{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T18:53:08Z","timestamp":1763664788013,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":56,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,4,5]],"date-time":"2022-04-05T00:00:00Z","timestamp":1649116800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,4,5]]},"DOI":"10.1145\/3517207.3526977","type":"proceedings-article","created":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T22:09:26Z","timestamp":1648591766000},"page":"45-53","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["BoGraph"],"prefix":"10.1145","author":[{"given":"Sami","family":"Alabed","sequence":"first","affiliation":[{"name":"University of Cambridge, UK"}]},{"given":"Eiko","family":"Yoneki","sequence":"additional","affiliation":[{"name":"University of Cambridge, UK"}]}],"member":"320","published-online":{"date-parts":[[2022,4,5]]},"reference":[{"key":"e_1_3_2_1_1_1","first-page":"3155","volume-title":"Javier Gonz\u00e1lez. Causal Bayesian Optimization. In International Conference on Artificial Intelligence and Statistics","author":"Aglietti Virginia","year":"2020","unstructured":"Virginia Aglietti , Xiaoyu Lu , Andrei Paleyes , and Javier Gonz\u00e1lez. Causal Bayesian Optimization. In International Conference on Artificial Intelligence and Statistics , pages 3155 -- 3164 . PMLR, 2020 . Virginia Aglietti, Xiaoyu Lu, Andrei Paleyes, and Javier Gonz\u00e1lez. Causal Bayesian Optimization. In International Conference on Artificial Intelligence and Statistics, pages 3155--3164. PMLR, 2020."},{"key":"e_1_3_2_1_2_1","volume-title":"Jianshu Chen, Shivaram Venkataraman, Minlan Yu, and Ming Zhang. CherryPick: Adaptively Unearthing the Best Cloud Configurations for Big Data Analytics","author":"Alipourfard Omid","year":"2017","unstructured":"Omid Alipourfard , Hongqiang Harry Liu , Jianshu Chen, Shivaram Venkataraman, Minlan Yu, and Ming Zhang. CherryPick: Adaptively Unearthing the Best Cloud Configurations for Big Data Analytics , 2017 . Omid Alipourfard, Hongqiang Harry Liu, Jianshu Chen, Shivaram Venkataraman, Minlan Yu, and Ming Zhang. CherryPick: Adaptively Unearthing the Best Cloud Configurations for Big Data Analytics, 2017."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1836543.1836554"},{"key":"e_1_3_2_1_4_1","first-page":"303","volume-title":"Saman Amarasinghe. OpenTuner. In Proceedings of the 23rd International Conference on Parallel Architectures and Compilation - PACT '14","author":"Ansel Jason","year":"2014","unstructured":"Jason Ansel , Shoaib Kamil , Kalyan Veeramachaneni , Jonathan Ragan-Kelley , Jeffrey Bosboom , Una-May O'Reilly , and Saman Amarasinghe. OpenTuner. In Proceedings of the 23rd International Conference on Parallel Architectures and Compilation - PACT '14 , pages 303 -- 316 , 2014 . Jason Ansel, Shoaib Kamil, Kalyan Veeramachaneni, Jonathan Ragan-Kelley, Jeffrey Bosboom, Una-May O'Reilly, and Saman Amarasinghe. OpenTuner. In Proceedings of the 23rd International Conference on Parallel Architectures and Compilation - PACT '14, pages 303--316, 2014."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1816038.1815967"},{"key":"e_1_3_2_1_6_1","volume-title":"December","author":"Balandat Maximilian","year":"2020","unstructured":"Maximilian Balandat , Brian Karrer , Daniel R. Jiang , Samuel Daulton , Benjamin Letham , Andrew Gordon Wilson, and Eytan Bakshy. BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization. arXiv:1910.06403 [cs, math, stat] , December 2020 . Maximilian Balandat, Brian Karrer, Daniel R. Jiang, Samuel Daulton, Benjamin Letham, Andrew Gordon Wilson, and Eytan Bakshy. BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization. arXiv:1910.06403 [cs, math, stat], December 2020."