References

Aas, Kjersti, Martin Jullum, and Anders Løland. 2021. “Explaining Individual Predictions When Features Are Dependent: More Accurate Approximations to Shapley Values.” Artificial Intelligence 298 (September): 103502. https://doi.org/10.1016/j.artint.2021.103502.
Adadi, Amina, and Mohammed Berrada. 2018. “Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI).” IEEE Access 6: 52138–60. https://doi.org/10.1109/ACCESS.2018.2870052.
Adebayo, Julius, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim. 2018. “Sanity Checks for Saliency Maps.” Advances in Neural Information Processing Systems 31.
Alemohammad, Sina, Josue Casco-Rodriguez, Lorenzo Luzi, Ahmed Imtiaz Humayun, Hossein Babaei, Daniel LeJeune, Ali Siahkoohi, and Richard G Baraniuk. 2023. “Self-Consuming Generative Models Go Mad.” arXiv Preprint arXiv:2307.01850.
Alimohamadi, Yousef, Mojtaba Sepandi, Maryam Taghdir, and Hadiseh Hosamirudsari. 2020. “Determine the Most Common Clinical Symptoms in COVID-19 Patients: A Systematic Review and Meta-Analysis.” Journal of Preventive Medicine and Hygiene 61 (3): E304.
“AlphaFold DB Website.” 2023. https://alphafold.ebi.ac.uk/.
Antoniou, Antreas, Amos Storkey, and Harrison Edwards. 2017. “Data Augmentation Generative Adversarial Networks.” arXiv Preprint arXiv:1711.04340.
Apley, Daniel W., and Jingyu Zhu. 2020. “Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models.” Journal of the Royal Statistical Society Series B: Statistical Methodology 82 (4): 1059–86. https://doi.org/10.1111/rssb.12377.
Arjovsky, Martin, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz. 2019. “Invariant Risk Minimization.” arXiv Preprint arXiv:1907.02893.
Athey, Susan, Julie Tibshirani, and Stefan Wager. 2019. “Generalized Random Forests.”
Ayoub, Fares, Toshiro Sato, and Atsushi Sakuraba. 2021. “Football and COVID-19 Risk: Correlation Is Not Causation.” Clinical Microbiology and Infection 27 (2): 291–92.
Bach, Victor Chernozhukov, Malte S. Kurz, and Martin Spindler. 2022. DoubleMLAn Object-Oriented Implementation of Double Machine Learning in Python.” Journal of Machine Learning Research 23 (53): 1–6. http://jmlr.org/papers/v23/21-0862.html.
Bach, V. Chernozhukov, M. S. Kurz, and M. Spindler. 2021. DoubleMLAn Object-Oriented Implementation of Double Machine Learning in R.” https://arxiv.org/abs/2103.09603.
Balestriero, Randall, Jerome Pesenti, and Yann LeCun. 2021. “Learning in High Dimension Always Amounts to Extrapolation.” arXiv Preprint arXiv:2110.09485.
Barnard, Etienne, and LFA Wessels. 1992. “Extrapolation and Interpolation in Neural Network Classifiers.” IEEE Control Systems Magazine 12 (5): 50–53.
Bartlett, Peter L., Philip M. Long, Gábor Lugosi, and Alexander Tsigler. 2020. “Benign Overfitting in Linear Regression.” Proceedings of the National Academy of Sciences 117 (48): 30063–70. https://doi.org/10.1073/pnas.1907378117.
Basso, Bruno, and Lin Liu. 2019. “Seasonal Crop Yield Forecast: Methods, Applications, and Accuracies.” In Advances in Agronomy, 154:201–55. Elsevier. https://doi.org/10.1016/bs.agron.2018.11.002.
Bates, Stephen, Trevor Hastie, and Robert Tibshirani. 2023. “Cross-Validation: What Does It Estimate and How Well Does It Do It?” Journal of the American Statistical Association, 1–12.
Battaglia, Peter W, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, et al. 2018. “Relational Inductive Biases, Deep Learning, and Graph Networks.” arXiv Preprint arXiv:1806.01261.
Beckers, Sander, and Joseph Y Halpern. 2019. “Abstracting Causal Models.” In Proceedings of the Aaai Conference on Artificial Intelligence, 33:2678–85. 01.
Beckers, Sander, and Joost Vennekens. 2018. “A Principled Approach to Defining Actual Causation.” Synthese 195 (2): 835–62.
