Automated Data Slicing for Model Validation: A Big Data - AI Integration Approach
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Tim Kraska | Neoklis Polyzotis | Steven Euijong Whang | Yeounoh Chung | Ki Hyun Tae | Tim Kraska | Neoklis Polyzotis | Yeounoh Chung | N. Polyzotis
[1] Sanjay Krishnan,et al. PALM: Machine Learning Explanations For Iterative Debugging , 2017, HILDA@SIGMOD.
[2] Luca Oneto,et al. Fairness in Machine Learning , 2020, INNSBDDL.
[3] Parag Agrawal,et al. Interpretable and Informative Explanations of Outcomes , 2014, Proc. VLDB Endow..
[4] Alex Alves Freitas,et al. Comprehensible classification models: a position paper , 2014, SKDD.
[5] Sunita Sarawagi,et al. Intelligent Rollups in Multidimensional OLAP Data , 2001, VLDB.
[6] Welch Bl. THE GENERALIZATION OF ‘STUDENT'S’ PROBLEM WHEN SEVERAL DIFFERENT POPULATION VARLANCES ARE INVOLVED , 1947 .
[7] Carsten Binnig,et al. Controlling False Discoveries During Interactive Data Exploration , 2016, SIGMOD Conference.
[8] Neoklis Polyzotis,et al. Data Management Challenges in Production Machine Learning , 2017, SIGMOD Conference.
[9] Reid A. Johnson,et al. Calibrating Probability with Undersampling for Unbalanced Classification , 2015, 2015 IEEE Symposium Series on Computational Intelligence.
[10] Toniann Pitassi,et al. Fairness through awareness , 2011, ITCS '12.
[11] Dean P. Foster,et al. α‐investing: a procedure for sequential control of expected false discoveries , 2008 .
[12] Jacob Cohen. Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.
[13] Wes McKinney,et al. pandas: a Foundational Python Library for Data Analysis and Statistics , 2011 .
[14] Neoklis Polyzotis,et al. Data Lifecycle Challenges in Production Machine Learning , 2018, SIGMOD Rec..
[15] Y. Benjamini,et al. Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .
[16] Gail M. Sullivan,et al. Using Effect Size-or Why the P Value Is Not Enough. , 2012, Journal of graduate medical education.
[17] Tim Kraska,et al. Slice Finder: Automated Data Slicing for Model Interpretability , 2017 .
[18] Carlos Guestrin,et al. "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.
[19] Nathan Srebro,et al. Equality of Opportunity in Supervised Learning , 2016, NIPS.
[20] Carlos Eduardo Scheidegger,et al. Certifying and Removing Disparate Impact , 2014, KDD.
[21] Vipin Kumar,et al. Parallel Formulations of Decision-Tree Classification Algorithms , 2004, Data Mining and Knowledge Discovery.
[22] Ramakrishnan Srikant,et al. Fast algorithms for mining association rules , 1998, VLDB 1998.
[23] Jure Leskovec,et al. Interpretable & Explorable Approximations of Black Box Models , 2017, ArXiv.
[24] Rachel K. E. Bellamy,et al. AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias , 2018, ArXiv.
[25] Tim Kraska,et al. Slice Finder: Automated Data Slicing for Model Validation , 2018, 2019 IEEE 35th International Conference on Data Engineering (ICDE).
[26] Carlos Guestrin,et al. Anchors: High-Precision Model-Agnostic Explanations , 2018, AAAI.
[27] Anna Rumshisky,et al. Unfolding physiological state: mortality modelling in intensive care units , 2014, KDD.
[28] Krishna P. Gummadi,et al. Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment , 2016, WWW.
[29] Percy Liang,et al. Understanding Black-box Predictions via Influence Functions , 2017, ICML.
[30] Aditya G. Parameswaran,et al. Interactive data exploration with smart drill-down , 2014, 2016 IEEE 32nd International Conference on Data Engineering (ICDE).
[31] Krishna P. Gummadi,et al. Fairness Constraints: Mechanisms for Fair Classification , 2015, AISTATS.
[32] Jon M. Kleinberg,et al. Inherent Trade-Offs in the Fair Determination of Risk Scores , 2016, ITCS.
[33] Enrico Bertini,et al. Interpreting Black-Box Classifiers Using Instance-Level Visual Explanations , 2017, HILDA@SIGMOD.
[34] Venkatesan Guruswami,et al. Combinatorial feature selection problems , 2000, Proceedings 41st Annual Symposium on Foundations of Computer Science.
[35] Minsuk Kahng,et al. Visual exploration of machine learning results using data cube analysis , 2016, HILDA '16.
[36] Ron Kohavi,et al. Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid , 1996, KDD.
[37] Isabelle Guyon,et al. An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..
[38] Osbert Bastani,et al. Interpreting Blackbox Models via Model Extraction , 2017, ArXiv.
[39] Martin Wattenberg,et al. Ad click prediction: a view from the trenches , 2013, KDD.
[40] Xin Zhang,et al. TFX: A TensorFlow-Based Production-Scale Machine Learning Platform , 2017, KDD.
[41] Been Kim,et al. Towards A Rigorous Science of Interpretable Machine Learning , 2017, 1702.08608.