},{"key":"e_1_3_2_1_7_1","volume-title":"Zain Patel, and Wesley Leong. CausalNex","author":"Beaumont Paul","year":"2021","unstructured":"Paul Beaumont , Ben Horsburgh , Philip Pilgerstorfer , Angel Droth , Richard Oentaryo , Steven Ler , Hiep Nguyen , Gabriel Azevedo Ferreira , Zain Patel, and Wesley Leong. CausalNex , 2021 . Paul Beaumont, Ben Horsburgh, Philip Pilgerstorfer, Angel Droth, Richard Oentaryo, Steven Ler, Hiep Nguyen, Gabriel Azevedo Ferreira, Zain Patel, and Wesley Leong. CausalNex, 2021."},{"key":"e_1_3_2_1_8_1","volume-title":"Random search for hyper-parameter optimization. Journal of machine learning research, 13(Feb):281--305","author":"Bergstra James","year":"2012","unstructured":"James Bergstra and Yoshua Bengio . Random search for hyper-parameter optimization. Journal of machine learning research, 13(Feb):281--305 , 2012 . James Bergstra and Yoshua Bengio. Random search for hyper-parameter optimization. Journal of machine learning research, 13(Feb):281--305, 2012."},{"key":"e_1_3_2_1_9_1","volume-title":"Site Reliability Engineering: How Google Runs Production Systems.\" O'Reilly Media","author":"Beyer Betsy","year":"2016","unstructured":"Betsy Beyer , Chris Jones , Jennifer Petoff , and Niall Richard Murphy . Site Reliability Engineering: How Google Runs Production Systems.\" O'Reilly Media , Inc .\", 2016 . Betsy Beyer, Chris Jones, Jennifer Petoff, and Niall Richard Murphy. Site Reliability Engineering: How Google Runs Production Systems.\" O'Reilly Media, Inc.\", 2016."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/LCA.2019.2910521"},{"key":"e_1_3_2_1_11_1","volume-title":"Pyro: Deep Universal Probabilistic Programming","author":"Bingham Eli","year":"2018","unstructured":"Eli Bingham , Jonathan P. Chen , Martin Jankowiak , Fritz Obermeyer , Neeraj Pradhan , Theofanis Karaletsos , Rohit Singh , Paul Szerlip , Paul Horsfall , and Noah D. Goodman . Pyro: Deep Universal Probabilistic Programming . October 2018 . Eli Bingham, Jonathan P. Chen, Martin Jankowiak, Fritz Obermeyer, Neeraj Pradhan, Theofanis Karaletsos, Rohit Singh, Paul Szerlip, Paul Horsfall, and Noah D. Goodman. Pyro: Deep Universal Probabilistic Programming. October 2018."},{"key":"e_1_3_2_1_12_1","volume-title":"The gem5 simulator. ACM SIGARCH computer architecture news, 39(2):1--7","author":"Binkert Nathan","year":"2011","unstructured":"Nathan Binkert , Bradford Beckmann , Gabriel Black , Steven K Reinhardt , Ali Saidi , Arkaprava Basu , Joel Hestness , Derek R Hower , Tushar Krishna , and Somayeh Sardashti . The gem5 simulator. ACM SIGARCH computer architecture news, 39(2):1--7 , 2011 . Nathan Binkert, Bradford Beckmann, Gabriel Black, Steven K Reinhardt, Ali Saidi, Arkaprava Basu, Joel Hestness, Derek R Hower, Tushar Krishna, and Somayeh Sardashti. The gem5 simulator. ACM SIGARCH computer architecture news, 39(2):1--7, 2011."},{"key":"e_1_3_2_1_13_1","volume-title":"Up & Running: Infrastructure and Application Performance Monitoring. \"O'Reilly Media","author":"Brazil Brian","year":"2018","unstructured":"Brian Brazil . Prometheus : Up & Running: Infrastructure and Application Performance Monitoring. \"O'Reilly Media , Inc .\", 2018 . Brian Brazil. Prometheus: Up & Running: Infrastructure and Application Performance Monitoring. \"O'Reilly Media, Inc.\", 2018."},{"key":"e_1_3_2_1_14_1","volume-title":"Learning to optimize tensor programs. arXiv preprint arXiv:1805.08166","author":"Chen Tianqi","year":"2018","unstructured":"Tianqi Chen , Lianmin Zheng , Eddie Yan , Ziheng Jiang , Thierry Moreau , Luis Ceze , Carlos Guestrin , and Arvind Krishnamurthy . Learning to optimize tensor programs. arXiv preprint arXiv:1805.08166 , 2018 . Tianqi Chen, Lianmin Zheng, Eddie Yan, Ziheng Jiang, Thierry Moreau, Luis Ceze, Carlos Guestrin, and Arvind Krishnamurthy. Learning to optimize tensor programs. arXiv preprint arXiv:1805.08166, 2018."