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To produce a truly usable estimator, we develop a Monte Carlo integration scheme on top of autoregressive models that can efficiently handle range queries with dozens of dimensions or more.<\/jats:p><jats:p>Like classical synopses, our estimator summarizes the data without supervision. Unlike previous solutions, we approximate the joint data distribution without any independence assumptions. Evaluated on real-world datasets and compared against real systems and dominant families of techniques, our estimator achieves single-digit multiplicative error at tail, an up to 90x accuracy improvement over the second best method, and is space- and runtime-efficient.<\/jats:p>","DOI":"10.14778\/3368289.3368294","type":"journal-article","created":{"date-parts":[[2020,9,11]],"date-time":"2020-09-11T03:17:35Z","timestamp":1599794255000},"page":"279-292","source":"Crossref","is-referenced-by-count":166,"title":["Deep unsupervised cardinality estimation"],"prefix":"10.14778","volume":"13","author":[{"given":"Zongheng","family":"Yang","sequence":"first","affiliation":[{"name":"UC Berkeley"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric","family":"Liang","sequence":"additional","affiliation":[{"name":"UC Berkeley"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amog","family":"Kamsetty","sequence":"additional","affiliation":[{"name":"UC Berkeley"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenggang","family":"Wu","sequence":"additional","affiliation":[{"name":"UC Berkeley"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Duan","sequence":"additional","affiliation":[{"name":"covariant.ai"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xi","family":"Chen","sequence":"additional","affiliation":[{"name":"UC Berkeley and covariant.ai"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pieter","family":"Abbeel","sequence":"additional","affiliation":[{"name":"UC Berkeley and covariant.ai"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joseph M.","family":"Hellerstein","sequence":"additional","affiliation":[{"name":"UC Berkeley"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sanjay","family":"Krishnan","sequence":"additional","affiliation":[{"name":"University of Chicago"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ion","family":"Stoica","sequence":"additional","affiliation":[{"name":"UC Berkeley"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2019,11]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/375663.375686"},{"key":"e_1_2_1_2_1","first-page":"263","volume-title":"ACM SIGMOD Record","author":"Chaudhuri S.","year":"1999","unstructured":"S. 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In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171--4186, Minneapolis, Minnesota, June 2019. Association for Computational Linguistics."},{"key":"e_1_2_1_9_1","first-page":"1735","volume-title":"Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research","author":"Durkan C.","year":"2019","unstructured":"C. Durkan and C. Nash . Autoregressive energy machines . In Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research , pages 1735 -- 1744 , Long Beach, California, USA, 09- -15 Jun 2019 . PMLR. C. Durkan and C. Nash. Autoregressive energy machines. In Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, pages 1735--1744, Long Beach, California, USA, 09--15 Jun 2019. PMLR."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.14778\/3329772.3329780"},{"key":"e_1_2_1_11_1","series-title":"Springer series in statistics New York","volume-title":"The elements of statistical learning","author":"Friedman J.","year":"2001","unstructured":"J. Friedman , T. Hastie , and R. Tibshirani . The elements of statistical learning . Springer series in statistics New York , 2001 . J. Friedman, T. Hastie, and R. Tibshirani. The elements of statistical learning. Springer series in statistics New York, 2001."},{"key":"e_1_2_1_12_1","first-page":"881","volume-title":"International Conference on Machine Learning","author":"Germain M.","year":"2015","unstructured":"M. Germain , K. Gregor , I. Murray , and H. Larochelle . MADE: Masked autoencoder for distribution estimation . In International Conference on Machine Learning , pages 881 -- 889 , 2015 . M. Germain, K. Gregor, I. Murray, and H. Larochelle. MADE: Masked autoencoder for distribution estimation. In International Conference on Machine Learning, pages 881--889, 2015."