{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T16:48:17Z","timestamp":1755794897020,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":99,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,7,20]],"date-time":"2025-07-20T00:00:00Z","timestamp":1752969600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0\/"}],"funder":[{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/100014013","name":"UK Research and Innovation","doi-asserted-by":"publisher","award":["MR\/X011135\/1"],"award-info":[{"award-number":["MR\/X011135\/1"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/100014013","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/100012338","name":"Alan Turing Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/100012338","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100000288","name":"Royal Society","doi-asserted-by":"publisher","award":["KTP\/R1\/231017"],"award-info":[{"award-number":["KTP\/R1\/231017"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100000288","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,7,20]]},"DOI":"10.1145\/3690624.3709229","type":"proceedings-article","created":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T18:48:32Z","timestamp":1743792512000},"page":"555-564","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["On the Hyperparameter Loss Landscapes of Machine Learning Models: An Exploratory Study"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2829-8673","authenticated-orcid":false,"given":"Mingyu","family":"Huang","sequence":"first","affiliation":[{"name":"University of Electronic Science and Technology of China, Chengdu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7200-4244","authenticated-orcid":false,"given":"Ke","family":"Li","sequence":"additional","affiliation":[{"name":"University of Exeter, Exeter, United Kingdom"}]}],"member":"320","published-online":{"date-parts":[[2025,7,20]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41559-016-0045"},{"key":"e_1_3_2_1_2_1","volume-title":"Optuna: A Next-generation Hyperparameter Optimization Framework. In KDD'19 : Proc. of the 25th ACM SIGKDD International Conference on Knowledge Discovery. ACM, 2623--2631","author":"Akiba Takuya","year":"2019","unstructured":"Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama. 2019. Optuna: A Next-generation Hyperparameter Optimization Framework. In KDD'19 : Proc. of the 25th ACM SIGKDD International Conference on Knowledge Discovery. ACM, 2623--2631."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10515-016-0197-7"},{"key":"e_1_3_2_1_4_1","volume-title":"Robust and Efficient Hyperparameter Optimization. In IJCAI'21: Proc. of the 13th International Joint Conference on Artificial Intelligence. ijcai.org, 2147--2153","author":"Awad Noor H.","year":"2021","unstructured":"Noor H. Awad, Neeratyoy Mallik, and Frank Hutter. 2021. DEHB: Evolutionary Hyberband for Scalable, Robust and Efficient Hyperparameter Optimization. In IJCAI'21: Proc. of the 13th International Joint Conference on Artificial Intelligence. ijcai.org, 2147--2153."},{"key":"e_1_3_2_1_5_1","volume-title":"NeurIPS'22: Proc. of Advances in Neural Information Processing Systems.","author":"Bansal Archit","year":"2022","unstructured":"Archit Bansal, Danny Stoll, Maciej Janowski, Arber Zela, and Frank Hutter. 2022. JAHS-Bench-201: A Foundation For Research On Joint Architecture And Hyperparameter Search. In NeurIPS'22: Proc. of Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_1_6_1","volume-title":"ICML'13: Proc. of the 30th International Conference on Machine Learning (JMLR Workshop and Conference Proceedings","volume":"207","author":"Bardenet R\u00e9mi","year":"2013","unstructured":"R\u00e9mi Bardenet, M\u00e1ty\u00e1s Brendel, Bal\u00e1zs K\u00e9gl, and Mich\u00e8le Sebag. 