Belkin, Mikhail. 2021. “Fit Without Fear: Remarkable Mathematical Phenomena of Deep Learning Through the Prism of Interpolation.” Acta Numerica 30: 203–48.
Belkin, Mikhail, Daniel Hsu, Siyuan Ma, and Soumik Mandal. 2019. “Reconciling Modern Machine-Learning Practice and the Classical Bias–Variance Trade-Off.” Proceedings of the National Academy of Sciences of the United States of America 116 (32): 15849–54. https://doi.org/10.1073/pnas.1903070116.
Bengio, Yoshua, Aaron Courville, and Pascal Vincent. 2013. “Representation Learning: A Review and New Perspectives.” IEEE Transactions on Pattern Analysis and Machine Intelligence 35 (8): 1798–828.
Bickler, Simon H. 2021. “Machine Learning Arrives in Archaeology.” Advances in Archaeological Practice 9 (2): 186–91.
Bird, Steven. 2006. “NLTK: The Natural Language Toolkit.” In Proceedings of the COLING/ACL 2006 Interactive Presentation Sessions, 69–72.
Bishop, Christopher M, and Nasser M Nasrabadi. 2006. Pattern Recognition and Machine Learning. Vol. 4. 4. Springer.
Bloice, Marcus D, Christof Stocker, and Andreas Holzinger. 2017. “Augmentor: An Image Augmentation Library for Machine Learning.” arXiv Preprint arXiv:1708.04680.
Breiman, Leo. 2001. “Random Forests.” Machine Learning 45 (1): 5–32. https://doi.org/10.1023/A:1010933404324.
———. 2017. Classification and Regression Trees. New York: Routledge. https://doi.org/10.1201/9781315139470.
Chakravartty, Anjan. 2017. Scientific Realism.” In The Stanford Encyclopedia of Philosophy, edited by Edward N. Zalta, Summer 2017. https://plato.stanford.edu/archives/sum2017/entries/scientific-realism/; Metaphysics Research Lab, Stanford University.
Chalapathy, Raghavendra, and Sanjay Chawla. 2019. “Deep Learning for Anomaly Detection: A Survey.” arXiv Preprint arXiv:1901.03407.
Chandrashekar, Girish, and Ferat Sahin. 2014. “A Survey on Feature Selection Methods.” Computers & Electrical Engineering 40 (1): 16–28.
Chasalow, Kyla, and Karen Levy. 2021. “Representativeness in Statistics, Politics, and Machine Learning.” In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 77–89. FAccT ’21. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3442188.3445872.
Chattopadhyay, Prithvijit, Ramakrishna Vedantam, Ramprasaath R Selvaraju, Dhruv Batra, and Devi Parikh. 2017. “Counting Everyday Objects in Everyday Scenes.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1135–44.
Chernozhukov, Victor, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, and James Robins. 2018. “Double/Debiased Machine Learning for Treatment and Structural Parameters.” Oxford University Press Oxford, UK.
Chomsky, Noam, Ian Roberts, and Jeffrey Watumull. 2023. “Noam Chomsky: The False Promise of ChatGPT.” The New York Times 8.
Ciravegna, Gabriele, Frédéric Precioso, Alessandro Betti, Kevin Mottin, and Marco Gori. 2023. “Knowledge-Driven Active Learning.” In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 38–54. Springer.
Clemmensen, Line H., and Rune D. Kjærsgaard. 2023. “Data Representativity for Machine Learning and AI Systems.” arXiv. http://arxiv.org/abs/2203.04706.
Corso, Gabriele, Hannes Stark, Stefanie Jegelka, Tommi Jaakkola, and Regina Barzilay. 2024. “Graph Neural Networks.” Nature Reviews Methods Primers 4 (1): 17.
Covert, Ian C., Scott Lundberg, and Su-In Lee. 2020. “Understanding Global Feature Contributions with Additive Importance Measures.” In Proceedings of the 34th International Conference on Neural Information Processing Systems, 17212–23. NIPS’20. Red Hook, NY, USA: Curran Associates Inc.
Cranmer, Kyle, Johann Brehmer, and Gilles Louppe. 2020. “The Frontier of Simulation-Based Inference.” Proceedings of the National Academy of Sciences 117 (48): 30055–62.
Cybenko, George. 1989. “Approximation by Superpositions of a Sigmoidal Function.” Mathematics of Control, Signals and Systems 2 (4): 303–14.
