm Configuration","author":[{"first_name":"Elias","full_name":"Schede, Elias","last_name":"Schede"},{"last_name":"Brandt","full_name":"Brandt, Jasmin","first_name":"Jasmin"},{"last_name":"Tornede","id":"38209","full_name":"Tornede, Alexander","first_name":"Alexander"},{"orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","full_name":"Wever, Marcel Dominik","id":"33176","first_name":"Marcel Dominik"},{"first_name":"Viktor","id":"76599","full_name":"Bengs, Viktor","last_name":"Bengs"},{"last_name":"Hüllermeier","id":"48129","full_name":"Hüllermeier, Eyke","first_name":"Eyke"},{"first_name":"Kevin","full_name":"Tierney, Kevin","last_name":"Tierney"}],"date_created":"2022-04-12T12:00:08Z","date_updated":"2022-04-12T12:01:15Z","status":"public","abstract":[{"text":"Algorithm configuration (AC) is concerned with the automated search of the\r\nmost suitable parameter configuration of a parametrized algorithm. There is\r\ncurrently a wide variety of AC problem variants and methods proposed in the\r\nliterature. Existing reviews do not take into account all derivatives of the AC\r\nproblem, nor do they offer a complete classification scheme. To this end, we\r\nintroduce taxonomies to describe the AC problem and features of configuration\r\nmethods, respectively. We review existing AC literature within the lens of our\r\ntaxonomies, outline relevant design choices of configuration approaches,\r\ncontrast methods and problem variants against each other, and describe the\r\nstate of AC in industry. Finally, our review provides researchers and\r\npractitioners with a look at future research directions in the field of AC.","lang":"eng"}],"publication":"arXiv:2202.01651","type":"preprint","language":[{"iso":"eng"}],"department":[{"_id":"34"},{"_id":"7"},{"_id":"26"}],"user_id":"38209","external_id":{"arxiv":["2202.01651"]},"_id":"30868","project":[{"name":"SFB 901: SFB 901","_id":"1"},{"name":"SFB 901 - B: SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - B2: SFB 901 - Subproject B2","_id":"10"}]},{"citation":{"apa":"Tornede, A., Bengs, V., &#38; Hüllermeier, E. (2022). Machine Learning for Online Algorithm Selection under Censored Feedback. In <i>Proceedings of the 36th AAAI Conference on Artificial Intelligence</i>. AAAI.","short":"A. Tornede, V. Bengs, E. Hüllermeier, Proceedings of the 36th AAAI Conference on Artificial Intelligence (2022).","mla":"Tornede, Alexander, et al. “Machine Learning for Online Algorithm Selection under Censored Feedback.” <i>Proceedings of the 36th AAAI Conference on Artificial Intelligence</i>, AAAI, 2022.","bibtex":"@article{Tornede_Bengs_Hüllermeier_2022, title={Machine Learning for Online Algorithm Selection under Censored Feedback}, journal={Proceedings of the 36th AAAI Conference on Artificial Intelligence}, publisher={AAAI}, author={Tornede, Alexander and Bengs, Viktor and Hüllermeier, Eyke}, year={2022} }","ama":"Tornede A, Bengs V, Hüllermeier E. Machine Learning for Online Algorithm Selection under Censored Feedback. <i>Proceedings of the 36th AAAI Conference on Artificial Intelligence</i>. Published online 2022.","ieee":"A. Tornede, V. Bengs, and E. Hüllermeier, “Machine Learning for Online Algorithm Selection under Censored Feedback,” <i>Proceedings of the 36th AAAI Conference on Artificial Intelligence</i>. AAAI, 2022.","chicago":"Tornede, Alexander, Viktor Bengs, and Eyke Hüllermeier. “Machine Learning for Online Algorithm Selection under Censored Feedback.” <i>Proceedings of the 36th AAAI Conference on Artificial Intelligence</i>. AAAI, 2022."