PIE—Partially Interpretable Estimators with Refinement

References

• Adebayo J, Gilmer J, Muelly M, Goodfellow I, Hardt M, Kim B (2018) Sanity checks for saliency maps. Adv. Neural Inform. Processing Systems 31:9505–9515.Google Scholar
• Agarwal R, Frosst N, Zhang X, Caruana R, Hinton GE (2020) Neural additive models: Interpretable machine learning with neural nets. Preprint, submitted April 29, https://arxiv.org/abs/2004.13912.Google Scholar
• Aïvodji U, Arai H, Fortineau O, Gambs S, Hara S, Tapp A (2019) Fairwashing: The risk of rationalization. Preprint, submitted January 28, https://arxiv.org/abs/1901.09749.Google Scholar
• Caruana R, Lou Y, Gehrke J, Koch P, Sturm M, Elhadad N (2015) Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. Proc. 21st ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 1721–1730.Google Scholar
• Chen T, Guestrin C (2016) XGBoost: A scalable tree boosting system. Proc. 22nd ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 785–794.Google Scholar
• Coull BA, Ruppert D, Wand M (2001) Simple incorporation of interactions into additive models. Biometrics 57(2):539–545.Crossref, Google Scholar
• Dhurandhar A, Shanmugam K, Luss R (2020) Enhancing simple models by exploiting what they already know. Internat. Conf. Machine Learning (PMLR, New York), 2525–2534.Google Scholar
• Doshi-Velez F, Kim B (2017) Towards a rigorous science of interpretable machine learning. Preprint, submitted February 28, https://arxiv.org/abs/1702.08608.Google Scholar
• Friedman J, Hastie T, Tibshirani R (2000) Additive logistic regression: A statistical view of boosting (with discussion). Ann. Statist. 28(2):337–407.Crossref, Google Scholar
• Garcez A, Besold TR, De Raedt L, Földiak P, Hitzler P, Icard T, Kühnberger KU, Lamb LC, Miikkulainen R, Silver DL (2015) Neural-symbolic learning and reasoning: Contributions and challenges. Proc. AAAI Spring Sympos. Knowledge Representation Reasoning Integrating Symbolic Neural Approaches, Stanford.Google Scholar
• Gilpin LH, Bau D, Yuan BZ, Bajwa A, Specter M, Kagal L (2018) Explaining explanations: An overview of interpretability of machine learning. IEEE 5th Internat. Conf. Data Sci. Advanced Analytics (DSAA) (IEEE, Piscataway, NJ), 80–89.Google Scholar
• Grinsztajn L, Oyallon E, Varoquaux G (2022) Why do tree-based models still outperform deep learning on typical tabular data? Adv. Neural Inform. Processing Systems 35:507–520. Crossref, Google Scholar
• Guo M, Zhang Q, Liao X, Chen Y (2019) An interpretable machine learning framework for modeling human decision behavior. Preprint, submitted June 4, https://arxiv.org/abs/1906.01233.Google Scholar
• Hastie TJ, Tibshirani RJ (1990) Generalized Additive Models, vol. 43 (CRC Press, Boca Raton, FL).Google Scholar
• Higgins I, Matthey L, Pal A, Burgess C, Glorot X, Botvinick M, Mohamed S, Lerchner A (2017) beta-VAE: Learning basic visual concepts with a constrained variational framework. Internat. Conf. Learn. Representations (OpenReview.net).Google Scholar
• Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. Preprint, submitted March 9, https://arxiv.org/abs/1503.02531.Google Scholar
• Hua Z, Zhang B (2006) A hybrid support vector machines and logistic regression approach for forecasting intermittent demand of spare parts. Appl. Math. Comput. 181(2):1035–1048.Crossref, Google Scholar
• Johnson R, Zhang T (2013) Learning nonlinear functions using regularized greedy forest. IEEE Trans. Pattern Anal. Mach. Intell. 36(5):942–954.Crossref, Google Scholar
• Kelly M, Longjohn R, Nottingham K (2025) UCI machine learning repository. http://archive.ics.uci.edu/ml.Google Scholar
• Kohavi R (1996) Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid. Proc. 2nd Internat. Conf. Knowledge Discovery Data Mining (AAAI Press, Palo Alto, CA), 202–207.Google Scholar
• Lakkaraju H, Kamar E, Caruana R, Leskovec J (2017) Interpretable & explorable approximations of black box models. Preprint, submitted July 4, https://arxiv.org/abs/1707.01154.Google Scholar
• Leist AK, Klee M, Kim JH, Rehkopf DH, Bordas SP, Muniz-Terrera G, Wade S (2022) Mapping of machine learning approaches for description, prediction, and causal inference in the social and health sciences. Sci. Adv. 8(42):eabk1942.Crossref, Google Scholar
• Lou Y, Caruana R, Gehrke J, Hooker G (2013) Accurate intelligible models with pairwise interactions. Proc. 19th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 623–631.Google Scholar
• Lundberg SM, Lee SI (2017) A unified approach to interpreting model predictions. Proc. 31st Internat. Conf. Neural Inform. Processing Systems (Curran Associates Inc., Red Hook, NY), 4768–4777. Google Scholar
• Marx BD, Eilers PH (1998) Direct generalized additive modeling with penalized likelihood. Comput. Statist. Data Anal. 28(2):193–209.Crossref, Google Scholar