},{"key":"e_1_3_2_1_15_1","volume-title":"May","author":"Chen Yu-Hsin","year":"2019","unstructured":"Yu-Hsin Chen , Tien-Ju Yang , Joel Emer , and Vivienne Sze . Eyeriss v2: A Flexible Accelerator for Emerging Deep Neural Networks on Mobile Devices. arXiv:1807.07928 [cs] , May 2019 . Yu-Hsin Chen, Tien-Ju Yang, Joel Emer, and Vivienne Sze. Eyeriss v2: A Flexible Accelerator for Emerging Deep Neural Networks on Mobile Devices. arXiv:1807.07928 [cs], May 2019."},{"key":"e_1_3_2_1_16_1","first-page":"479","volume-title":"Proceedings of the 26th International Conference on World Wide Web - WWW '17","author":"Dalibard Valentin","year":"2017","unstructured":"Valentin Dalibard , Michael Schaarschmidt , and Eiko Yoneki . BOAT : Building auto-tuners with structured Bayesian optimization . In Proceedings of the 26th International Conference on World Wide Web - WWW '17 , pages 479 -- 488 , New York, New York, USA , 2017 . ACM Press. Valentin Dalibard, Michael Schaarschmidt, and Eiko Yoneki. BOAT: Building auto-tuners with structured Bayesian optimization. In Proceedings of the 26th International Conference on World Wide Web - WWW '17, pages 479--488, New York, New York, USA, 2017. ACM Press."},{"key":"e_1_3_2_1_17_1","first-page":"207","volume-title":"Artificial Intelligence and Statistics","author":"Damianou Andreas","year":"2013","unstructured":"Andreas Damianou and Neil D Lawrence . Deep gaussian processes . In Artificial Intelligence and Statistics , pages 207 -- 215 . PMLR, 2013 . Andreas Damianou and Neil D Lawrence. Deep gaussian processes. In Artificial Intelligence and Statistics, pages 207--215. PMLR, 2013."},{"key":"e_1_3_2_1_18_1","volume-title":"Differentiable expected hypervolume improvement for parallel multi-objective Bayesian optimization. arXiv preprint arXiv:2006.05078","author":"Daulton Samuel","year":"2020","unstructured":"Samuel Daulton , Maximilian Balandat , and Eytan Bakshy . Differentiable expected hypervolume improvement for parallel multi-objective Bayesian optimization. arXiv preprint arXiv:2006.05078 , 2020 . Samuel Daulton, Maximilian Balandat, and Eytan Bakshy. Differentiable expected hypervolume improvement for parallel multi-objective Bayesian optimization. arXiv preprint arXiv:2006.05078, 2020."},{"key":"e_1_3_2_1_19_1","volume-title":"BOHB: Robust and efficient hyperparameter optimization at scale. arXiv preprint arXiv:1807.01774","author":"Falkner Stefan","year":"2018","unstructured":"Stefan Falkner , Aaron Klein , and Frank Hutter . BOHB: Robust and efficient hyperparameter optimization at scale. arXiv preprint arXiv:1807.01774 , 2018 . Stefan Falkner, Aaron Klein, and Frank Hutter. BOHB: Robust and efficient hyperparameter optimization at scale. arXiv preprint arXiv:1807.01774, 2018."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.14778\/3137765.3137786"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/2591062.2591175"},{"key":"e_1_3_2_1_22_1","volume-title":"January","author":"Gardner Jacob R.","year":"2019","unstructured":"Jacob R. Gardner , Geoff Pleiss , David Bindel , Kilian Q. Weinberger , and Andrew Gordon Wilson . GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration. arXiv:1809.11165 [cs, stat] , January 2019 . Jacob R. Gardner, Geoff Pleiss, David Bindel, Kilian Q. Weinberger, and Andrew Gordon Wilson. GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration. arXiv:1809.11165 [cs, stat], January 2019."},{"key":"e_1_3_2_1_23_1","volume-title":"Los Alamos National Lab.(LANL)","author":"Hagberg Aric","year":"2008","unstructured":"Aric Hagberg , Pieter Swart , and Daniel S Chult . Exploring network structure, dynamics, and function using NetworkX. Technical report , Los Alamos National Lab.