{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T17:51:19Z","timestamp":1764784279311,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":57,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,7,25]],"date-time":"2019-07-25T00:00:00Z","timestamp":1564012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1763618"],"award-info":[{"award-number":["1763618"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,7,25]]},"DOI":"10.1145\/3292500.3330997","type":"proceedings-article","created":{"date-parts":[[2019,7,26]],"date-time":"2019-07-26T13:17:26Z","timestamp":1564147046000},"page":"714-722","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":37,"title":["Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space"],"prefix":"10.1145","author":[{"given":"Nicholas","family":"Monath","sequence":"first","affiliation":[{"name":"University of Massachusetts Amherst, Amherst, MA, USA"}]},{"given":"Manzil","family":"Zaheer","sequence":"additional","affiliation":[{"name":"Google Research, Mountain View, CA, USA"}]},{"given":"Daniel","family":"Silva","sequence":"additional","affiliation":[{"name":"Google Research, Mountain View, CA, USA"}]},{"given":"Andrew","family":"McCallum","sequence":"additional","affiliation":[{"name":"University of Massachusetts Amherst, Amherst, MA, USA"}]},{"given":"Amr","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Google Research, Mountain View, CA, USA"}]}],"member":"320","published-online":{"date-parts":[[2019,7,25]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"R. P. Adams Z. Ghahramani and M. I. Jordan. 2010. Tree-Structured Stick Breaking for Hierarchical Data.  R. P. Adams Z. Ghahramani and M. I. Jordan. 2010. Tree-Structured Stick Breaking for Hierarchical Data."},{"key":"e_1_3_2_1_2_1","unstructured":"O. Bachem M Lucic H. Hassani and A. Krause. 2016. Fast and provably good seedings for k-means. NeurIPS.   O. Bachem M Lucic H. Hassani and A. Krause. 2016. Fast and provably good seedings for k-means. NeurIPS."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1374376.1374474"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"crossref","unstructured":"J. Bingham and S. Sudarsanam. 2000. Visualizing large hierarchical clusters in hyperbolic space. Bioinformatics.  J. Bingham and S. Sudarsanam. 2000. Visualizing large hierarchical clusters in hyperbolic space. Bioinformatics.","DOI":"10.1093\/bioinformatics\/16.7.660"},{"key":"e_1_3_2_1_5_1","unstructured":"C. Blundell Y. W. Teh and K. A. Heller. 2010. Bayesian rose trees. UAI.   C. Blundell Y. W. Teh and K. A. Heller. 2010. Bayesian rose trees. UAI."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"F. Bogun\u00e1 M.and Papadopoulos and D. Krioukov. 2010. Sustaining the internet with hyperbolic mapping. Nature communications.  F. Bogun\u00e1 M.and Papadopoulos and D. Krioukov. 2010. Sustaining the internet with hyperbolic mapping. Nature communications.","DOI":"10.1038\/ncomms1063"},{"volume-title":"Stochastic gradient descent on Riemannian manifolds","author":"Bonnabel S.","key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","DOI":"10.1109\/TAC.2013.2254619"},{"key":"e_1_3_2_1_8_1","unstructured":"P. F. Brown P. V Desouza R. L Mercer V. J D. Pietra and J. C Lai. 1992. Class-based n-gram models of natural language. Computational linguistics.   P. F. Brown P. V Desouza R. L Mercer V. J D. Pietra and J. C Lai. 1992. Class-based n-gram models of natural language. Computational linguistics."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","unstructured":"M. Charikar and V. Chatziafratis. 2017. Approximate hierarchical clustering via sparsest cut and spreading metrics. SODA.   M. Charikar and V. Chatziafratis. 2017. Approximate hierarchical clustering via sparsest cut and spreading metrics. SODA.","DOI":"10.1137\/1.9781611974782.53"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","unstructured":"M. Charikar V. Chatziafratis and R. Niazadeh. 