},"year":"2022","author":[{"last_name":"Tornede","id":"38209","full_name":"Tornede, Alexander","first_name":"Alexander"},{"first_name":"Viktor","id":"76599","full_name":"Bengs, Viktor","last_name":"Bengs"},{"first_name":"Eyke","last_name":"Hüllermeier","id":"48129","full_name":"Hüllermeier, Eyke"}],"date_created":"2022-04-12T11:58:56Z","date_updated":"2022-08-24T12:44:27Z","publisher":"AAAI","title":"Machine Learning for Online Algorithm Selection under Censored Feedback","publication":"Proceedings of the 36th AAAI Conference on Artificial Intelligence","type":"preprint","status":"public","abstract":[{"lang":"eng","text":"In online algorithm selection (OAS), instances of an algorithmic problem\r\nclass are presented to an agent one after another, and the agent has to quickly\r\nselect a presumably best algorithm from a fixed set of candidate algorithms.\r\nFor decision problems such as satisfiability (SAT), quality typically refers to\r\nthe algorithm's runtime. As the latter is known to exhibit a heavy-tail\r\ndistribution, an algorithm is normally stopped when exceeding a predefined\r\nupper time limit. As a consequence, machine learning methods used to optimize\r\nan algorithm selection strategy in a data-driven manner need to deal with\r\nright-censored samples, a problem that has received little attention in the\r\nliterature so far. In this work, we revisit multi-armed bandit algorithms for\r\nOAS and discuss their capability of dealing with the problem. Moreover, we\r\nadapt them towards runtime-oriented losses, allowing for partially censored\r\ndata while keeping a space- and time-complexity independent of the time\r\nhorizon. In an extensive experimental evaluation on an adapted version of the\r\nASlib benchmark, we demonstrate that theoretically well-founded methods based\r\non Thompson sampling perform specifically strong and improve in comparison to\r\nexisting methods."}],"department":[{"_id":"34"},{"_id":"7"},{"_id":"26"}],"user_id":"38209","_id":"30867","external_id":{"arxiv":["2109.06234"]},"project":[{"_id":"1","name":"SFB 901: SFB 901"},{"_id":"3","name":"SFB 901 - B: SFB 901 - Project Area B"},{"name":"SFB 901 - B2: SFB 901 - Subproject B2","_id":"10"}],"language":[{"iso":"eng"}]},{"status":"public","type":"book_chapter","project":[{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"_id":"18014","series_title":"Lecture Notes in Computer Science","user_id":"76599","department":[{"_id":"34"},{"_id":"7"},{"_id":"355"}],"place":"Cham","citation":{"chicago":"El Mesaoudi-Paul, Adil, Dimitri Weiß, Viktor Bengs, Eyke Hüllermeier, and Kevin Tierney. “Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach.” In <i>Learning and Intelligent Optimization. LION 2020.</i>, 12096:216–32. Lecture Notes in Computer Science. Cham: Springer, 2020. <a href=\"https://doi.org/10.1007/978-3-030-53552-0_22\">https://doi.org/10.1007/978-3-030-53552-0_22</a>.","ieee":"A. El Mesaoudi-Paul, D. Weiß, V. Bengs, E. Hüllermeier, and K. Tierney, “Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach,” in <i>Learning and Intelligent Optimization. LION 2020.</i>, vol. 12096, Cham: Springer, 2020, pp. 216–232.","ama":"El Mesaoudi-Paul A, Weiß D, Bengs V, Hüllermeier E, Tierney K. Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach. In: <i>Learning and Intelligent Optimization. LION 2020.</i> Vol 12096. Lecture Notes in Computer Science. Cham: Springer; 2020:216-232. doi:<a href=\"https://doi.org/10.1007/978-3-030-53552-0_22\">10.1007/978-3-030-53552-0_22</a>","apa":"El Mesaoudi-Paul, A., Weiß, D., Bengs, V., Hüllermeier, E., &#38; Tierney, K. (2020). Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach. In <i>Learning and Intelligent Optimization. LION 2020.</i> (Vol. 12096, pp. 216–232). Cham: Springer. <a href=\"https://doi.org/10.1007/978-3-030-53552-0_22\">https://doi.org/10.1007/978-3-030-53552-0_22</a>","mla":"El Mesaoudi-Paul, Adil, et al. “Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach.” <i>Learning and Intelligent Optimization. LION 2020.