},{"key":"e_1_2_1_13_1","first-page":"170","volume-title":"ICML","volume":"1","author":"Getoor L.","year":"2001","unstructured":"L. Getoor , N. Friedman , D. Koller , and B. Taskar . Learning probabilistic models of relational structure . In ICML , volume 1 , pages 170 -- 177 , 2001 . L. Getoor, N. Friedman, D. Koller, and B. Taskar. Learning probabilistic models of relational structure. In ICML, volume 1, pages 170--177, 2001."},{"key":"e_1_2_1_14_1","first-page":"461","volume-title":"ACM SIGMOD Record","author":"Getoor L.","year":"2001","unstructured":"L. Getoor , B. Taskar , and D. Koller . Selectivity estimation using probabilistic models . In ACM SIGMOD Record , volume 30 , pages 461 -- 472 . ACM , 2001 . L. Getoor, B. Taskar, and D. Koller. Selectivity estimation using probabilistic models. In ACM SIGMOD Record, volume 30, pages 461--472. ACM, 2001."},{"key":"e_1_2_1_15_1","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780198572237.001.0001","volume-title":"Probability and random processes","author":"Grimmett G.","year":"2001","unstructured":"G. Grimmett , D. Stirzaker , Probability and random processes . Oxford university press , 2001 . G. Grimmett, D. Stirzaker, et al. Probability and random processes. Oxford university press, 2001."},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-003-0090-4"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/2723372.2749438"},{"key":"e_1_2_1_18_1","first-page":"1102","volume-title":"Synopses for query optimization: A space-complexity perspective","author":"Kaushik R.","year":"2005","unstructured":"R. Kaushik , J. F. Naughton , R. Ramakrishnan , and V. T. Chakravarthy . Synopses for query optimization: A space-complexity perspective . volume 30 , pages 1102 -- 1127 , New York, NY , USA, Dec. 2005 . ACM. R. Kaushik, J. F. Naughton, R. Ramakrishnan, and V. T. Chakravarthy. Synopses for query optimization: A space-complexity perspective. volume 30, pages 1102--1127, New York, NY, USA, Dec. 2005. ACM."},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.14778\/3151106.3151112"},{"key":"e_1_2_1_20_1","volume-title":"3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7--9, 2015, Conference Track Proceedings","author":"Kingma D. P.","year":"2015","unstructured":"D. P. Kingma and J. Ba . Adam: A method for stochastic optimization . In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7--9, 2015, Conference Track Proceedings , 2015 . D. P. Kingma and J. Ba. Adam: A method for stochastic optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7--9, 2015, Conference Track Proceedings, 2015."},{"key":"e_1_2_1_21_1","volume-title":"Github repository, learnedcardinalities. github.com\/andreaskipf\/learnedcardinalities","author":"Kipf A.","year":"2019","unstructured":"A. Kipf . Github repository, learnedcardinalities. github.com\/andreaskipf\/learnedcardinalities , 2019 . [Online; accessed March, 2019]. A. Kipf. Github repository, learnedcardinalities. github.com\/andreaskipf\/learnedcardinalities, 2019. [Online; accessed March, 2019]."},{"key":"e_1_2_1_22_1","volume-title":"CIDR 2019, 9th Biennial Conference on Innovative Data Systems Research","author":"Kipf A.","year":"2019","unstructured":"A. Kipf , T. Kipf , B. Radke , V. Leis , P. A. Boncz , and A. Kemper . Learned cardinalities: Estimating correlated joins with deep learning . In CIDR 2019, 9th Biennial Conference on Innovative Data Systems Research , Asilomar, CA, USA, January 13--16 , 2019 . A. Kipf, T. Kipf, B. Radke, V. Leis, P. A. Boncz, and A. Kemper. Learned cardinalities: Estimating correlated joins with deep learning. In CIDR 2019, 9th Biennial Conference on Innovative Data Systems Research, Asilomar, CA, USA, January 13--16, 2019."},{"key":"e_1_2_1_23_1","volume-title":"Probabilistic graphical models: principles and techniques","author":"Koller D.","year":"2009","unstructured":"D. Koller and N. Friedman . Probabilistic graphical models: principles and techniques . MIT press , 2009 . D. Koller and N. Friedman. Probabilistic graphical models: principles and techniques. MIT press, 2009."