(LANL) , Los Alamos, NM ( United States) , 2008 . Aric Hagberg, Pieter Swart, and Daniel S Chult. Exploring network structure, dynamics, and function using NetworkX. Technical report, Los Alamos National Lab.(LANL), Los Alamos, NM (United States), 2008."},{"volume-title":"October","year":"2016","key":"e_1_3_2_1_24_1","unstructured":"gem5-aladdin harvard-acc. Gem5-Aladdin SoC Simulator. Harvard Architecture, Circuits, and Compilers , October 2016 . gem5-aladdin harvard-acc. Gem5-Aladdin SoC Simulator. Harvard Architecture, Circuits, and Compilers, October 2016."},{"volume-title":"October","year":"2021","key":"e_1_3_2_1_25_1","unstructured":"gem5-aladdin-param harvard-acc. Gem5-Aladdin SoC Simulator. Harvard Architecture, Circuits, and Compilers , October 2021 . gem5-aladdin-param harvard-acc. Gem5-Aladdin SoC Simulator. Harvard Architecture, Circuits, and Compilers, October 2021."},{"key":"e_1_3_2_1_26_1","volume-title":"Deep reinforcement learning that matters. arXiv preprint arXiv:1709.06560","author":"Henderson Peter","year":"2017","unstructured":"Peter Henderson , Riashat Islam , Philip Bachman , Joelle Pineau , Doina Precup , and David Meger . Deep reinforcement learning that matters. arXiv preprint arXiv:1709.06560 , 2017 . Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger. Deep reinforcement learning that matters. arXiv preprint arXiv:1709.06560, 2017."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/2464576.2501592"},{"key":"e_1_3_2_1_28_1","volume-title":"Population based training of neural networks. arXiv preprint arXiv:1711.09846","author":"Jaderberg Max","year":"2017","unstructured":"Max Jaderberg , Valentin Dalibard , Simon Osindero , Wojciech M. Czarnecki , Jeff Donahue , Ali Razavi , Oriol Vinyals , Tim Green , Iain Dunning , and Karen Simonyan . Population based training of neural networks. arXiv preprint arXiv:1711.09846 , 2017 . Max Jaderberg, Valentin Dalibard, Simon Osindero, Wojciech M. Czarnecki, Jeff Donahue, Ali Razavi, Oriol Vinyals, Tim Green, Iain Dunning, and Karen Simonyan. Population based training of neural networks. arXiv preprint arXiv:1711.09846, 2017."},{"key":"e_1_3_2_1_29_1","volume-title":"Population Based Training of Neural Networks","author":"Jaderberg Max","year":"2017","unstructured":"Max Jaderberg , Valentin Dalibard , Simon Osindero , Wojciech M. Czarnecki , Jeff Donahue , Ali Razavi , Oriol Vinyals , Tim Green , Iain Dunning , Karen Simonyan , Chrisantha Fernando , and Koray Kavukcuoglu . Population Based Training of Neural Networks . November 2017 . Max Jaderberg, Valentin Dalibard, Simon Osindero, Wojciech M. Czarnecki, Jeff Donahue, Ali Razavi, Oriol Vinyals, Tim Green, Iain Dunning, Karen Simonyan, Chrisantha Fernando, and Koray Kavukcuoglu. Population Based Training of Neural Networks. November 2017."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.4324\/9781315788135"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.5555\/1795555"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.5555\/3122009.3242042"},{"issue":"3","key":"e_1_3_2_1_33_1","first-page":"18","article-title":"Classification and regression by randomForest","volume":"2","author":"Liaw Andy","year":"2002","unstructured":"Andy Liaw and Matthew Wiener . Classification and regression by randomForest . R news , 2 ( 3 ): 18 -- 22 , 2002 . Andy Liaw and Matthew Wiener. Classification and regression by randomForest. R news, 2(3):18--22, 2002.","journal-title":"R news"},{"key":"e_1_3_2_1_34_1","volume-title":"April","author":"Liu Haitao","year":"2019","unstructured":"Haitao Liu , Yew-Soon Ong , Xiaobo Shen , and Jianfei Cai . When Gaussian Process Meets Big Data: A Review of Scalable GPs. arXiv:1807.01065 [cs, stat] , April 2019 . Haitao Liu, Yew-Soon Ong, Xiaobo Shen, and Jianfei Cai. When Gaussian Process Meets Big Data: A Review of Scalable GPs. arXiv:1807.01065 [cs, stat], April 2019."