</i>, vol. 12096, Springer, 2020, pp. 216–32, doi:<a href=\"https://doi.org/10.1007/978-3-030-53552-0_22\">10.1007/978-3-030-53552-0_22</a>.","short":"A. El Mesaoudi-Paul, D. Weiß, V. Bengs, E. Hüllermeier, K. Tierney, in: Learning and Intelligent Optimization. LION 2020., Springer, Cham, 2020, pp. 216–232.","bibtex":"@inbook{El Mesaoudi-Paul_Weiß_Bengs_Hüllermeier_Tierney_2020, place={Cham}, series={Lecture Notes in Computer Science}, title={Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach}, volume={12096}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-53552-0_22\">10.1007/978-3-030-53552-0_22</a>}, booktitle={Learning and Intelligent Optimization. LION 2020.}, publisher={Springer}, author={El Mesaoudi-Paul, Adil and Weiß, Dimitri and Bengs, Viktor and Hüllermeier, Eyke and Tierney, Kevin}, year={2020}, pages={216–232}, collection={Lecture Notes in Computer Science} }"},"page":"216 - 232","intvolume":"     12096","publication_status":"published","publication_identifier":{"issn":["0302-9743","1611-3349"],"isbn":["9783030535513","9783030535520"]},"doi":"10.1007/978-3-030-53552-0_22","date_updated":"2022-01-06T06:53:25Z","author":[{"first_name":"Adil","last_name":"El Mesaoudi-Paul","full_name":"El Mesaoudi-Paul, Adil"},{"first_name":"Dimitri","full_name":"Weiß, Dimitri","last_name":"Weiß"},{"id":"76599","full_name":"Bengs, Viktor","last_name":"Bengs","first_name":"Viktor"},{"first_name":"Eyke","id":"48129","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier"},{"full_name":"Tierney, Kevin","last_name":"Tierney","first_name":"Kevin"}],"volume":12096,"publication":"Learning and Intelligent Optimization. LION 2020.","language":[{"iso":"eng"}],"year":"2020","title":"Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach","publisher":"Springer","date_created":"2020-08-17T11:44:37Z"},{"citation":{"ieee":"A. El Mesaoudi-Paul, V. Bengs, and E. Hüllermeier, “Online Preselection with Context Information under the Plackett-Luce  Model,” <i>arXiv:2002.04275</i>. .","chicago":"El Mesaoudi-Paul, Adil, Viktor Bengs, and Eyke Hüllermeier. “Online Preselection with Context Information under the Plackett-Luce  Model.” <i>ArXiv:2002.04275</i>, n.d.","ama":"El Mesaoudi-Paul A, Bengs V, Hüllermeier E. Online Preselection with Context Information under the Plackett-Luce  Model. <i>arXiv:200204275</i>.","bibtex":"@article{El Mesaoudi-Paul_Bengs_Hüllermeier, title={Online Preselection with Context Information under the Plackett-Luce  Model}, journal={arXiv:2002.04275}, author={El Mesaoudi-Paul, Adil and Bengs, Viktor and Hüllermeier, Eyke} }","mla":"El Mesaoudi-Paul, Adil, et al. “Online Preselection with Context Information under the Plackett-Luce  Model.” <i>ArXiv:2002.04275</i>.","short":"A. El Mesaoudi-Paul, V. Bengs, E. Hüllermeier, ArXiv:2002.04275 (n.d.).","apa":"El Mesaoudi-Paul, A., Bengs, V., &#38; Hüllermeier, E. (n.d.). Online Preselection with Context Information under the Plackett-Luce  Model. <i>ArXiv:2002.04275</i>."},"year":"2020","publication_status":"draft","title":"Online Preselection with Context Information under the Plackett-Luce  Model","date_created":"2020-08-17T11:49:40Z","author":[{"last_name":"El Mesaoudi-Paul","full_name":"El Mesaoudi-Paul, Adil","first_name":"Adil"},{"first_name":"Viktor","last_name":"Bengs","id":"76599","full_name":"Bengs, Viktor"},{"last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","id":"48129","first_name":"Eyke"}],"date_updated":"2022-01-06T06:53:25Z","status":"public","abstract":[{"text":"We consider an extension of the contextual multi-armed bandit problem, in\r\nwhich, instead of selecting a single alternative (arm), a learner is