D’Ecclesiis, Oriana, Costanza Gavioli, Chiara Martinoli, Sara Raimondi, Susanna Chiocca, Claudia Miccolo, Paolo Bossi, et al. 2022. “Vitamin d and SARS-CoV2 Infection, Severity and Mortality: A Systematic Review and Meta-Analysis.” PLoS One 17 (7): e0268396.
Dandl, Susanne. 2023. “Causality Concepts in Machine Learning: Heterogeneous Treatment Effect Estimation with Machine Learning & Model Interpretation with Counterfactual and Semi-Factual Explanations.” PhD thesis, lmu.
Danka, Tivadar, and Peter Horvath. 2018. “modAL: A Modular Active Learning Framework for Python.” arXiv Preprint arXiv:1805.00979.
De Regt, Henk W. 2020. “Understanding, Values, and the Aims of Science.” Philosophy of Science 87 (5): 921–32.
De Sarkar, Sohan, Fan Yang, and Arjun Mukherjee. 2018. “Attending Sentences to Detect Satirical Fake News.” In Proceedings of the 27th International Conference on Computational Linguistics, 3371–80.
Dennis, Brian, Jose Miguel Ponciano, Mark L Taper, and Subhash R Lele. 2019. “Errors in Statistical Inference Under Model Misspecification: Evidence, Hypothesis Testing, and AIC.” Frontiers in Ecology and Evolution 7: 372.
Denouden, Taylor, Rick Salay, Krzysztof Czarnecki, Vahdat Abdelzad, Buu Phan, and Sachin Vernekar. 2018. “Improving Reconstruction Autoencoder Out-of-Distribution Detection with Mahalanobis Distance.” arXiv Preprint arXiv:1812.02765.
Doshi-Velez, Finale, and Been Kim. 2017. “Towards A Rigorous Science of Interpretable Machine Learning.” arXiv. http://arxiv.org/abs/1702.08608.
Douglas, Heather E. 2009. “Reintroducing Prediction to Explanation.” Philosophy of Science 76 (4): 444–63.
Earman, John. 1992. “Bayes or Bust?: A Critical Examination of Bayesian Confirmation Theory.”
Eberhardt, Frederick, Clark Glymour, and Richard Scheines. 2005. “On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations Among n Variables.” In Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence, 178–84. UAI’05. Arlington, Virginia, USA: AUAI Press.
Ellenberg, Jordan. 2014. How Not to Be Wrong: The Hidden Maths of Everyday Life. Penguin UK.
Erickson, Bradley J, Panagiotis Korfiatis, Zeynettin Akkus, and Timothy L Kline. 2017. “Machine Learning for Medical Imaging.” Radiographics 37 (2): 505–15.
Feng, Steven Y, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, and Eduard Hovy. 2021. “A Survey of Data Augmentation Approaches for NLP.” arXiv Preprint arXiv:2105.03075.
Fischer, Alain. 2020. “Resistance of Children to Covid-19. How?” Mucosal Immunology 13 (4): 563–65.
Fisher, Aaron, Cynthia Rudin, and Francesca Dominici. 2019. “All Models Are Wrong, but Many Are Useful: Learning a Variable’s Importance by Studying an Entire Class of Prediction Models Simultaneously.” Journal of Machine Learning Research : JMLR 20: 177. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8323609/.
Flora, Montgomery, Corey Potvin, Amy McGovern, and Shawn Handler. 2022. “Comparing Explanation Methods for Traditional Machine Learning Models Part 1: An Overview of Current Methods and Quantifying Their Disagreement.” arXiv. http://arxiv.org/abs/2211.08943.
Frankle, Jonathan, and Michael Carbin. 2019. “The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks.” arXiv. https://doi.org/10.48550/arXiv.1803.03635.
Freiesleben, Timo. 2022. “The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples.” Minds and Machines 32 (1): 77–109.
———. 2023. “Artificial Neural Nets and the Representation of Human Concepts.” arXiv Preprint arXiv:2312.05337.
Freiesleben, Timo, and Thomas Grote. 2023. “Beyond Generalization: A Theory of Robustness in Machine Learning.” Synthese 202 (4): 109.
Freiesleben, Timo, Gunnar König, Christoph Molnar, and Alvaro Tejero-Cantero. 2022. “Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena.” arXiv. http://arxiv.org/abs/2206.05487.