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/0168-9002(94)00931-7"},{"key":"e_1_3_2_1_36_1","volume-title":"June","author":"Mirhoseini Azalia","year":"2017","unstructured":"Azalia Mirhoseini , Hieu Pham , Quoc V. Le , Benoit Steiner , Rasmus Larsen , Yuefeng Zhou , Naveen Kumar , Mohammad Norouzi , Samy Bengio , and Jeff Dean . Device Placement Optimization with Reinforcement Learning. CoRR, abs\/1706.0 , June 2017 . Azalia Mirhoseini, Hieu Pham, Quoc V. Le, Benoit Steiner, Rasmus Larsen, Yuefeng Zhou, Naveen Kumar, Mohammad Norouzi, Samy Bengio, and Jeff Dean. Device Placement Optimization with Reinforcement Learning. CoRR, abs\/1706.0, June 2017."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447786.3456245"},{"key":"e_1_3_2_1_38_1","volume-title":"PostgreSQL: Introduction and Concepts","author":"Momjian Bruce","year":"2001","unstructured":"Bruce Momjian . PostgreSQL: Introduction and Concepts , volume 192 . Addison-Wesley New York , 2001 . Bruce Momjian. PostgreSQL: Introduction and Concepts, volume 192. Addison-Wesley New York, 2001."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/MASCOTS.2019.00045"},{"key":"e_1_3_2_1_40_1","unstructured":"OpenSource. Microsoft\/nni. Microsoft December 2020.  OpenSource. Microsoft\/nni. Microsoft December 2020."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/MDAT.2016.2626445"},{"key":"e_1_3_2_1_42_1","volume-title":"Gaussian Processes for Machine Learning. Adaptive Computation and Machine Learning","author":"Rasmussen Carl Edward","year":"2008","unstructured":"Carl Edward Rasmussen and Christopher K. I. Williams . Gaussian Processes for Machine Learning. Adaptive Computation and Machine Learning . MIT Press , Cambridge, Mass ., 3. print edition, 2008 . Carl Edward Rasmussen and Christopher K. I. Williams. Gaussian Processes for Machine Learning. Adaptive Computation and Machine Learning. MIT Press, Cambridge, Mass., 3. print edition, 2008."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/IISWC.2014.6983050"},{"key":"e_1_3_2_1_44_1","first-page":"1","volume-title":"Progress in Artificial Intelligence","author":"Scanagatta Mauro","year":"2019","unstructured":"Mauro Scanagatta , Antonio Salmer\u00f3n , and Fabio Stella . A survey on Bayesian network structure learning from data . Progress in Artificial Intelligence , pages 1 -- 15 , 2019 . Mauro Scanagatta, Antonio Salmer\u00f3n, and Fabio Stella. A survey on Bayesian network structure learning from data. Progress in Artificial Intelligence, pages 1--15, 2019."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2015.2494218"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISCA.2014.6853196"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/MICRO.2016.7783751"},{"key":"e_1_3_2_1_48_1","first-page":"2951","volume-title":"Advances in Neural Information Processing Systems","author":"Snoek Jasper","year":"2012","unstructured":"Jasper Snoek , Hugo Larochelle , and Ryan P Adams . Practical bayesian optimization of machine learning algorithms . In Advances in Neural Information Processing Systems , pages 2951 -- 2959 , 2012 . Jasper Snoek, Hugo Larochelle, and Ryan P Adams. Practical bayesian optimization of machine learning algorithms. In Advances in Neural Information Processing Systems, pages 2951--2959, 2012."