supposed\r\nto make a preselection in the form of a subset of alternatives. More\r\nspecifically, in each iteration, the learner is presented a set of arms and a\r\ncontext, both described in terms of feature vectors. The task of the learner is\r\nto preselect $k$ of these arms, among which a final choice is made in a second\r\nstep. In our setup, we assume that each arm has a latent (context-dependent)\r\nutility, and that feedback on a preselection is produced according to a\r\nPlackett-Luce model. We propose the CPPL algorithm, which is inspired by the\r\nwell-known UCB algorithm, and evaluate this algorithm on synthetic and real\r\ndata. In particular, we consider an online algorithm selection scenario, which\r\nserved as a main motivation of our problem setting. Here, an instance (which\r\ndefines the context) from a certain problem class (such as SAT) can be solved\r\nby different algorithms (the arms), but only $k$ of these algorithms can\r\nactually be run.","lang":"eng"}],"type":"preprint","publication":"arXiv:2002.04275","language":[{"iso":"eng"}],"user_id":"76599","department":[{"_id":"34"},{"_id":"7"},{"_id":"355"}],"project":[{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"_id":"18017"},{"language":[{"iso":"eng"}],"_id":"14027","department":[{"_id":"34"},{"_id":"355"}],"user_id":"76599","status":"public","publication":"Journal of Multivariate Analysis","type":"journal_article","title":"Asymptotic confidence sets for the jump curve in bivariate regression problems","doi":"10.1016/j.jmva.2019.02.017","date_updated":"2022-01-06T06:51:52Z","date_created":"2019-10-30T14:22:57Z","author":[{"last_name":"Bengs","full_name":"Bengs, Viktor","id":"76599","first_name":"Viktor"},{"first_name":"Matthias","last_name":"Eulert","full_name":"Eulert, Matthias"},{"last_name":"Holzmann","full_name":"Holzmann, Hajo","first_name":"Hajo"}],"year":"2019","page":"291-312","citation":{"mla":"Bengs, Viktor, et al. “Asymptotic Confidence Sets for the Jump Curve in Bivariate Regression Problems.” <i>Journal of Multivariate Analysis</i>, 2019, pp. 291–312, doi:<a href=\"https://doi.org/10.1016/j.jmva.2019.02.017\">10.1016/j.jmva.2019.02.017</a>.","short":"V. Bengs, M. Eulert, H. Holzmann, Journal of Multivariate Analysis (2019) 291–312.","bibtex":"@article{Bengs_Eulert_Holzmann_2019, title={Asymptotic confidence sets for the jump curve in bivariate regression problems}, DOI={<a href=\"https://doi.org/10.1016/j.jmva.2019.02.017\">10.1016/j.jmva.2019.02.017</a>}, journal={Journal of Multivariate Analysis}, author={Bengs, Viktor and Eulert, Matthias and Holzmann, Hajo}, year={2019}, pages={291–312} }","apa":"Bengs, V., Eulert, M., &#38; Holzmann, H. (2019). Asymptotic confidence sets for the jump curve in bivariate regression problems. <i>Journal of Multivariate Analysis</i>, 291–312. <a href=\"https://doi.org/10.1016/j.jmva.2019.02.017\">https://doi.org/10.1016/j.jmva.2019.02.017</a>","chicago":"Bengs, Viktor, Matthias Eulert, and Hajo Holzmann. “Asymptotic Confidence Sets for the Jump Curve in Bivariate Regression Problems.” <i>Journal of Multivariate Analysis</i>, 2019, 291–312. <a href=\"https://doi.org/10.1016/j.jmva.2019.02.017\">https://doi.org/10.1016/j.jmva.2019.02.017</a>.","ieee":"V. Bengs, M. Eulert, and H. Holzmann, “Asymptotic confidence sets for the jump curve in bivariate regression problems,” <i>Journal of Multivariate Analysis</i>, pp. 291–312, 2019.","ama":"Bengs V, Eulert M, Holzmann H. Asymptotic confidence sets for the jump curve in bivariate regression problems. <i>Journal of Multivariate Analysis</i>. 