Friedman, Jerome H. 2001. “Greedy Function Approximation: A Gradient Boosting Machine.” The Annals of Statistics 29 (5): 1189–1232. https://doi.org/10.1214/aos/1013203451.
Friedman, Jerome H., and Bogdan E. Popescu. 2008. “Predictive Learning via Rule Ensembles.” The Annals of Applied Statistics 2 (3): 916–54. https://www.jstor.org/stable/30245114.
Frigg, Roman, and Stephan Hartmann. 2020. Models in Science.” In The Stanford Encyclopedia of Philosophy, edited by Edward N. Zalta, Spring 2020. https://plato.stanford.edu/archives/spr2020/entries/models-science/; Metaphysics Research Lab, Stanford University.
Fukumizu, Kenji, Arthur Gretton, Xiaohai Sun, and Bernhard Schölkopf. 2007. “Kernel Measures of Conditional Dependence.” Advances in Neural Information Processing Systems 20.
Gao, Yue, Guang-Yao Cai, Wei Fang, Hua-Yi Li, Si-Yuan Wang, Lingxi Chen, Yang Yu, et al. 2020. “Machine Learning Based Early Warning System Enables Accurate Mortality Risk Prediction for COVID-19.” Nature Communications 11 (1): 5033.
Ghai, Bhavya, Q Vera Liao, Yunfeng Zhang, Rachel Bellamy, and Klaus Mueller. 2021. “Explainable Active Learning (Xal) Toward Ai Explanations as Interfaces for Machine Teachers.” Proceedings of the ACM on Human-Computer Interaction 4 (CSCW3): 1–28.
Gibbons, Jason B, Edward C Norton, Jeffrey S McCullough, David O Meltzer, Jill Lavigne, Virginia C Fiedler, and Robert D Gibbons. 2022. “Association Between Vitamin d Supplementation and COVID-19 Infection and Mortality.” Scientific Reports 12 (1): 19397.
Goldstein, Alex, Adam Kapelner, Justin Bleich, and Emil Pitkin. 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.
Gong, Yue, and Guochang Zhao. 2022. “Wealth, Health, and Beyond: Is COVID-19 Less Likely to Spread in Rich Neighborhoods?” Plos One 17 (5): e0267487.
Goodfellow, Ian J, Jonathon Shlens, and Christian Szegedy. 2014. “Explaining and Harnessing Adversarial Examples.” arXiv Preprint arXiv:1412.6572.
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. MIT press.
Goodfellow, Ian, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. “Generative Adversarial Nets.” Advances in Neural Information Processing Systems 27.
Goschenhofer, Jann, Franz MJ Pfister, Kamer Ali Yuksel, Bernd Bischl, Urban Fietzek, and Janek Thomas. 2020. “Wearable-Based Parkinson’s Disease Severity Monitoring Using Deep Learning.” In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2019, würzburg, Germany, September 16–20, 2019, Proceedings, Part III, 400–415. Springer.
Gupta, Meghna, Caleigh M Azumaya, Michelle Moritz, Sergei Pourmal, Amy Diallo, Gregory E Merz, Gwendolyn Jang, et al. 2021. “CryoEM and AI Reveal a Structure of SARS-CoV-2 Nsp2, a Multifunctional Protein Involved in Key Host Processes.” Research Square.
Hacking, Ian. 1992. “‘Style’for Historians and Philosophers.” Studies in History and Philosophy of Science Part A 23 (1): 1–20.
Haley, Pamela J, and DONALD Soloway. 1992. “Extrapolation Limitations of Multilayer Feedforward Neural Networks.” In [Proceedings 1992] IJCNN International Joint Conference on Neural Networks, 4:25–30. IEEE.
Halmos, Paul R. 2013. Measure Theory. Vol. 18. Springer.
Han, Sicong, Chenhao Lin, Chao Shen, Qian Wang, and Xiaohong Guan. 2023. “Interpreting Adversarial Examples in Deep Learning: A Review.” ACM Computing Surveys 55 (14s): 1–38.
Hardt, Moritz, and Benjamin Recht. 2022. Patterns, Predictions, and Actions: Foundations of Machine Learning. Princeton University Press.
Hasson, Uri, Samuel A Nastase, and Ariel Goldstein. 2020. “Direct Fit to Nature: An Evolutionary Perspective on Biological and Artificial Neural Networks.” Neuron 105 (3): 416–34.