},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/SSDM.1999.787640"},{"key":"e_1_2_1_25_1","volume-title":"Learning to optimize join queries with deep reinforcement learning. arXiv preprint arXiv:1808.03196","author":"Krishnan S.","year":"2018","unstructured":"S. Krishnan , Z. Yang , K. Goldberg , J. Hellerstein , and I. Stoica . Learning to optimize join queries with deep reinforcement learning. arXiv preprint arXiv:1808.03196 , 2018 . S. Krishnan, Z. Yang, K. Goldberg, J. Hellerstein, and I. Stoica. Learning to optimize join queries with deep reinforcement learning. arXiv preprint arXiv:1808.03196, 2018."},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.14778\/2850583.2850594"},{"key":"e_1_2_1_27_1","volume-title":"CIDR","author":"Leis V.","year":"2017","unstructured":"V. Leis , B. Radke , A. Gubichev , A. Kemper , and T. Neumann . Cardinality estimation done right: Index-based join sampling . In CIDR , 2017 . V. Leis, B. Radke, A. Gubichev, A. Kemper, and T. Neumann. Cardinality estimation done right: Index-based join sampling. In CIDR, 2017."},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-017-0480-7"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.5555\/2886444.2886453"},{"key":"e_1_2_1_30_1","volume-title":"feedback-kde. bitbucket.org\/mheimel\/feedback-kde","author":"Heimel M.","year":"2019","unstructured":"M. Heimel . Bitbucket repository , feedback-kde. bitbucket.org\/mheimel\/feedback-kde , 2019 . [Online; accessed March, 2019]. M. Heimel. Bitbucket repository, feedback-kde. bitbucket.org\/mheimel\/feedback-kde, 2019. [Online; accessed March, 2019]."},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.14778\/3342263.3342644"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3211954.3211957"},{"key":"e_1_2_1_33_1","first-page":"28","volume-title":"ACM SIGMOD Record","author":"Muralikrishna M.","year":"1988","unstructured":"M. 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Quicksel: Quick selectivity learning with mixture models. arXiv preprint arXiv:1812.10568, 2018."},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2019.00191"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/233269.233342"},{"key":"e_1_2_1_38_1","first-page":"486","volume-title":"VLDB","volume":"97","author":"Poosala V.","year":"1997","unstructured":"V. Poosala and Y. E. Ioannidis . Selectivity estimation without the attribute value independence assumption . In VLDB , volume 97 , pages 486 -- 495 , 1997 . V. Poosala and Y. E. Ioannidis. Selectivity estimation without the attribute value independence assumption. In VLDB, volume 97, pages 486--495, 1997."},{"key":"e_1_2_1_39_1","volume-title":"Language models are unsupervised multitask learners. URL https:\/\/openai.com\/blog\/better-language-models","author":"Radford A.","year":"2019","unstructured":"A. Radford , J. Wu , R. Child , D. Luan , D. Amodei , and I. Sutskever . Language models are unsupervised multitask learners. URL https:\/\/openai.com\/blog\/better-language-models , 2019 . A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever. Language models are unsupervised multitask learners. URL https:\/\/openai.com\/blog\/better-language-models, 2019."},{"key":"e_1_2_1_40_1","volume-title":"5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24--26, 2017, Conference Track Proceedings","author":"Salimans T.","year":"2017","unstructured":"T. Salimans , A. Karpathy , X. Chen , and D. P. Kingma . PixelCNN++: Improving the pixelcnn with discretized logistic mixture likelihood and other modifications . In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24--26, 2017, Conference Track Proceedings , 2017 . T. Salimans, A. Karpathy, X. Chen, and D. P. Kingma. PixelCNN++: Improving the pixelcnn with discretized logistic mixture likelihood and other modifications. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24--26, 2017, Conference Track Proceedings, 2017."},{"key":"e_1_2_1_41_1","doi-asserted-by":"crossref","DOI":"10.1002\/9780470316849","volume-title":"Multivariate Density Estimation Theory, Practice, and Visualization","author":"Scott D. W.","year":"1992","unstructured":"D. W. Scott . Multivariate Density Estimation Theory, Practice, and Visualization . John Wiley & Sons, Inc. , 1992 . D. W. Scott. Multivariate Density Estimation Theory, Practice, and Visualization. John Wiley & Sons, Inc., 1992."