2013. Collaborative hyperparameter tuning. In ICML'13: Proc. of the 30th International Conference on Machine Learning (JMLR Workshop and Conference Proceedings, Vol. 28). JMLR.org, 199--207."},{"key":"e_1_3_2_1_7_1","volume-title":"NIPS'18: Proc. of Advances in Neural Information Processing Systems. 2306--2317","author":"Belkin Mikhail","year":"2018","unstructured":"Mikhail Belkin, Daniel J. Hsu, and Partha Mitra. 2018. Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate. In NIPS'18: Proc. of Advances in Neural Information Processing Systems. 2306--2317."},{"key":"e_1_3_2_1_8_1","volume-title":"Algorithms for Hyper-Parameter Optimization. In NIPS'11: Proc. of the 25th Annual Conference on Neural Information Processing Systems. 2546--2554","author":"Bergstra James","year":"2011","unstructured":"James Bergstra, R\u00e9mi Bardenet, Yoshua Bengio, and Bal\u00e1zs K\u00e9gl. 2011. Algorithms for Hyper-Parameter Optimization. In NIPS'11: Proc. of the 25th Annual Conference on Neural Information Processing Systems. 2546--2554."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.5555\/2188385.2188395"},{"key":"e_1_3_2_1_10_1","volume-title":"Visualization and Evaluation. In LION'18: Proc. of the 12th International Conference","volume":"11353","author":"Biedenkapp Andre","year":"2018","unstructured":"Andre Biedenkapp, Joshua Marben, Marius Lindauer, and Frank Hutter. 2018. CAVE: Configuration Assessment, Visualization and Evaluation. In LION'18: Proc. of the 12th International Conference, Vol. 11353. Springer, 115--130."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1484"},{"volume-title":"Conjugate Gradient, and Early Stopping. In NIPS'00: Proc. of Advances in Neural Information Processing Systems","author":"Caruana Rich","key":"e_1_3_2_1_12_1","unstructured":"Rich Caruana, Steve Lawrence, and C. Lee Giles. 2000. Overfitting in Neural Nets: Backpropagation, Conjugate Gradient, and Early Stopping. In NIPS'00: Proc. of Advances in Neural Information Processing Systems. MIT Press, 402--408."},{"key":"e_1_3_2_1_13_1","volume-title":"XGBoost: A Scalable Tree Boosting System. In SIGKDD'16: Proc. of the 22nd ACM International Conference on Knowledge Discovery and Data Mining. ACM, 785--794","author":"Chen Tianqi","year":"2016","unstructured":"Tianqi Chen and Carlos Guestrin. 2016. XGBoost: A Scalable Tree Boosting System. In SIGKDD'16: Proc. of the 22nd ACM International Conference on Knowledge Discovery and Data Mining. ACM, 785--794."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3253818"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrg3744"},{"key":"e_1_3_2_1_16_1","volume-title":"ICLR'20: Proc. of the 8th International Conference on Learning Representations. OpenReview.net.","author":"Dong Xuanyi","year":"2020","unstructured":"Xuanyi Dong and Yi Yang. 2020. NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search. In ICLR'20: Proc. of the 8th International Conference on Learning Representations. OpenReview.net."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.88.238701"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2023\/406"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3377325.3377501"},{"key":"e_1_3_2_1_20_1","volume-title":"HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO. In NeurIPS'21: Proc. of the Neural Information Processing Systems Track on Datasets and Benchmarks.","author":"Eggensperger Katharina","year":"2021","unstructured":"Katharina Eggensperger, Philipp M\u00fcller, Neeratyoy Mallik, Matthias Feurer, Ren\u00e9 Sass, Aaron Klein, Noor H. Awad, Marius Lindauer, and Frank Hutter. 2021. HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO. In NeurIPS'21: Proc. of the Neural Information Processing Systems Track on Datasets and Benchmarks."