2019 a. Hierarchical Clustering better than Average-Linkage. SODA.   M. Charikar V. Chatziafratis and R. Niazadeh. 2019 a. Hierarchical Clustering better than Average-Linkage. SODA.","DOI":"10.1137\/1.9781611975482.139"},{"key":"e_1_3_2_1_11_1","unstructured":"M. Charikar V. Chatziafratis R. Niazadeh and G. Yaroslavtsev. 2019 b. Hierarchical Clustering for Euclidean Data. AISTATS.  M. Charikar V. Chatziafratis R. Niazadeh and G. Yaroslavtsev. 2019 b. Hierarchical Clustering for Euclidean Data. AISTATS."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"crossref","unstructured":"K. Clark and C. D Manning. 2016. Improving coreference resolution by learning entity-level distributed representations. ACL.  K. Clark and C. D Manning. 2016. Improving coreference resolution by learning entity-level distributed representations. ACL.","DOI":"10.18653\/v1\/P16-1061"},{"key":"e_1_3_2_1_13_1","unstructured":"V. Cohen-Addad V. Kanade and F. Mallmann-Trenn. 2017. Hierarchical clustering beyond the worst-case. NeurIPS.   V. Cohen-Addad V. Kanade and F. Mallmann-Trenn. 2017. Hierarchical clustering beyond the worst-case. NeurIPS."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"crossref","unstructured":"V. Cohen-Addad V. Kanade F. Mallmann-Trenn and C. Mathieu. 2018. Hierarchical clustering: Objective functions and algorithms. SODA.   V. Cohen-Addad V. Kanade F. Mallmann-Trenn and C. Mathieu. 2018. Hierarchical clustering: Objective functions and algorithms. SODA.","DOI":"10.1137\/1.9781611975031.26"},{"key":"e_1_3_2_1_15_1","unstructured":"A. Culotta P. Kanani R. Hall M. Wick and A. McCallum. 2007. Author disambiguation using error-driven machine learning with a ranking loss function. IIWeb.  A. Culotta P. Kanani R. Hall M. Wick and A. McCallum. 2007. Author disambiguation using error-driven machine learning with a ranking loss function. IIWeb."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/2897518.2897527"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"E. Emamjomeh-Zadeh and D. Kempe. 2018. Adaptive hierarchical clustering using ordinal queries. SODA.   E. Emamjomeh-Zadeh and D. Kempe. 2018. Adaptive hierarchical clustering using ordinal queries. SODA.","DOI":"10.1137\/1.9781611975031.28"},{"volume-title":"BICO: BIRCH meets coresets for k-means clustering. ESA.","year":"2013","author":"Fichtenberger H.","key":"e_1_3_2_1_18_1"},{"key":"e_1_3_2_1_19_1","unstructured":"O.E. Ganea G. B\u00e9cigneul and T. Hofmann. 2018. Hyperbolic Entailment Cones for Learning Hierarchical Embeddings. ICML.  O.E. Ganea G. B\u00e9cigneul and T. Hofmann. 2018. Hyperbolic Entailment Cones for Learning Hierarchical Embeddings. ICML."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"crossref","unstructured":"P. Goyal Z. Hu X. Liang C. Wang and E. P. Xing. 2017. Nonparametric variational auto-encoders for hierarchical representation learning. ICCV.  P. Goyal Z. Hu X. Liang C. Wang and E. P. Xing. 2017. Nonparametric variational auto-encoders for hierarchical representation learning. ICCV.","DOI":"10.1109\/ICCV.2017.545"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"crossref","unstructured":"V. Guillemin and A. Pollack. 2010. Differential topology.  V. Guillemin and A. Pollack. 2010. Differential topology.","DOI":"10.1090\/chel\/370"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/1102351.1102389"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"crossref","unstructured":"J. Himberg A. Hyv\"arinen and F. Esposito. 2004. Validating the independent components of neuroimaging time series via clustering and visualization. Neuroimage.  J. Himberg A. Hyv\"arinen and F. Esposito. 