},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/582095.582099"},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2006.84"},{"key":"e_1_2_1_44_1","volume-title":"snowmobile, and boat registrations. catalog.data.gov\/dataset\/vehicle-snowmobile-and-boat-registrations","author":"State of New York. Vehicle","year":"2019","unstructured":"State of New York. Vehicle , snowmobile, and boat registrations. catalog.data.gov\/dataset\/vehicle-snowmobile-and-boat-registrations , 2019 . [Online; accessed March 1st, 2019]. State of New York. Vehicle, snowmobile, and boat registrations. catalog.data.gov\/dataset\/vehicle-snowmobile-and-boat-registrations, 2019. [Online; accessed March 1st, 2019]."},{"key":"e_1_2_1_45_1","first-page":"19","volume-title":"VLDB","volume":"1","author":"Stillger M.","year":"2001","unstructured":"M. Stillger , G. M. Lohman , V. Markl , and M. Kandil . LEO-DB2's learning optimizer . In VLDB , volume 1 , pages 19 -- 28 , 2001 . M. Stillger, G. M. Lohman, V. Markl, and M. Kandil. LEO-DB2's learning optimizer. In VLDB, volume 1, pages 19--28, 2001."},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.14778\/3402707.3402724"},{"key":"e_1_2_1_47_1","first-page":"467","volume-title":"Proceedings of the 31st International Conference on International Conference on Machine Learning -","volume":"32","author":"Uria B.","unstructured":"B. Uria , I. Murray , and H. Larochelle . A deep and tractable density estimator . In Proceedings of the 31st International Conference on International Conference on Machine Learning - Volume 32 , ICML'14, pages I- 467 -I-475. JMLR.org, 2014. B. Uria, I. Murray, and H. Larochelle. A deep and tractable density estimator. In Proceedings of the 31st International Conference on International Conference on Machine Learning - Volume 32, ICML'14, pages I-467-I-475. JMLR.org, 2014."},{"key":"e_1_2_1_48_1","volume-title":"WaveNet: A generative model for raw audio. arXiv preprint arXiv:1609.03499","author":"den Oord A. Van","year":"2016","unstructured":"A. Van den Oord , S. Dieleman , H. Zen , K. Simonyan , O. Vinyals , A. Graves , N. Kalchbrenner , A. Senior , and K. Kavukcuoglu . WaveNet: A generative model for raw audio. arXiv preprint arXiv:1609.03499 , 2016 . A. Van den Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. Senior, and K. Kavukcuoglu. WaveNet: A generative model for raw audio. arXiv preprint arXiv:1609.03499, 2016."},{"key":"e_1_2_1_49_1","first-page":"4790","volume-title":"Advances in neural information processing systems","author":"den Oord A. Van","year":"2016","unstructured":"A. Van den Oord , N. Kalchbrenner , L. Espeholt , O. Vinyals , A. Graves , Conditional image generation with pixelcnn decoders . In Advances in neural information processing systems , pages 4790 -- 4798 , 2016 . A. Van den Oord, N. Kalchbrenner, L. Espeholt, O. Vinyals, A. Graves, et al. Conditional image generation with pixelcnn decoders. In Advances in neural information processing systems, pages 4790--4798, 2016."},{"key":"e_1_2_1_50_1","first-page":"5998","volume-title":"Advances in neural information processing systems","author":"Vaswani A.","year":"2017","unstructured":"A. Vaswani , N. Shazeer , N. Parmar , J. Uszkoreit , L. Jones , A. N. Gomez , \u0141. Kaiser, and I. Polosukhin . Attention is all you need . In Advances in neural information processing systems , pages 5998 -- 6008 , 2017 . A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, \u0141. Kaiser, and I. Polosukhin. Attention is all you need. In Advances in neural information processing systems, pages 5998--6008, 2017."},{"key":"e_1_2_1_51_1","volume-title":"ICDT","author":"Veldhuizen T. L.","year":"2014","unstructured":"T. L. Veldhuizen . Triejoin : A simple, worst-case optimal join algorithm . In ICDT , 2014 . T. L. Veldhuizen. Triejoin: A simple, worst-case optimal join algorithm. In ICDT, 2014."},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.5555\/1315451.1315477"},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.14778\/3291264.3291267"},{"key":"e_1_2_1_54_1","volume-title":"Xlnet: Generalized autoregressive pretraining for language understanding. arXiv preprint arXiv:1906.08237","author":"Yang Z.","year":"2019","unstructured":"Z. Yang , Z. Dai , Y. Yang , J. Carbonell , R. Salakhutdinov , and Q. V. Le . Xlnet: Generalized autoregressive pretraining for language understanding. arXiv preprint arXiv:1906.08237 , 2019 . Z. Yang, Z. Dai, Y. Yang, J. Carbonell, R. Salakhutdinov, and Q. V. Le. 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