},{"key":"e_1_3_2_1_21_1","volume-title":"Hyperparameters in Reinforcement Learning and How To Tune Them. In ICML'23: Proc. of the International Conference on Machine Learning (Proceedings of Machine Learning Research","volume":"9149","author":"Eimer Theresa","year":"2023","unstructured":"Theresa Eimer, Marius Lindauer, and Roberta Raileanu. 2023. Hyperparameters in Reinforcement Learning and How To Tune Them. In ICML'23: Proc. of the International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 202). PMLR, 9104--9149."},{"key":"e_1_3_2_1_22_1","volume-title":"Neural network extrapolation to distant regions of the protein fitness landscape. bioRxiv","author":"Fahlberg Sarah A","year":"2023","unstructured":"Sarah A Fahlberg, Chase R Freschlin, Pete Heinzelman, and Philip A Romero. 2023. Neural network extrapolation to distant regions of the protein fitness landscape. bioRxiv (2023), 2023--11."},{"key":"e_1_3_2_1_23_1","volume-title":"ICML'18: Proc. of the 35th International Conference on Machine Learning","volume":"80","author":"Falkner Stefan","year":"2018","unstructured":"Stefan Falkner, Aaron Klein, and Frank Hutter. 2018. BOHB: Robust and Efficient Hyperparameter Optimization at Scale. In ICML'18: Proc. of the 35th International Conference on Machine Learning, Vol. 80. PMLR, 1436--1445."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v29i1.9354"},{"key":"e_1_3_2_1_25_1","volume-title":"Greedy function approximation: A gradient boosting machine. Annals of statistics","author":"Friedman Jerome H.","year":"2001","unstructured":"Jerome H. Friedman. 2001. Greedy function approximation: A gradient boosting machine. Annals of statistics (2001), 1189--1232."},{"key":"e_1_3_2_1_26_1","first-page":"2672","article-title":"Generative Adversarial Nets. In NIPS'14","volume":"27","author":"Goodfellow Ian J.","year":"2014","unstructured":"Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio. 2014. Generative Adversarial Nets. In NIPS'14: Proc. Advances in Neural Information Processing Systems 27. 2672--2680.","journal-title":"Proc. Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_27_1","unstructured":"L\u00e9o Grinsztajn Edouard Oyallon and Ga\u00ebl Varoquaux. 2022. Why do tree-based models still outperform deep learning on typical tabular data?. In NeurIPS."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1080\/10556788.2020.1808977"},{"key":"e_1_3_2_1_29_1","unstructured":"Moritz Herrmann F. Julian D. Lange Katharina Eggensperger Giuseppe Casalicchio Marcel Wever Matthias Feurer David Rugamer Eyke Hullermeier Anne-Laure Boulesteix and Bernd Bischl. 2024. Position Paper: Rethinking Empirical Research in Machine Learning: Addressing Epistemic and Methodological Challenges of Experimentation. In ICML'24: Proc. of the 41th International Conference on Machine Learning (Proceedings of Machine Learning Research). PMLR accepted for publication."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2023\/621"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"crossref","unstructured":"Mingyu Huang Peili Mao and Ke Li. 2025. Rethinking Performance Analysis for Configurable Software Systems: A Case Study from a Fitness Landscape Perspective. arxiv: 2412.16888","DOI":"10.1145\/3728954"},{"key":"e_1_3_2_1_32_1","volume-title":"Sequential Model-Based Optimization for General Algorithm Configuration. In LION'11: Proc. of the 5th International Conference on Learning and Intelligent Optimization","volume":"6683","author":"Hutter Frank","year":"2011","unstructured":"Frank Hutter, Holger H. Hoos, and Kevin Leyton-Brown. 2011. Sequential Model-Based Optimization for General Algorithm Configuration. In LION'11: Proc. of the 5th International Conference on Learning and Intelligent Optimization, Vol. 6683. Springer, 507--523."