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We study the interpretability of predictive systems that use high-dimensonal behavioral and textual data.
URL http://arxiv.org/abs/1901.04592
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Dumais, T.S., Platt, J., Hecherman, D., Sahami, M.: Inductive learning algorithms and representations for text categorization · 1998
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In: Proceedings of the 10th European Conference on Machine Learning, ECML ’98, pp. 137–142. Springer-Verlag, London, UK, UK (1998)
Joachims, T.: Text categorization with suport vector machines: Learning with many relevant features · 1998
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Gregor, S., Benbasat, I.: Explanations from intelligent systems: Theoretical foundations and implications for practice · 1999
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In: Machine Learning, pp. 5–32 (2001)
Statistics, L.B., Breiman, L.: Random forests · 2001
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Hsu, C.N., Chung, H.H., Huang, H.S.: Mining skewed and sparse transaction data for personalized shopping recommendation 57
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Brozovsky, L., Petricek, V.: Recommender system for online dating service (2007)
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EJOR 183
Martens, D., Baesens, B., van Gestel, T., Vanthienen, J.: Comprehensible credit scoring models using rule extraction from support vector machines · 2007
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Mislove, A., Marcon, M., Gummadi, K.P., Druschel, P., Bhattacharjee, B.: Measurement and Analysis of Online Social Networks (2007)
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Quercia, D., Kosinski, M., Stillwell, D., Crowcroft, J.: Our twitter profiles, our selves: Predicting personality with twitter · 2011
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Li, P., Owen, A.B., Zhang, C.: One permutation hashing for efficient search and learning · 2012
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BMC Med. Res. Meth. 13
Fagerland, M., Lydersen, S., Laake, P.: Mcnemar test for binary matched-pairs data: Mid-p and asymptotic better than exact conditional · 2013
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Big Data 1
Junqué de Fortuny, E., David, M., Foster, P.: Predictive modeling with big data: is bigger really better? · 2013
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National Academy of Sciences 110
Kosinski, M., Stillwell, D., Graepel, T.: Private traits and attributes are predictable from digital records of human behavior · 2013
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SIGKDD Explor. Newsl. 15
Freitas, A.: Comprehensible classification models: a position paper · 2014
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MIS Quarterly 38
Martens, D., Provost, F.: Explaining data-driven document classifications · 2014
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Keynote Lecture, Strata Europe. (2014)
Provost, F.: Understanding decisions driven by big data: From analytics management to privacy-friendly cloaking devices · 2014
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Kdd cup 2015 data · 2015
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Quality Engineering 29
Shmueli, G.: Analyzing behavioral big data: Methodological, practical, ethical, and moral issues · 2016
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Big Data 5
Chen, D., Fraiberger, S., Moakler, R., Provost, F.: Enhancing transparency and control when drawing data-driven inferences about individuals · 2017
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Doshi-Velez, F., Kim, B.: Towards a rigorous science of interpretable machine learning (2017)
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In: NIPS Conference (2017)
Lundberg, S., Lee, S.I.: A unified approach to interpreting model predictions · 2017
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Proceedings of the National Academy of Sciences 114
Matz, S.C., Kosinski, M., Nave, G., Stillwell, D.J.: Psychological targeting as an effective approach to digital mass persuasion · 2017
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Clark, J., Provost, F.: Dimensionality reduction via matrix factorization for predictive modeling from large, sparse behavioral data (2015)
2015
Cited alongside, same era.
Tech. rep., Department of Applied Economics, Antwerp University, Belgium (2015)
De Cnudde, S., Moeyersoms, J., Stankova, M., Tobback, E., Javaly, V., Martens, D.: Who cares about your Facebook friends? Credit scoring for microfinance · 2015
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ACM Trans. Interact. Intell. Syst. 5
Harper, F.M., Konstan, J.A.: The movielens datasets: History and context · 2015
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Samek, W., Binder, A., Montavon, G., Bach, S., Klaus-Robert, M.: Evaluating the visualization of what a deep neural network has learned · 2015
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Working papers, University of Antwerp, Faculty of Business and Economics (2015)
Stankova, M., Martens, D., Provost, F.: Classification over bipartite graphs through projection · 2015
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CoRR (2016)
Arras, L., Horn, F., Montavon, G., Müller, K., Samek, W.: "what is relevant in a text document?": An interpretable machine learning approach · 2016
Cited alongside, same era.
Wachter, S., Mittelstadt, B.D., Russell, C.: Counterfactual explanations without opening the black box: Automated decisions and the GDPR · 2017
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Guidotti, R., Monreale, A., Ruggieri, S., Pedreschi, D., Turini, F., Giannotti, F.: Local rule-based explanations of black box decision systems · 2018
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Hendricks, L.A., Hu, R., Darrell, T., Akata, Z.: Grounding visual explanations · 2018
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Artificial Intelligence 267
Miller, T.: Explanation in artificial intelligence: Insights from the social sciences · 2018
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Psychological Science (2018)
Nave, G., Kosinski, M., Stillwell, D., Rentfrow, J., Minxha, J., Greenberg, D.: Musical preferences predict personality: Evidence from active listening and facebook likes · 2018
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In: The 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2018)
Nguyen, D.: Comparing automatic and human evaluation of local explanations for text classification · 2018
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CoRR (2018)
Ras, G., van Gerven, M., Haselager, P.: Explanation methods in deep learning: Users, values, concerns and challenges · 2018
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Journal of the ACM (JACM) 65
Schreiber, E.L., Korf, R.E., Moffitt, M.D.: Optimal multi-way number partitioning · 2018
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Stiene, P., David, M.: I like, therefore, i am (2018)
2018
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International Journal of Data Science and Analytics (2019)
De Cnudde, S., Martens, D., Evgeniou, T., Provost, F.: A benchmarking study of classification techniques for behavioral data · 2019
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In: SafeAI@AAAI, CEUR Workshop Proceedings , vol. 2301. CEUR-WS.org (2019)
Sokol, K., Flach, P.A.: Counterfactual explanations of machine learning predictions: Opportunities and challenges for AI safety · 2019
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