{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T05:23:30Z","timestamp":1743139410741,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030757618"},{"type":"electronic","value":"9783030757625"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-75762-5_45","type":"book-chapter","created":{"date-parts":[[2021,5,8]],"date-time":"2021-05-08T09:07:43Z","timestamp":1620464863000},"page":"566-577","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Maximizing Explainability with SF-Lasso and Selective Inference for Video and Picture Ads"],"prefix":"10.1007","author":[{"given":"Eunkyung","family":"Park","sequence":"first","affiliation":[]},{"given":"Raymond K.","family":"Wong","sequence":"additional","affiliation":[]},{"given":"Junbum","family":"Kwon","sequence":"additional","affiliation":[]},{"given":"Victor W.","family":"Chu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,5,9]]},"reference":[{"key":"45_CR1","doi-asserted-by":"publisher","first-page":"521","DOI":"10.3150\/11-BEJ410","volume":"19","author":"A Belloni","year":"2013","unstructured":"Belloni, A., Chernozhukov, V.: Least squares after model selection in high-dimensional sparse models. Bernoulli 19, 521\u2013547 (2013)","journal-title":"Bernoulli"},{"key":"45_CR2","unstructured":"Chen, J., Song, L., Wainwright, M.J., Jordan, M.I.: Learning to explain: an information-theoretic perspective on model interpretation. In: ICML, vol. 80, pp. 882\u2013891 (2018)"},{"key":"45_CR3","unstructured":"Doshi-Velez, F., Kim, B.: Towards a rigorous science of interpretable machine learning. arXiv, Machine Learning (2017)"},{"issue":"1","key":"45_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v033.i01","volume":"33","author":"JH Friedman","year":"2010","unstructured":"Friedman, J.H., Hastie, T.J., Tibshirani, R.: Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33(1), 1\u201322 (2010)","journal-title":"J. Stat. Softw."},{"key":"45_CR5","doi-asserted-by":"crossref","unstructured":"Hara, S., Maehara, T.: Enumerate lasso solutions for feature selection. In: AAAI, pp. 1985\u20131991 (2017)","DOI":"10.1609\/aaai.v31i1.10793"},{"key":"45_CR6","doi-asserted-by":"crossref","unstructured":"Harder, F., Bauer, M., Park, M.: Interpretable and differentially private predictions. In: AAAI, pp. 4083\u20134090 (2020)","DOI":"10.1609\/aaai.v34i04.5827"},{"key":"45_CR7","series-title":"Springer Series in Statistics","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-21606-5","volume-title":"The Elements of Statistical Learning","author":"T Hastie","year":"2001","unstructured":"Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer Series in Statistics. Springer, New York (2001)"},{"key":"45_CR8","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., Wainwright, M.: Statistical Learning with Sparsity: The Lasso and Generalizations (2015)","DOI":"10.1201\/b18401"},{"issue":"1","key":"45_CR9","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1080\/00401706.1970.10488634","volume":"12","author":"AE Hoerl","year":"1970","unstructured":"Hoerl, A.E., Kennard, R.W.: Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12(1), 55\u201367 (1970)","journal-title":"Technometrics"},{"key":"45_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.patcog.2018.05.013","volume":"83","author":"Y Kim","year":"2018","unstructured":"Kim, Y., Bum Kim, S.: Collinear groupwise feature selection via discrete fusion group regression. Pattern Recognit. 83, 1\u201313 (2018)","journal-title":"Pattern Recognit."},{"issue":"3","key":"45_CR11","doi-asserted-by":"publisher","first-page":"907","DOI":"10.1214\/15-AOS1371","volume":"44","author":"JD Lee","year":"2016","unstructured":"Lee, J.D., Sun, D.L., Sun, Y., Taylor, J.E.: Exact post-selection inference, with application to the lasso. Ann. Stat. 44(3), 907\u2013927 (2016)","journal-title":"Ann. Stat."},{"issue":"2","key":"45_CR12","first-page":"413","volume":"42","author":"R Lockhart","year":"2014","unstructured":"Lockhart, R., Taylor, J., Tibshirani, R.J., Tibshirani, R.: A significance test for the lasso. Ann. Stat. 42(2), 413\u2013468 (2014)","journal-title":"Ann. Stat."},{"key":"45_CR13","unstructured":"Ross, A.S., Lage, I., Doshi-Velez, F.: The neural lasso: local linear sparsity for interpretable explanations. In: NIPS (2017)"},{"key":"45_CR14","unstructured":"Shrikumar, A., Greenside, P., Kundaje, A.: Learning important features through propagating activation differences. In: ICML, pp. 3145\u20133153 (2017)"},{"key":"45_CR15","unstructured":"Shrikumar, A., Greenside, P., Kundaje, A.: Learning important features through propagating activation differences. In: ICML, vol. 70, pp. 3145\u20133153 (2017)"},{"key":"45_CR16","unstructured":"Singh, C., Murdoch, W.J., Yu, B.: Hierarchical interpretations for neural network predictions. In: ICLR (2019)"},{"issue":"1","key":"45_CR17","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1002\/cjs.11313","volume":"46","author":"J Taylor","year":"2018","unstructured":"Taylor, J., Tibshirani, R.: Post-selection inference for $$l$$1-penalized likelihood models. Can. J. Stat. 46(1), 41\u201361 (2018)","journal-title":"Can. J. Stat."},{"issue":"1","key":"45_CR18","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani, R.: Regression shrinkage and selection via the lasso. J. Royal Stat. Soc. Series B (Methodol.) 58(1), 267\u2013288 (1996)","journal-title":"J. Royal Stat. Soc. Series B (Methodol.)"},{"key":"45_CR19","doi-asserted-by":"crossref","unstructured":"Tu, M., Huang, K., Wang, G., Huang, J., He, X., Zhou, B.: Select, answer and explain: interpretable multi-hop reading comprehension over multiple documents. In: AAAI, pp. 9073\u20139080 (2020)","DOI":"10.1609\/aaai.v34i05.6441"},{"key":"45_CR20","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","volume":"67","author":"H Zou","year":"2005","unstructured":"Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. J. Roy. Stat. Soc. B 67, 301\u2013320 (2005)","journal-title":"J. Roy. Stat. Soc. B"}],"container-title":["Lecture Notes in Computer Science","Advances in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-75762-5_45","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T12:19:25Z","timestamp":1725020365000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-75762-5_45"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030757618","9783030757625"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-75762-5_45","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"9 May 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 May 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 May 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/pakdd2021.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"673","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"157","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"23% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}