},{"volume-title":"Systems, Challenges","author":"Hutter Frank","key":"e_1_3_2_1_33_1","unstructured":"Frank Hutter, Lars Kotthoff, and Joaquin Vanschoren. 2019. Automated Machine Learning - Methods, Systems, Challenges. Springer."},{"key":"e_1_3_2_1_34_1","volume-title":"ICML'20: Proc. of the 37th International Conference on Machine Learning","volume":"119","author":"Ishida Takashi","year":"2020","unstructured":"Takashi Ishida, Ikko Yamane, Tomoya Sakai, Gang Niu, and Masashi Sugiyama. 2020. Do We Need Zero Training Loss After Achieving Zero Training Error?. In ICML'20: Proc. of the 37th International Conference on Machine Learning, Vol. 119. PMLR, 4604--4614."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-021-00737-9"},{"key":"e_1_3_2_1_36_1","volume-title":"Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations. In NIPS'16: Proc. of Advances in Neural Information Processing Systems. 992--1000","author":"Kandasamy Kirthevasan","year":"2016","unstructured":"Kirthevasan Kandasamy, Gautam Dasarathy, Junier B. Oliva, Jeff G. Schneider, and Barnab\u00e1s P\u00f3czos. 2016. Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations. In NIPS'16: Proc. of Advances in Neural Information Processing Systems. 992--1000."},{"key":"e_1_3_2_1_37_1","volume-title":"Multi-fidelity Bayesian Optimisation with Continuous Approximations. In ICML'17: Proc. of the 34th International Conference on Machine Learning","volume":"70","author":"Kandasamy Kirthevasan","year":"2017","unstructured":"Kirthevasan Kandasamy, Gautam Dasarathy, Jeff G. Schneider, and Barnab\u00e1s P\u00f3czos. 2017. Multi-fidelity Bayesian Optimisation with Continuous Approximations. In ICML'17: Proc. of the 34th International Conference on Machine Learning, Vol. 70. PMLR, 1799--1808."},{"key":"e_1_3_2_1_38_1","volume-title":"Almost Optimal Exploration in Multi-Armed Bandits. In ICML'13: Proc. of the 30th International Conference on Machine Learning","volume":"28","author":"Karnin Zohar Shay","year":"2013","unstructured":"Zohar Shay Karnin, Tomer Koren, and Oren Somekh. 2013. Almost Optimal Exploration in Multi-Armed Bandits. In ICML'13: Proc. of the 30th International Conference on Machine Learning, Vol. 28. JMLR.org, 1238--1246."},{"volume-title":"The Origins of Order: Self-Organization and Selection in Evolution","author":"Kauffman Stuart A.","key":"e_1_3_2_1_39_1","unstructured":"Stuart A. Kauffman. 1993. The Origins of Order: Self-Organization and Selection in Evolution. Oxford University Press, New York."},{"key":"e_1_3_2_1_40_1","volume-title":"LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In NIPS'17: Proc. of Advances in Neural Information Processing Systems. 3146--3154","author":"Ke Guolin","year":"2017","unstructured":"Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In NIPS'17: Proc. of Advances in Neural Information Processing Systems. 3146--3154."},{"key":"e_1_3_2_1_41_1","volume-title":"Learning to warm-start Bayesian hyperparameter optimization. CoRR","author":"Kim Jungtaek","year":"2017","unstructured":"Jungtaek Kim, Saehoon Kim, and Seungjin Choi. 2017. Learning to warm-start Bayesian hyperparameter optimization. CoRR, Vol. abs\/1710.06219 (2017)."},{"key":"e_1_3_2_1_42_1","first-page":"1106","article-title":"ImageNet Classification with Deep Convolutional Neural Networks. In NIPS'12","volume":"25","author":"Krizhevsky Alex","year":"2012","unstructured":"Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. ImageNet Classification with Deep Convolutional Neural Networks. In NIPS'12: Proc. of Advances in Neural Information Processing Systems 25. 