He, Yang-Hui. 2017. “Machine-Learning the String Landscape.” Physics Letters B 774: 564–68.
Hendrycks, Dan, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, et al. 2021. “The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization.” In Proceedings of the IEEE/CVF International Conference on Computer Vision, 8340–49.
Hitchcock, Christopher, and Miklós Rédei. 2021. Reichenbach’s Common Cause Principle.” In The Stanford Encyclopedia of Philosophy, edited by Edward N. Zalta, Summer 2021. https://plato.stanford.edu/archives/sum2021/entries/physics-Rpcc/; Metaphysics Research Lab, Stanford University.
Hofner, Benjamin, Andreas Mayr, Nikolay Robinzonov, and Matthias Schmid. 2014. “Model-Based Boosting in r: A Hands-on Tutorial Using the r Package Mboost.” Computational Statistics 29: 3–35.
Holland, Paul W. 1986. “Statistics and Causal Inference.” Journal of the American Statistical Association 81 (396): 945–60.
Hornik, Kurt. 1991. “Approximation Capabilities of Multilayer Feedforward Networks.” Neural Networks 4 (2): 251–57.
Howard, Jeremy, Austin Huang, Zhiyuan Li, Zeynep Tufekci, Vladimir Zdimal, Helene-Mari Van Der Westhuizen, Arne Von Delft, et al. 2021. “An Evidence Review of Face Masks Against COVID-19.” Proceedings of the National Academy of Sciences 118 (4): e2014564118.
Hu, Shaoping, Yuan Gao, Zhangming Niu, Yinghui Jiang, Lao Li, Xianglu Xiao, Minhao Wang, et al. 2020. “Weakly Supervised Deep Learning for Covid-19 Infection Detection and Classification from Ct Images.” IEEE Access 8: 118869–83.
Hu, Weihua, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. 2020. “Open Graph Benchmark: Datasets for Machine Learning on Graphs.” Advances in Neural Information Processing Systems 33: 22118–33.
Hutter, Frank, Lars Kotthoff, and Joaquin Vanschoren. 2019. Automated Machine Learning: Methods, Systems, Challenges. Springer Nature.
Ilyas, Andrew, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry. 2019. “Adversarial Examples Are Not Bugs, They Are Features.” Advances in Neural Information Processing Systems 32.
Isola, Phillip, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros. 2017. “Image-to-Image Translation with Conditional Adversarial Networks.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1125–34.
Jehi, Lara, Xinge Ji, Alex Milinovich, Serpil Erzurum, Brian P Rubin, Steve Gordon, James B Young, and Michael W Kattan. 2020. “Individualizing Risk Prediction for Positive Coronavirus Disease 2019 Testing: Results from 11,672 Patients.” Chest 158 (4): 1364–75.
Jumper, John, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, et al. 2021. “Highly Accurate Protein Structure Prediction with AlphaFold.” Nature 596 (7873): 583–89.
Kalisch, Markus, Martin Mächler, Diego Colombo, Marloes H Maathuis, and Peter Bühlmann. 2012. “Causal Inference Using Graphical Models with the r Package Pcalg.” Journal of Statistical Software 47: 1–26.
Kamath, Pritish, Akilesh Tangella, Danica Sutherland, and Nathan Srebro. 2021. “Does Invariant Risk Minimization Capture Invariance?” In International Conference on Artificial Intelligence and Statistics, 4069–77. PMLR.
Kell, Alexander JE, Daniel LK Yamins, Erica N Shook, Sam V Norman-Haignere, and Josh H McDermott. 2018. “A Task-Optimized Neural Network Replicates Human Auditory Behavior, Predicts Brain Responses, and Reveals a Cortical Processing Hierarchy.” Neuron 98 (3): 630–44.
Kim, Jongpil, and Vladimir Pavlovic. 2014. “Ancient Coin Recognition Based on Spatial Coding.” In 2014 22nd International Conference on Pattern Recognition, 321–26. https://doi.org/10.1109/ICPR.2014.64.
Knaus, Michael C. 2022. “Double Machine Learning-Based Programme Evaluation Under Unconfoundedness.” The Econometrics Journal 25 (3): 602–27. https://doi.org/10.1093/ectj/utac015.
Koh, Pang Wei, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, and Percy Liang. 2020. “Concept Bottleneck Models.” In International Conference on Machine Learning, 5338–48. PMLR.