2004. Validating the independent components of neuroimaging time series via clustering and visualization. Neuroimage.","DOI":"10.1016\/S1053-8119(04)00166-1"},{"key":"e_1_3_2_1_24_1","unstructured":"Y. Jernite A. Choromanska and D. Sontag. 2017. Simultaneous learning of trees and representations for extreme classification and density estimation. ICML.   Y. Jernite A. Choromanska and D. Sontag. 2017. Simultaneous learning of trees and representations for extreme classification and density estimation. ICML."},{"volume":"201","journal-title":"J. Ba.","author":"Kingma D. P","key":"e_1_3_2_1_25_1"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFCOM.2007.221"},{"key":"e_1_3_2_1_27_1","unstructured":"D. A Knowles and Z. Ghahramani. 2011. Pitman-Yor diffusion trees. UAI.   D. A Knowles and Z. Ghahramani. 2011. Pitman-Yor diffusion trees. UAI."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098079"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"crossref","unstructured":"D. Krioukov F. Papadopoulos M. Kitsak A. Vahdat and M. Bogun\u00e1. 2010. Hyperbolic geometry of complex networks. Physical Review E.  D. Krioukov F. Papadopoulos M. Kitsak A. Vahdat and M. Bogun\u00e1. 2010. Hyperbolic geometry of complex networks. Physical Review E.","DOI":"10.1103\/PhysRevE.82.036106"},{"key":"e_1_3_2_1_30_1","unstructured":"A. Krishnamurthy S. Balakrishnan M. Xu and A. Singh. 2012. Efficient active algorithms for hierarchical clustering. ICML.   A. Krishnamurthy S. Balakrishnan M. Xu and A. Singh. 2012. Efficient active algorithms for hierarchical clustering. ICML."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/192426.192430"},{"key":"e_1_3_2_1_32_1","unstructured":"H. Lee M. Recasens A. Chang M. Surdeanu and D. Jurafsky. 2012. Joint entity and event coreference resolution across documents. EMNLP.   H. Lee M. Recasens A. Chang M. Surdeanu and D. Jurafsky. 2012. Joint entity and event coreference resolution across documents. EMNLP."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"crossref","unstructured":"K. Lee L. He M. Lewis and L. Zettlemoyer. 2017. End-to-end neural coreference resolution. EMNLP.  K. Lee L. He M. Lewis and L. Zettlemoyer. 2017. End-to-end neural coreference resolution. EMNLP.","DOI":"10.18653\/v1\/D17-1018"},{"volume":"201","journal-title":"J. Wang.","author":"Moseley B.","key":"e_1_3_2_1_34_1"},{"key":"e_1_3_2_1_35_1","unstructured":"M. Nickel and D. Kiela. 2017. Poincar\u00e9 embeddings for learning hierarchical representations. NeurIPS.   M. Nickel and D. Kiela. 2017. Poincar\u00e9 embeddings for learning hierarchical representations. NeurIPS."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"crossref","unstructured":"J. Pennington R. Socher and C. D. Manning. 2014. GloVe: Global Vectors for Word Representation. EMNLP.  J. Pennington R. Socher and C. D. Manning. 2014. GloVe: Global Vectors for Word Representation. EMNLP.","DOI":"10.3115\/v1\/D14-1162"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"crossref","unstructured":"L. Ratinov and D. Roth. 2009. Design challenges and misconceptions in named entity recognition. ACL.   L. Ratinov and D. Roth. 2009. Design challenges and misconceptions in named entity recognition. ACL.","DOI":"10.3115\/1596374.1596399"},{"volume-title":"BPR: Bayesian personalized ranking from implicit feedback. UAI.","year":"2009","author":"Rendle S.","key":"e_1_3_2_1_38_1"},{"key":"e_1_3_2_1_39_1","unstructured":"S. Roy and S. Pokutta. 2016. Hierarchical clustering via spreading metrics. NeurIPS.   S. Roy and S. Pokutta. 2016. Hierarchical clustering via spreading metrics. NeurIPS."},{"key":"e_1_3_2_1_40_1","unstructured":"F. Sala C. De Sa A. Gu and C. R\u00e9. 2018. Representation tradeoffs for hyperbolic embeddings. ICML.  F. Sala C. De Sa A. Gu and C. R\u00e9. 2018. Representation tradeoffs for hyperbolic embeddings. ICML."