1106--1114.","journal-title":"Proc. of Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_43_1","volume-title":"Multi-Fidelity Methods for Optimization: A Survey. CoRR","author":"Li Ke","year":"2024","unstructured":"Ke Li and Fan Li. 2024. Multi-Fidelity Methods for Optimization: A Survey. CoRR, Vol. abs\/2402.09638 (2024)."},{"key":"e_1_3_2_1_44_1","volume-title":"DaNuoYi: Evolutionary Multi-Task Injection Testing on Web Application Firewalls","author":"Li Ke","year":"2023","unstructured":"Ke Li, Heng Yang, and Willem Visser. 2023. DaNuoYi: Evolutionary Multi-Task Injection Testing on Web Application Firewalls. IEEE Trans. Softw. Eng. (2023). accepted for publication."},{"key":"e_1_3_2_1_45_1","article-title":"Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization","volume":"18","author":"Li Lisha","year":"2017","unstructured":"Lisha Li, Kevin G. Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, and Ameet Talwalkar. 2017. Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization. J. Mach. Learn. Res., Vol. 18 (2017), 185:1--185:52.","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539369"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3395363.3397375"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3104872"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.3390\/a14020040"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2013.04.015"},{"key":"e_1_3_2_1_51_1","volume-title":"UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. CoRR","author":"McInnes Leland","year":"2018","unstructured":"Leland McInnes and John Healy. 2018. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. CoRR, Vol. abs\/1802.03426 (2018)."},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2018.2846636"},{"key":"e_1_3_2_1_53_1","volume-title":"AutoRL Hyperparameter Landscapes. CoRR","author":"Mohan Aditya","year":"2023","unstructured":"Aditya Mohan, Carolin Benjamins, Konrad Wienecke, Alexander Dockhorn, and Marius Lindauer. 2023. AutoRL Hyperparameter Landscapes. CoRR, Vol. abs\/2304.02396 (2023)."},{"key":"e_1_3_2_1_54_1","volume-title":"Mixing patterns in networks. Physical review E","author":"Newman Mark EJ","year":"2003","unstructured":"Mark EJ Newman. 2003. Mixing patterns in networks. Physical review E, Vol. 67, 2 (2003), 026126."},{"key":"e_1_3_2_1_55_1","volume-title":"ICML'97: Proc. of the Fourteenth International Conference on Machine Learning. Morgan Kaufmann, 245--253","author":"Ng Andrew Y.","year":"1997","unstructured":"Andrew Y. Ng. 1997. Preventing \"Overfitting\" of Cross-Validation Data. In ICML'97: Proc. of the Fourteenth International Conference on Machine Learning. Morgan Kaufmann, 245--253."},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/1389095.1389204"},{"key":"e_1_3_2_1_57_1","unstructured":"OpenAI. 2023. GPT-4 Technical Report. CoRR Vol. abs\/2303.08774 (2023)."},{"key":"e_1_3_2_1_58_1","volume-title":"Asymmetric Transitivity Preserving Graph Embedding. In KDD'16: Proc. of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1105--1114","author":"Ou Mingdong","year":"2016","unstructured":"Mingdong Ou, Peng Cui, Jian Pei, Ziwei Zhang, and Wenwu Zhu. 2016. Asymmetric Transitivity Preserving Graph Embedding. In KDD'16: Proc. of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1105--1114."},{"key":"e_1_3_2_1_59_1","volume-title":"Jose Antonio Escudero, and Andreas Wagner","author":"Papkou Andrei","year":"2023","unstructured":"Andrei Papkou, Lucia Garcia-Pastor, Jose Antonio Escudero, and Andreas Wagner. 2023. A rugged yet easily navigable fitness landscape. Science, Vol. 382, 6673 (2023), 3860."},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41576-018-0069-z"},{"key":"e_1_3_2_1_61_1","volume-title":"NIPS'19: Proc. of the 2019 Annual Conference on Neural Information Processing Systems. 12751--12761","author":"Perrone Valerio","year":"2019","unstructured":"Valerio Perrone and Huibin Shen. 2019. Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning. In NIPS'19: Proc. of the 2019 Annual Conference on Neural Information Processing Systems. 12751--12761."},{"key":"e_1_3_2_1_62_1","volume-title":"Fitness Landscape Analysis of Automated Machine Learning Search Spaces. In EvoCOP'20: Proc. of the 20th European Conference Evolutionary Computation in Combinatorial Optimization","volume":"12102","author":"Pimenta Cristiano Guimar","unstructured":"Cristiano Guimar aes Pimenta, Alex Guimar aes Cardoso de S\u00e1, Gabriela Ochoa, and Gisele L. Pappa. 2020. Fitness Landscape Analysis of Automated Machine Learning Search Spaces. In EvoCOP'20: Proc. of the 20th European Conference Evolutionary Computation in Combinatorial Optimization, Vol. 12102. Springer, 114--130."},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2011.2163638"},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1145\/3558774"},{"key":"e_1_3_2_1_65_1","volume-title":"ICLR'10: Proc. of the 10th International Conference on Learning Representations. OpenReview.net.","author":"Rakotoarison Herilalaina","year":"2022","unstructured":"Herilalaina Rakotoarison, Louisot Milijaona, Andry Rasoanaivo, Mich\u00e8le Sebag, and Marc Schoenauer. 2022. Learning meta-features for AutoML. In ICLR'10: Proc. of the 10th International Conference on Learning Representations. OpenReview.net."},{"key":"e_1_3_2_1_66_1","volume-title":"ICML'19: Proc. of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research","volume":"5400","author":"Recht Benjamin","year":"2019","unstructured":"Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar. 2019. Do ImageNet Classifiers Generalize to ImageNet?. In ICML'19: Proc. of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 97). PMLR, 5389--5400."},{"key":"e_1_3_2_1_67_1","volume-title":"PNeurIPS'19: Annual Conference on Neural Information Processing Systems","author":"Roelofs Rebecca","year":"2019","unstructured":"Rebecca Roelofs, Vaishaal Shankar, Benjamin Recht, Sara Fridovich-Keil, Moritz Hardt, John Miller, and Ludwig Schmidt. 2019. A Meta-Analysis of Overfitting in Machine Learning. In PNeurIPS'19: Annual Conference on Neural Information Processing Systems 2019. 9175--9185."},{"key":"e_1_3_2_1_68_1","volume-title":"Exploring protein fitness landscapes by directed evolution. Nature reviews Molecular cell biology","author":"Romero Philip A","year":"2009","unstructured":"Philip A Romero and Frances H Arnold. 2009. Exploring protein fitness landscapes by directed evolution. Nature reviews Molecular cell biology, Vol. 10, 12 (2009), 866--876."},{"key":"e_1_3_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.1145\/3470918"},{"key":"e_1_3_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-14714-2_40"},{"key":"e_1_3_2_1_71_1","volume-title":"ACM Comput. Surv.","volume":"41","author":"Smith-Miles Kate","year":"2008","unstructured":"Kate Smith-Miles. 2008. Cross-disciplinary perspectives on meta-learning for algorithm selection. ACM Comput. Surv., Vol. 41, 1 (2008), 6:1--6:25."},{"key":"e_1_3_2_1_72_1","volume-title":"Practical Bayesian Optimization of Machine Learning Algorithms. In NIPS'12: Proc of the 26th Annual Conference on Neural Information Processing Systems","author":"Snoek Jasper","year":"2012","unstructured":"Jasper Snoek, Hugo Larochelle, and Ryan P. Adams. 2012. Practical Bayesian Optimization of Machine Learning Algorithms. In NIPS'12: Proc of the 26th Annual Conference on Neural Information Processing Systems 2012. 2960--2968."},{"key":"e_1_3_2_1_73_1","doi-asserted-by":"crossref","unstructured":"Charles Spearman. 