König, Gunnar, Timo Freiesleben, and Moritz Grosse-Wentrup. 2023. “Improvement-Focused Causal Recourse (ICR).” In Proceedings of the AAAI Conference on Artificial Intelligence, 37:11847–55. 10.
Künzel, Sören R, Jasjeet S Sekhon, Peter J Bickel, and Bin Yu. 2019. “Metalearners for Estimating Heterogeneous Treatment Effects Using Machine Learning.” Proceedings of the National Academy of Sciences 116 (10): 4156–65.
Lagerquist, Ryan, Amy McGovern, Cameron R Homeyer, David John Gagne II, and Travis Smith. 2020. “Deep Learning on Three-Dimensional Multiscale Data for Next-Hour Tornado Prediction.” Monthly Weather Review 148 (7): 2837–61.
Lam, Remi, Alvaro Sanchez-Gonzalez, Matthew Willson, Peter Wirnsberger, Meire Fortunato, Ferran Alet, Suman Ravuri, et al. 2023. “Learning Skillful Medium-Range Global Weather Forecasting.” Science, eadi2336.
Lei, Jing, Max G’Sell, Alessandro Rinaldo, Ryan J. Tibshirani, and Larry Wasserman. 2018. “Distribution-Free Predictive Inference for Regression.” Journal of the American Statistical Association 113 (523): 1094–1111. https://doi.org/10.1080/01621459.2017.1307116.
Lei, Lihua, and Emmanuel J Candès. 2021. “Conformal Inference of Counterfactuals and Individual Treatment Effects.” Journal of the Royal Statistical Society Series B: Statistical Methodology 83 (5): 911–38.
Letzgus, Simon, and Klaus-Robert Müller. 2023. “An Explainable AI Framework for Robust and Transparent Data-Driven Wind Turbine Power Curve Models.” Energy and AI, December, 100328. https://doi.org/10.1016/j.egyai.2023.100328.
Lipton, Zachary C. 2017. “The Mythos of Model Interpretability.” arXiv. http://arxiv.org/abs/1606.03490.
London, Ian. 2016. “Encoding Cyclical Continuous Features - 24-Hour Time.” Ian London’s Blog. //ianlondon.github.io/blog/encoding-cyclical-features-24hour-time/.
Lu, Yulong, and Jianfeng Lu. 2020. “A Universal Approximation Theorem of Deep Neural Networks for Expressing Probability Distributions.” Advances in Neural Information Processing Systems 33: 3094–3105.
Luan, Hui, and Chin-Chung Tsai. 2021. “A Review of Using Machine Learning Approaches for Precision Education.” Educational Technology & Society 24 (1): 250–66.
Lucas, Tim CD. 2020. “A Translucent Box: Interpretable Machine Learning in Ecology.” Ecological Monographs 90 (4): e01422.
Lundberg, Scott M., and Su-In Lee. 2017. “A Unified Approach to Interpreting Model Predictions.” In Proceedings of the 31st International Conference on Neural Information Processing Systems, 4768–77. NIPS’17. Red Hook, NY, USA: Curran Associates Inc.
Maaz, Kai, Cordula Artelt, Pia Brugger, Sandra Buchholz, Stefan Kühne, Holger Leerhoff, Thomas Rauschenbach, Josef Schrader, and Susan Seeber. 2022. Bildung in Deutschland 2022: Ein Indikatorengestützter Bericht Mit Einer Analyse Zum Bildungspersonal. wbv Publikation.
Manski, Charles F. 2003. Partial Identification of Probability Distributions. Vol. 5. Springer.
Marcus, Gary. 2020. “The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence.” arXiv Preprint arXiv:2002.06177.
Maurage, Pierre, Alexandre Heeren, and Mauro Pesenti. 2013. “Does Chocolate Consumption Really Boost Nobel Award Chances? The Peril of over-Interpreting Correlations in Health Studies.” The Journal of Nutrition 143 (6): 931–33.
McDermott, Matthew BA, Shirly Wang, Nikki Marinsek, Rajesh Ranganath, Luca Foschini, and Marzyeh Ghassemi. 2021. “Reproducibility in Machine Learning for Health Research: Still a Ways to Go.” Science Translational Medicine 13 (586): eabb1655.
Miller, Tim. 2019. “Explanation in Artificial Intelligence: Insights from the Social Sciences.” Artificial Intelligence 267 (February): 1–38. https://doi.org/10.1016/j.artint.2018.07.007.