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.5555\/551283"},{"key":"e_1_3_2_1_50_1","first-page":"1009","volume-title":"Bohan Zhang. Automatic Database Management System Tuning Through Large-scale Machine Learning. In Proceedings of the 2017 ACM International Conference on Management of Data - SIGMOID '17","author":"Aken Dana Van","year":"2017","unstructured":"Dana Van Aken , Andrew Pavlo , Geoffrey J. Gordon , and Bohan Zhang. Automatic Database Management System Tuning Through Large-scale Machine Learning. In Proceedings of the 2017 ACM International Conference on Management of Data - SIGMOID '17 , pages 1009 -- 1024 , New York, New York, USA , 2017 . ACM Press. Dana Van Aken, Andrew Pavlo, Geoffrey J. Gordon, and Bohan Zhang. Automatic Database Management System Tuning Through Large-scale Machine Learning. In Proceedings of the 2017 ACM International Conference on Management of Data - SIGMOID '17, pages 1009--1024, New York, New York, USA, 2017. ACM Press."},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.14778\/3450980.3450992"},{"key":"e_1_3_2_1_52_1","first-page":"363","volume-title":"13th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI} 16)","author":"Venkataraman Shivaram","year":"2016","unstructured":"Shivaram Venkataraman , Zongheng Yang , Michael Franklin , Benjamin Recht , and Ion Stoica . Ernest: Efficient performance prediction for large-scale advanced analytics . In 13th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI} 16) , pages 363 -- 378 , 2016 . Shivaram Venkataraman, Zongheng Yang, Michael Franklin, Benjamin Recht, and Ion Stoica. Ernest: Efficient performance prediction for large-scale advanced analytics. In 13th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI} 16), pages 363--378, 2016."},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.5555\/3013558.3013569"},{"key":"e_1_3_2_1_54_1","volume-title":"Principal component analysis. Chemometrics and intelligent laboratory systems, 2(1--3):37--52","author":"Wold Svante","year":"1987","unstructured":"Svante Wold , Kim Esbensen , and Paul Geladi . Principal component analysis. Chemometrics and intelligent laboratory systems, 2(1--3):37--52 , 1987 . Svante Wold, Kim Esbensen, and Paul Geladi. Principal component analysis. Chemometrics and intelligent laboratory systems, 2(1--3):37--52, 1987."},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2012.6227202"},{"key":"e_1_3_2_1_56_1","volume-title":"Dags with no tears: Continuous optimization for structure learning. arXiv preprint arXiv:1803.01422","author":"Zheng Xun","year":"2018","unstructured":"Xun Zheng , Bryon Aragam , Pradeep Ravikumar , and Eric P Xing . Dags with no tears: Continuous optimization for structure learning. arXiv preprint arXiv:1803.01422 , 2018 . Xun Zheng, Bryon Aragam, Pradeep Ravikumar, and Eric P Xing. Dags with no tears: Continuous optimization for structure learning. arXiv preprint arXiv:1803.01422, 2018."}],"event":{"name":"EuroSys '22: Seventeenth European Conference on Computer Systems","sponsor":["SIGOPS ACM Special Interest Group on Operating Systems"],"location":"Rennes France","acronym":"EuroSys '22"},"container-title":["Proceedings of the 2nd European Workshop on Machine Learning and Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3517207.3526977","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3517207.3526977","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:31:29Z","timestamp":1750188689000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3517207.3526977"}},"subtitle":["structured bayesian optimization from logs for expensive systems with many parameters"],"short-title":[],"issued":{"date-parts":[[2022,4,5]]},"references-count":56,"alternative-id":["10.1145\/3517207.3526977","10.1145\/3517207"],"URL":"https:\/\/doi.org\/10.1145\/3517207.3526977","relation":{},"subject":[],"published":{"date-parts":[[2022,4,5]]},"assertion":[{"value":"2022-04-05","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}