1961. The proof and measurement of association between two things. (1961).","DOI":"10.1037\/11491-005"},{"volume-title":"Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design. In ICML'10: Proc. of the 27th International Conference on Machine Learning. Omnipress, 1015--1022","author":"Srinivas Niranjan","key":"e_1_3_2_1_74_1","unstructured":"Niranjan Srinivas, Andreas Krause, Sham M. Kakade, and Matthias W. Seeger. 2010. Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design. In ICML'10: Proc. of the 27th International Conference on Machine Learning. Omnipress, 1015--1022."},{"key":"e_1_3_2_1_75_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.267.5206.1935"},{"key":"e_1_3_2_1_76_1","volume-title":"NIPS","volume":"26","author":"Swersky Kevin","year":"2013","unstructured":"Kevin Swersky, Jasper Snoek, and Ryan P Adams. 2013. Multi-task Bayesian optimization. NIPS, Vol. 26 (2013)."},{"key":"e_1_3_2_1_77_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2013.2281502"},{"volume-title":"GECCO'22: Genetic and Evolutionary Computation Conference. ACM, 449--457","author":"Teixeira Matheus C\u00e2ndido","key":"e_1_3_2_1_78_1","unstructured":"Matheus C\u00e2ndido Teixeira and Gisele L. Pappa. 2022. Understanding AutoML search spaces with local optima networks. In GECCO'22: Genetic and Evolutionary Computation Conference. ACM, 449--457."},{"key":"e_1_3_2_1_79_1","volume-title":"Meta Kaggle: Competition Shake-Up. https:\/\/www.kaggle.com\/jtrotman\/meta-kaggle-competition-shake-up","author":"Trotman James","year":"2019","unstructured":"James Trotman. 2019. Meta Kaggle: Competition Shake-Up. https:\/\/www.kaggle.com\/jtrotman\/meta-kaggle-competition-shake-up"},{"key":"e_1_3_2_1_80_1","volume-title":"NeurIPS'22: Proc. of Advances in Neural Information Processing Systems.","author":"Tu Renbo","year":"2022","unstructured":"Renbo Tu, Nicholas Roberts, Mikhail Khodak, Junhong Shen, Frederic Sala, and Ameet Talwalkar. 2022. NAS-Bench-360: Benchmarking Neural Architecture Search on Diverse Tasks. In NeurIPS'22: Proc. of Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_1_81_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-022-04506-6"},{"key":"e_1_3_2_1_82_1","volume-title":"van Rijn and Frank Hutter","author":"Jan","year":"2017","unstructured":"Jan N. van Rijn and Frank Hutter. 2017. An Empirical Study of Hyperparameter Importance Across Datasets. In AutoML@PKDD\/ECML, Vol. 1998. CEUR-WS.org, 91--98."},{"key":"e_1_3_2_1_83_1","volume-title":"A Survey. CoRR","author":"Vanschoren Joaquin","year":"2018","unstructured":"Joaquin Vanschoren. 2018. Meta-Learning: A Survey. CoRR, Vol. abs\/1810.03548 (2018). showeprint[arXiv]1810.03548"},{"key":"e_1_3_2_1_84_1","first-page":"5998","article-title":"Attention is All you Need. In NIPS'17","volume":"30","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. In NIPS'17: Proc. of Advances in Neural Information Processing Systems 30. 5998--6008.","journal-title":"Proc. of Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_85_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2010.2046175"},{"key":"e_1_3_2_1_86_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2022.3157143"},{"key":"e_1_3_2_1_87_1","article-title":"Energy Landscape Theory, Funnels, Specificity, and Optimal Criterion of Biomolecular","volume":"90","author":"Wang Jin","year":"2003","unstructured":"Jin Wang and Gennady M. Verkhivker. 2003. Energy Landscape Theory, Funnels, Specificity, and Optimal Criterion of Biomolecular Binding. Phys. Rev. Lett., Vol. 90 (May 2003), 188101. Issue 18.","journal-title":"Binding. Phys. Rev. Lett."},{"key":"e_1_3_2_1_88_1","volume-title":"NeurIPS'20: Proc. of Advances in Neural Information Processing Systems.","author":"Wang Linnan","year":"2020","unstructured":"Linnan Wang, Rodrigo Fonseca, and Yuandong Tian. 2020. Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search. In NeurIPS'20: Proc. of Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_1_89_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2023\/487"},{"key":"e_1_3_2_1_90_1","volume-title":"Correlated and uncorrelated fitness landscapes and how to tell the difference. Biological cybernetics","author":"Weinberger Edward","year":"1990","unstructured":"Edward Weinberger. 1990. Correlated and uncorrelated fitness landscapes and how to tell the difference. Biological cybernetics, Vol. 63, 5 (1990), 325--336."},{"key":"e_1_3_2_1_91_1","volume-title":"CVPR'23: Proc. of the 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 12291--12301","author":"Williams Phoenix Neale","year":"2023","unstructured":"Phoenix Neale Williams and Ke Li. 2023. Black-Box Sparse Adversarial Attack via Multi-Objective Optimisation. In CVPR'23: Proc. of the 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 12291--12301."},{"key":"e_1_3_2_1_92_1","volume-title":"Proc. of the 2015 MetaSel workshop at PKDD-ECML","volume":"1455","author":"Wistuba Martin","year":"2015","unstructured":"Martin Wistuba, Nicolas Schilling, and Lars Schmidt-Thieme. 2015a. Learning Data Set Similarities for Hyperparameter Optimization Initializations. In Proc. of the 2015 MetaSel workshop at PKDD-ECML, Vol. 1455. CEUR-WS.org, 15--26."},{"key":"e_1_3_2_1_93_1","doi-asserted-by":"publisher","DOI":"10.1109\/DSAA.2015.7344817"},{"key":"e_1_3_2_1_94_1","volume-title":"Proc. of the 11th International Congress of Genetics","volume":"1","author":"Wright Sewall","year":"1932","unstructured":"Sewall Wright. 1932. The roles of mutations, inbreeding, crossbreeding and selection in evolution. In Proc. of the 11th International Congress of Genetics, Vol. 1. 356--366."},{"key":"e_1_3_2_1_95_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.07.061"},{"key":"e_1_3_2_1_96_1","volume-title":"ICML'19: Proc. of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research","volume":"7114","author":"Ying Chris","year":"2019","unstructured":"Chris Ying, Aaron Klein, Eric Christiansen, Esteban Real, Kevin Murphy, and Frank Hutter. 2019. NAS-Bench-101: Towards Reproducible Neural Architecture Search. In ICML'19: Proc. of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 97). PMLR, 7105--7114."},{"key":"e_1_3_2_1_97_1","doi-asserted-by":"publisher","DOI":"10.1145\/3660808"},{"key":"e_1_3_2_1_98_1","doi-asserted-by":"publisher","DOI":"10.1145\/3520304.3528981"},{"key":"e_1_3_2_1_99_1","volume-title":"Attention-Based Genetic Algorithm for Adversarial Attack in Natural Language Processing. In PPSN'22L Proc. of the 17th International Conference on Parallel Problem Solving from Nature","volume":"13398","author":"Zhou Shasha","year":"2022","unstructured":"Shasha Zhou, Ke Li, and Geyong Min. 2022b. Attention-Based Genetic Algorithm for Adversarial Attack in Natural Language Processing. In PPSN'22L Proc. of the 17th International Conference on Parallel Problem Solving from Nature, Vol. 13398. Springer, 341--355."}],"event":{"name":"KDD '25: The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"],"location":"Toronto ON Canada","acronym":"KDD '25"},"container-title":["Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3690624.3709229","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3690624.3709229","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,16]],"date-time":"2025-08-16T15:45:01Z","timestamp":1755359101000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3690624.3709229"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,20]]},"references-count":99,"alternative-id":["10.1145\/3690624.3709229","10.1145\/3690624"],"URL":"https:\/\/doi.org\/10.1145\/3690624.3709229","relation":{},"subject":[],"published":{"date-parts":[[2025,7,20]]},"assertion":[{"value":"2025-07-20","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}