{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T06:00:02Z","timestamp":1672293602592},"reference-count":3,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2022,8]]},"abstract":"<jats:p>Machine learning models are everywhere now; but only few of them are transparent in how they work. To remedy this, local explanations aim to show users how and why learned models produce a certain output for a given input (data sample). However, most existing approaches are oriented around images or text data and, thus, cannot leverage the structure and properties of tabular data. Therefore, we demonstrate Quest, a new framework for generating explanations that are a better fit for tabular data. The main idea is to create explanations in the form of relational predicates (called queries hereafter) that approximate the behavior of a classifier around the given sample. For this demo, we use Quest on different synthetic and real-world tabular data sets and pair it with a user interface intended to be used during model development by a data scientist working on classification models.<\/jats:p>","DOI":"10.14778\/3554821.3554884","type":"journal-article","created":{"date-parts":[[2022,9,29]],"date-time":"2022-09-29T22:28:39Z","timestamp":1664490519000},"page":"3722-3725","source":"Crossref","is-referenced-by-count":0,"title":["Demonstrating quest"],"prefix":"10.14778","volume":"15","author":[{"given":"Nadja","family":"Geisler","sequence":"first","affiliation":[{"name":"Technical University of Darmstadt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Benjamin","family":"H\u00e4ttasch","sequence":"additional","affiliation":[{"name":"Technical University of Darmstadt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carsten","family":"Binnig","sequence":"additional","affiliation":[{"name":"Technical University of Darmstadt"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,9,29]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"I","article-title":"A Unified Approach to Interpreting Model Predictions","volume":"30","author":"Lundberg Scott M","year":"2017","unstructured":"Scott M Lundberg and Su-In Lee . 2017 . A Unified Approach to Interpreting Model Predictions . In Advances in Neural Information Processing Systems 30 , I . Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.). Curran Associates, Inc., Red Hook, NY, USA, 4765--4774. http:\/\/papers.nips.cc\/paper\/7062-a-unified-approach-to-interpreting-model-predictions.pdf Scott M Lundberg and Su-In Lee. 2017. A Unified Approach to Interpreting Model Predictions. In Advances in Neural Information Processing Systems 30, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.). Curran Associates, Inc., Red Hook, NY, USA, 4765--4774. http:\/\/papers.nips.cc\/paper\/7062-a-unified-approach-to-interpreting-model-predictions.pdf","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939778"},{"key":"e_1_2_1_3_1","unstructured":"Marco Tulio Ribeiro Sameer Singh and Carlos Guestrin. 2018. Anchors: High-Precision Model-Agnostic Explanations. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence (AAAI'18\/IAAI'18\/EAAI'18). AAAI Press New Orleans Louisiana USA Article 187 9 pages.  Marco Tulio Ribeiro Sameer Singh and Carlos Guestrin. 2018. Anchors: High-Precision Model-Agnostic Explanations. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence (AAAI'18\/IAAI'18\/EAAI'18) . AAAI Press New Orleans Louisiana USA Article 187 9 pages."}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3554821.3554884","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T11:36:09Z","timestamp":1672227369000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3554821.3554884"}},"subtitle":["a query-driven framework to explain classification models on tabular data"],"short-title":[],"issued":{"date-parts":[[2022,8]]},"references-count":3,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["10.14778\/3554821.3554884"],"URL":"https:\/\/doi.org\/10.14778\/3554821.3554884","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2022,8]]}}}