Molnar, Christoph. 2022. Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. 2nd ed. https://christophm.github.io/interpretable-ml-book.
Molnar, Christoph, Giuseppe Casalicchio, and Bernd Bischl. 2020. “Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc Interpretability.” In Machine Learning and Knowledge Discovery in Databases: International Workshops of ECML PKDD 2019, würzburg, Germany, September 16–20, 2019, Proceedings, Part i, 193–204. Springer.
Molnar, Christoph, Timo Freiesleben, Gunnar König, Julia Herbinger, Tim Reisinger, Giuseppe Casalicchio, Marvin N. Wright, and Bernd Bischl. 2023. “Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process.” In Explainable Artificial Intelligence, edited by Luca Longo, 456–79. Communications in Computer and Information Science. Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-44064-9_24.
Molnar, Christoph, Gunnar König, Bernd Bischl, and Giuseppe Casalicchio. 2023. “Model-Agnostic Feature Importance and Effects with Dependent FeaturesA Conditional Subgroup Approach.” Data Mining and Knowledge Discovery, January. https://doi.org/10.1007/s10618-022-00901-9.
Mumuni, Alhassan, and Fuseini Mumuni. 2021. “CNN Architectures for Geometric Transformation-Invariant Feature Representation in Computer Vision: A Review.” SN Computer Science 2 (5): 340.
———. 2022. “Data Augmentation: A Comprehensive Survey of Modern Approaches.” Array 16: 100258.
Neal, Brady. 2020. “Introduction to Causal Inference.” Course Lecture Notes (Draft).
Neethu, MS, and R Rajasree. 2013. “Sentiment Analysis in Twitter Using Machine Learning Techniques.” In 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), 1–5. IEEE.
Neupert, Titus, Mark H Fischer, Eliska Greplova, Kenny Choo, and M Michael Denner. 2021. “Introduction to Machine Learning for the Sciences.” arXiv Preprint arXiv:2102.04883.
Nguyen, An-phi, and Marı́a Rodrı́guez Martı́nez. 2019. MonoNet: Towards Interpretable Models by Learning Monotonic Features.” arXiv. https://doi.org/10.48550/arXiv.1909.13611.
Norouzzadeh, Mohammad Sadegh, Anh Nguyen, Margaret Kosmala, Alexandra Swanson, Meredith S Palmer, Craig Packer, and Jeff Clune. 2018. “Automatically Identifying, Counting, and Describing Wild Animals in Camera-Trap Images with Deep Learning.” Proceedings of the National Academy of Sciences 115 (25): E5716–25.
Oikarinen, Tuomas, Karthik Srinivasan, Olivia Meisner, Julia B Hyman, Shivangi Parmar, Adrian Fanucci-Kiss, Robert Desimone, Rogier Landman, and Guoping Feng. 2019. “Deep Convolutional Network for Animal Sound Classification and Source Attribution Using Dual Audio Recordings.” The Journal of the Acoustical Society of America 145 (2): 654–62.
Papernot, Nicolas, Patrick McDaniel, and Ian Goodfellow. 2016. “Transferability in Machine Learning: From Phenomena to Black-Box Attacks Using Adversarial Samples.” arXiv Preprint arXiv:1605.07277.
Pearl, Judea. 2009. Causality. Cambridge university press.
———. 2019. “The Limitations of Opaque Learning Machines.” Possible Minds 25: 13–19.
Pearl, Judea, and Dana Mackenzie. 2018. The Book of Why: The New Science of Cause and Effect. Basic books.
Pedersen, Morten Axel. 2023. “Editorial Introduction: Towards a Machinic Anthropology.” Big Data & Society. SAGE Publications Sage UK: London, England.
Pereira, Joana, Adam J Simpkin, Marcus D Hartmann, Daniel J Rigden, Ronan M Keegan, and Andrei N Lupas. 2021. “High-Accuracy Protein Structure Prediction in CASP14.” Proteins: Structure, Function, and Bioinformatics 89 (12): 1687–99.
Perry, George LW, Rupert Seidl, André M Bellvé, and Werner Rammer. 2022. “An Outlook for Deep Learning in Ecosystem Science.” Ecosystems, 1–19.
Peters, Jonas, Dominik Janzing, and Bernhard Schölkopf. 2017. Elements of Causal Inference: Foundations and Learning Algorithms. The MIT Press.