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-25878-7_34"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772862"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2002.1016905"},{"volume-title":"S. S Jeffrey, et almbox.","year":"2001","author":"S\u00f8rlie T.","key":"e_1_3_2_1_44_1"},{"key":"e_1_3_2_1_45_1","unstructured":"M. Spivak. 1979. A comprehensive introduction to differential geometry Publish or Perish.  M. Spivak. 1979. A comprehensive introduction to differential geometry Publish or Perish."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"crossref","unstructured":"E. Strubell P. Verga D. Andor D. Weiss and A. McCallum. 2018. Linguistically-Informed Self-Attention for Semantic Role Labeling. EMNLP.  E. Strubell P. Verga D. Andor D. Weiss and A. McCallum. 2018. Linguistically-Informed Self-Attention for Semantic Role Labeling. EMNLP.","DOI":"10.18653\/v1\/D18-1548"},{"volume-title":"Poincare Glove: Hyperbolic Word Embeddings. ICLR.","year":"2019","author":"Tifrea Alexandru","key":"e_1_3_2_1_47_1"},{"key":"e_1_3_2_1_48_1","unstructured":"T. D. Q. Vinh Y. Tay S. Zhang G. Cong and X.-L. Li. 2018. Hyperbolic Recommender Systems. arxiv.  T. D. Q. Vinh Y. Tay S. Zhang G. Cong and X.-L. Li. 2018. Hyperbolic Recommender Systems. arxiv."},{"key":"e_1_3_2_1_49_1","unstructured":"D. Wang and Y. Wang. 2018. An Improved Cost Function for Hierarchical Cluster Trees. arXiv.  D. Wang and Y. Wang. 2018. An Improved Cost Function for Hierarchical Cluster Trees. arXiv."},{"key":"e_1_3_2_1_50_1","unstructured":"M. Wick S. Singh and A. McCallum. 2012. A discriminative hierarchical model for fast coreference at large scale. ACL.   M. Wick S. Singh and A. McCallum. 2012. A discriminative hierarchical model for fast coreference at large scale. ACL."},{"volume":"200","journal-title":"J. Yen.","author":"Widyantoro D. H","key":"e_1_3_2_1_51_1"},{"volume-title":"Taskonomy: Disentangling task transfer learning. CVPR.","year":"2018","author":"Zamir A. R","key":"e_1_3_2_1_52_1"},{"key":"e_1_3_2_1_53_1","unstructured":"H. Zhang S. J Reddi and S. Sra. 2016. Riemannian SVRG: Fast stochastic optimization on Riemannian manifolds. NeurIPS.   H. Zhang S. J Reddi and S. Sra. 2016. Riemannian SVRG: Fast stochastic optimization on Riemannian manifolds. NeurIPS."},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/233269.233324"},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"crossref","unstructured":"Y. Zhang A. Ahmed V. Josifovski and A. Smola. 2014. Taxonomy discovery for personalized recommendation. ICDM.  Y. Zhang A. Ahmed V. Josifovski and A. Smola. 2014. Taxonomy discovery for personalized recommendation. ICDM.","DOI":"10.1145\/2556195.2556236"},{"key":"e_1_3_2_1_56_1","unstructured":"Y. Zhang and D.-Y. Yeung. 2010. A Convex Formulation for Learning Task Relationships in Multi-Task Learning citation. UAI.   Y. Zhang and D.-Y. Yeung. 2010. A Convex Formulation for Learning Task Relationships in Multi-Task Learning citation. UAI."},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/584792.584877"}],"event":{"name":"KDD '19: The 25th 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":"Anchorage AK USA","acronym":"KDD '19"},"container-title":["Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3292500.3330997","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3292500.3330997","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3292500.3330997","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:26:04Z","timestamp":1750206364000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3292500.3330997"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,25]]},"references-count":57,"alternative-id":["10.1145\/3292500.3330997","10.1145\/3292500"],"URL":"https:\/\/doi.org\/10.1145\/3292500.3330997","relation":{},"subject":[],"published":{"date-parts":[[2019,7,25]]},"assertion":[{"value":"2019-07-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}