• Nesvijevskaia A, Ouillade S, Guilmin P, Zucker JD (2021) The accuracy versus interpretability trade-off in fraud detection model. Data Policy 3:e12.Crossref, Google Scholar
• Neumeier M, Botsch M, Tollkühn A, Berberich T (2021) Variational autoencoder-based vehicle trajectory prediction with an interpretable latent space. IEEE Internat. Intelligent Transportation Systems Conf. (ITSC) (IEEE, Piscataway, NJ), 820–827.Google Scholar
• Pan SJ, Yang Q (2009) A survey on transfer learning. IEEE Trans. Knowledge Data Engrg. 22(10):1345–1359.Crossref, Google Scholar
• Pan D, Wang T, Hara S (2020) Interpretable companions for black-box models. Internat. Conf. Artificial Intelligence Statistics (PMLR, New York), 2444–2454.Google Scholar
• Parikh N, Boyd S (2014) Proximal algorithms. Foundations Trends Optim. 1(3):127–239.Crossref, Google Scholar
• Parliament of the European Union Council (2016) General Data Protection Regulation. https://gdpr-info.eu/.Google Scholar
• Phillips M (2018) International data-sharing norms: From the OECD to the general data protection regulation (GDPR). Human Genetics 137(8):575–582.Crossref, Google Scholar
• Pintelas E, Livieris IE, Pintelas P (2020) A grey-box ensemble model exploiting black-box accuracy and white-box intrinsic interpretability. Algorithms 13(1):17.Crossref, Google Scholar
• Ragodos R, Wang T, Feng L (2024) From model explanation to data misinterpretation: Uncovering the pitfalls of post hoc explainers in business research. Preprint, submitted August 30, https://arxiv.org/pdf/2408.16987?Google Scholar
• Ramabathiran AA, Ramachandran P (2021) SPINN: Sparse, physics-based, and partially interpretable neural networks for PDEs. J. Comput. Phys. 445:110600.Crossref, Google Scholar
• Ribeiro MT, Singh S, Guestrin C (2016) Why should I trust you?: Explaining the predictions of any classifier. Proc. 22nd ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 1135–1144.Google Scholar
• Richards J, Huser R (2022) A unifying partially-interpretable framework for neural network-based extreme quantile regression. Preprint, submitted August 16, https://arxiv.org/abs/2208.07581.Google Scholar
• Richter MM, Weber RO (2013) Case-Based Reasoning (Springer, Berlin, Heidelberg).Crossref, Google Scholar
• Ross AS, Hughes MC, Doshi-Velez F (2017) Right for the right reasons: Training differentiable models by constraining their explanations. Preprint, submitted March 10, https://arxiv.org/abs/1703.03717.Google Scholar
• Rudin C (2019) Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1(5):206–215.Crossref, Google Scholar
• Samek W, Wiegand T, Müller KR (2017) Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. Preprint, submitted August 28, https://arxiv.org/abs/1708.08296.Google Scholar
• Shwartz-Ziv R, Armon A (2022) Tabular data: Deep learning is not all you need. Inform. Fusion 81:84–90.Crossref, Google Scholar
• Simonyan K, Vedaldi A, Zisserman A (2013) Deep inside convolutional networks: Visualising image classification models and saliency maps. Preprint, submitted December 20, https://arxiv.org/abs/1312.6034.Google Scholar
• Slack D, Hilgard S, Jia E, Singh S, Lakkaraju H (2019) Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. Preprint, submitted November 6, https://arxiv.org/abs/1911.02508.Google Scholar
• Wachter S, Mittelstadt B, Russell C (2017) Counterfactual explanations without opening the black box: Automated decisions and the GDPR. Harvard J. Law Tech. 31(2):841–887.Google Scholar
• Wang T, Lin Q (2021) Hybrid predictive models: When an interpretable model collaborates with a black-box model. J. Machine Learn. Res. 22(137):1–38.Google Scholar
• Wang B, Wu Y, Ye C (2023) The art of transfer learning: An adaptive and robust pipeline. Stat 12(1):e582.Crossref, Google Scholar
• Wang T, Yang J, Li Y, Wang B (2025) PIE - Partially interpretable estimators with refinement. https://doi.org/10.1287/ijoc.2022.0098.cd, https://github.com/INFORMSJoC/2022.0098.Google Scholar
• Wang T, Rudin C, Doshi-Velez F, Liu Y, Klampfl E, MacNeille P (2017) A Bayesian framework for learning rule sets for interpretable classification. J. Machine Learn. Res. 18(1):2357–2393.Google Scholar
• Wu J, Tenenbaum JB, Kohli P (2017) Neural scene de-rendering. Proc. IEEE Conf. Comput. Vision Pattern Recognition (Honolulu), 699–707.Google Scholar
• Yuan M, Lin Y (2006) Model selection and estimation in regression with grouped variables. J. Roy. Statist. Soc.: Ser. B (Stat. Methodol.) 68(1):49–67.Crossref, Google Scholar
• Zeng J, Ustun B, Rudin C (2017) Interpretable classification models for recidivism prediction. J. Roy. Statist. Soc.: Ser. A (Statist. Soc.) 180(3):689–722.Crossref, Google Scholar