2019:291-312. doi:<a href=\"https://doi.org/10.1016/j.jmva.2019.02.017\">10.1016/j.jmva.2019.02.017</a>"},"publication_identifier":{"issn":["0047-259X"]},"publication_status":"published"},{"type":"journal_article","publication":"Electronic Journal of Statistics","status":"public","_id":"14028","user_id":"76599","department":[{"_id":"34"},{"_id":"355"}],"language":[{"iso":"eng"}],"publication_status":"published","publication_identifier":{"issn":["1935-7524"]},"year":"2019","citation":{"chicago":"Bengs, Viktor, and Hajo Holzmann. “Adaptive Confidence Sets for Kink Estimation.” <i>Electronic Journal of Statistics</i>, 2019, 1523–79. <a href=\"https://doi.org/10.1214/19-ejs1555\">https://doi.org/10.1214/19-ejs1555</a>.","ieee":"V. Bengs and H. Holzmann, “Adaptive confidence sets for kink estimation,” <i>Electronic Journal of Statistics</i>, pp. 1523–1579, 2019.","ama":"Bengs V, Holzmann H. Adaptive confidence sets for kink estimation. <i>Electronic Journal of Statistics</i>. 2019:1523-1579. doi:<a href=\"https://doi.org/10.1214/19-ejs1555\">10.1214/19-ejs1555</a>","apa":"Bengs, V., &#38; Holzmann, H. (2019). Adaptive confidence sets for kink estimation. <i>Electronic Journal of Statistics</i>, 1523–1579. <a href=\"https://doi.org/10.1214/19-ejs1555\">https://doi.org/10.1214/19-ejs1555</a>","bibtex":"@article{Bengs_Holzmann_2019, title={Adaptive confidence sets for kink estimation}, DOI={<a href=\"https://doi.org/10.1214/19-ejs1555\">10.1214/19-ejs1555</a>}, journal={Electronic Journal of Statistics}, author={Bengs, Viktor and Holzmann, Hajo}, year={2019}, pages={1523–1579} }","mla":"Bengs, Viktor, and Hajo Holzmann. “Adaptive Confidence Sets for Kink Estimation.” <i>Electronic Journal of Statistics</i>, 2019, pp. 1523–79, doi:<a href=\"https://doi.org/10.1214/19-ejs1555\">10.1214/19-ejs1555</a>.","short":"V. Bengs, H. Holzmann, Electronic Journal of Statistics (2019) 1523–1579."},"page":"1523-1579","date_updated":"2022-01-06T06:51:52Z","date_created":"2019-10-30T14:25:16Z","author":[{"last_name":"Bengs","id":"76599","full_name":"Bengs, Viktor","first_name":"Viktor"},{"first_name":"Hajo","full_name":"Holzmann, Hajo","last_name":"Holzmann"}],"title":"Adaptive confidence sets for kink estimation","doi":"10.1214/19-ejs1555"},{"type":"dissertation","year":"2018","status":"public","citation":{"ama":"Bengs V. <i>Confidence Sets for Change-Point Problems in Nonparametric Regression </i>.; 2018. doi:<a href=\"https://doi.org/10.17192/z2018.0511\">https://doi.org/10.17192/z2018.0511</a>","chicago":"Bengs, Viktor. <i>Confidence Sets for Change-Point Problems in Nonparametric Regression </i>, 2018. <a href=\"https://doi.org/10.17192/z2018.0511\">https://doi.org/10.17192/z2018.0511</a>.","ieee":"V. Bengs, <i>Confidence sets for change-point problems in nonparametric regression </i>. 2018.","bibtex":"@book{Bengs_2018, title={Confidence sets for change-point problems in nonparametric regression }, DOI={<a href=\"https://doi.org/10.17192/z2018.0511\">https://doi.org/10.17192/z2018.0511</a>}, author={Bengs, Viktor}, year={2018} }","mla":"Bengs, Viktor. <i>Confidence Sets for Change-Point Problems in Nonparametric Regression </i>. 2018, doi:<a href=\"https://doi.org/10.17192/z2018.0511\">https://doi.org/10.17192/z2018.0511</a>.","short":"V. Bengs, Confidence Sets for Change-Point Problems in Nonparametric Regression , 2018.","apa":"Bengs, V. (2018). <i>Confidence sets for change-point problems in nonparametric regression </i>. <a href=\"https://doi.org/10.17192/z2018.0511\">https://doi.org/10.17192/z2018.0511</a>"},"_id":"14031","date_updated":"2022-01-06T06:51:52Z","user_id":"76599","author":[{"last_name":"Bengs","full_name":"Bengs, Viktor","id":"76599","first_name":"Viktor"}],"date_created":"2019-10-30T14:27:45Z","title":"Confidence sets for change-point problems in nonparametric regression ","doi":"https://doi.org/10.17192/z2018.0511","language":[{"iso":"eng"}]}]