Machine Learning Articles

Collection of articles for the Machine Learning Journal Club.

Overview

• Domingos P (2012) "A Few Useful Things to Know About Machine Learning" Commun. ACM 55, 10 (October 2012), 78-87. DOI: https://doi.org/10.1145/2347736.2347755 web: https://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf

• Beam AL, Kohane IS (2018) "Big Data and Machine Learning in Health Care" JAMA; 319(13): 1317-1318. DOI: 10.1001/jama.2017.18391 http://jamanetwork.com/article.aspx?doi=10.1001/jama.2017.18391

• Mehta P, Bukov M, Wang CH, Day AGR, Richardson C, Fisher CK, Schwab DJ (2018) "A high-bias, low-variance introduction to machine learning for physicists" arXiv: https://arxiv.org/abs/1803.08823

• Zitnik M, Nguyen F, Wang B, Leskovec J, Goldenberg A, Hoffman MM (2018) "Machine learning for integrating data in biology and medicine: Principles, practive, and opportunities" arXiv: https://arxiv.org/abs/1807.00123

Algorithms

• Geurts P, Ernst D, Wehenkel L (2006) "Extremely randomized trees" Machine Learning 63(1). 3-42. https://doi.org/10.1007/s10994-006-6226-1

• Breiman L (2001) "Random Forests" Machine Learning 45(1). 5–32. https://doi.org/10.1023/A:1010933404324

• Breiman L (1996) "Bagging predictors" Machine Learning 26(2), 123–140. https://doi.org/10.1007/BF00058655

• Friedman JH (1991) "Multivariate Adaptive Regression Splines" The Annals of Statistics 19(1), 1-67. http://www.jstor.org/stable/2241837

• Wolpert DH (1992) "Stacked generalization" Neural Networks 5(2), 241-259. https://doi.org/10.1016/S0893-6080(05)80023-1

• Yoav F, Schapire RE (1997) "A decision-theoretic generalization of on-line learning and an application to boosting" Journal of Computer and System Sciences, 55(1):119–139. https://doi.org/10.1006/jcss.1997.1504

• Friedman JH (1997) "On Bias, Variance, 0/1-Loss, and the Curse-of-Dimensionality" Data Mining and Knowledge Discovery 1: 55-77. https://doi.org/10.1023/A:1009778005914

• Friedman JH, Popescu BE (2008) "Predictive Learning via Rule Ensembles" The Annals of Applied Statistics. 2(3), 916–54. https://projecteuclid.org/euclid.aoas/1223908046

• Ruczinski I, Kooperberg C, LeBlanc ML (2003) "Logic Regression" Journal of Computational and Graphical Statistics, 12(3), 475-511. https://doi.org/10.1198/1061860032238

• Krstajic D, Buturovic LJ, Leahy DE, Thomas S (2014) "Cross-validation pitfalls when selecting and assessing regression and classification models" Journal of Cheminformatics, 6(10). https://doi.org/10.1186/1758-2946-6-10

Applications

• Prasad N, Cheng L-F, Chivers C, Draugelis M, Engelhardt BE (2017) "A Reinforcement Learning Approach to Weaning of Mechanical Ventilation in Intensive Care Units" arXiv: https://arxiv.org/abs/1704.06300

• Letham B, Rudin C, McCormick TH, Madigan D (2015) "Interpretable Classifiers using Rules and Bayesian Analysis: Building a Better Stroke Prediction Model", The Annals of Applied Statistics, 9(3): 1350-1371. https://projecteuclid.org/euclid.aoas/1446488742

Imbalanced Data

• He H, Bai Y, Garcia EA, Li S (2008) "ADASYN: Adaptive Synthetic Sampling Approach for Imbalanced Learning" 2008 IEEE International Joint Conference on Neural Networks, 1322-132.: doi: 10.1109/IJCNN.2008.4633969 https://ieeexplore.ieee.org/document/4633969/

Interpretability

• Goldstein A, Kapelner A, Bleich J, Pitkin E (2015) "Peeking Inside the Black Box: Visualizing Statistical Learning with Plots of Individual Conditional Expectation" Journal of Computational and Graphical Statistics 24(1), 44–65. https://doi.org/10.1080/10618600.2014.907095

• Tulio Ribeiro M, Singh S, Guestrin C (2016) "Why Should I Trust You? Explaining the Prediction of Any Classifier" arXiv: https://arxiv.org/abs/1602.04938

• Lipton ZC (2017) "The Mythos of Model Interpretability" arXiv: https://arxiv.org/abs/1606.03490

• Hall P, Phan W, and Satish Ambati S (2017). Ideas on interpreting machine learning. O’Reilly Ideas, https://www.oreilly.com/ideas/ideas-on-interpreting-machine-learning

• Lei J, G'Sell M, Rinaldo A, Tibshirani RJ, Wasserman L (2017) Distribution-Free Predictive Inference for Regression, arXiv, https://arxiv.org/abs/1604.04173

• Fisher A, Rudin C, Dominici F (2018) "Model Class Reliance: Variable Importance Measures for any Machine Learning Model Class, from the 'Rashomon' Perspective" arXiv: https://arxiv.org/abs/1801.01489

• Molnar, C (2018) "Interpretable Machine Learning": https://christophm.github.io/interpretable-ml-book/

• Doshi-Velez F, Kim B (2017) "Towards a rigorous science of interpretable machine learning" arXiv: https://arxiv.org/abs/1702.08608

• Shrikumar A, Greenside P, Kundaje A (2017) "Learning Important Features Through Propagating Activation Differences" arXiv: https://arxiv.org/abs/1704.02685

• Lundberg SM, Lee S-I (2017) "A Unified Approach to Interpreting Model Predictions" http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions

Automatic Machine Learning

• Olson RS, Urbanowicz RJ, Andrews PC, Lavender NA, Kidd LC, Moore JH (2016) Automating biomedical data science through tree-based pipeline optimization. Applications of Evolutionary Computation, pages 123-137. http://dx.doi.org/10.1007/978-3-319-31204-0_9

Optimization

• Bottou L, Curtis FE, Nocedal J (2018) Optimization methods for large-scale machine learning. arXiv: https://arxiv.org/abs/1606.04838

Reinforcement learning

• Gotttesman O, Johansson F, Komorowski M, Faisal A, Sontag D, Doshi-Velez F, Celi L (2019) "Guidelines for reinforcement learning in healthcase" https://www.nature.com/articles/s41591-018-0310-5

Software

• Abadi M, Agarwal A, et al. (2015) TensorFlow: Large-scale machine learning on heterogeneous systems, https://www.tensorflow.org/about/bib

History

• Donoho D (2017) 50 Years of Data Science, Journal of Computational and Graphical Statistics, 26(4) 745-766. https://doi.org/10.1080/10618600.2017.1384734

• Stone M (1974) Cross-Validatory Choice and Assessment of Statistical Predictions with discussion, Journal of the Royal Statistical Society. Series B, 36(2), 111-147. http://www.jstor.org/stable/2984809