Pfisterer, Florian, Stefan Coors, Janek Thomas, and Bernd Bischl. 2019. “Multi-Objective Automatic Machine Learning with Autoxgboostmc.” arXiv Preprint arXiv:1908.10796.
Popper, Karl. 2005. The Logic of Scientific Discovery. Routledge.
Pu, Zhaoxia, and Eugenia Kalnay. 2019. “Numerical Weather Prediction Basics: Models, Numerical Methods, and Data Assimilation.” Handbook of Hydrometeorological Ensemble Forecasting, 67–97.
Rajpurkar, Pranav, Jeremy Irvin, Kaylie Zhu, Brandon Yang, Hershel Mehta, Tony Duan, Daisy Ding, et al. 2017. CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning.” arXiv. https://doi.org/10.48550/arXiv.1711.05225.
Rebuffi, Sylvestre-Alvise, Sven Gowal, Dan Andrei Calian, Florian Stimberg, Olivia Wiles, and Timothy A Mann. 2021. “Data Augmentation Can Improve Robustness.” Advances in Neural Information Processing Systems 34: 29935–48.
Reichstein, Markus, Gustau Camps-Valls, Bjorn Stevens, Martin Jung, Joachim Denzler, Nuno Carvalhais, et al. 2019. “Deep Learning and Process Understanding for Data-Driven Earth System Science.” Nature 566 (7743): 195–204.
Ren, Pengzhen, Yun Xiao, Xiaojun Chang, Po-Yao Huang, Zhihui Li, Brij B Gupta, Xiaojiang Chen, and Xin Wang. 2021. “A Survey of Deep Active Learning.” ACM Computing Surveys (CSUR) 54 (9): 1–40.
Ren, Xiaoli, Xiaoyong Li, Kaijun Ren, Junqiang Song, Zichen Xu, Kefeng Deng, and Xiang Wang. 2021. “Deep Learning-Based Weather Prediction: A Survey.” Big Data Research 23: 100178.
Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. 2016. “"Why Should I Trust You?": Explaining the Predictions of Any Classifier.” In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–44. KDD ’16. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/2939672.2939778.
———. 2018. “Anchors: High-Precision Model-Agnostic Explanations.” Proceedings of the AAAI Conference on Artificial Intelligence 32 (1). https://doi.org/10.1609/aaai.v32i1.11491.
Roberts, Michael, Derek Driggs, Matthew Thorpe, Julian Gilbey, Michael Yeung, Stephan Ursprung, Angelica I Aviles-Rivero, et al. 2021. “Common Pitfalls and Recommendations for Using Machine Learning to Detect and Prognosticate for COVID-19 Using Chest Radiographs and CT Scans.” Nature Machine Intelligence 3 (3): 199–217.
Rocks, Jason W, and Pankaj Mehta. 2022. “Memorizing Without Overfitting: Bias, Variance, and Interpolation in Overparameterized Models.” Physical Review Research 4 (1): 013201.
Rothfuss, Jonas, Fabio Ferreira, Simon Walther, and Maxim Ulrich. 2019. “Conditional Density Estimation with Neural Networks: Best Practices and Benchmarks.” arXiv Preprint arXiv:1903.00954.
Rudin, Cynthia, Chaofan Chen, Zhi Chen, Haiyang Huang, Lesia Semenova, and Chudi Zhong. 2022. “Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges.” Statistics Surveys 16 (none): 1–85. https://doi.org/10.1214/21-SS133.
Ruff, Lukas, Jacob R Kauffmann, Robert A Vandermeulen, Grégoire Montavon, Wojciech Samek, Marius Kloft, Thomas G Dietterich, and Klaus-Robert Müller. 2021. “A Unifying Review of Deep and Shallow Anomaly Detection.” Proceedings of the IEEE 109 (5): 756–95.
Schaeffer, Rylan, Mikail Khona, Zachary Robertson, Akhilan Boopathy, Kateryna Pistunova, Jason W. Rocks, Ila Rani Fiete, and Oluwasanmi Koyejo. 2023. “Double Descent Demystified: Identifying, Interpreting & Ablating the Sources of a Deep Learning Puzzle.” arXiv. http://arxiv.org/abs/2303.14151.
Schmidt, Jonathan, Mário RG Marques, Silvana Botti, and Miguel AL Marques. 2019. “Recent Advances and Applications of Machine Learning in Solid-State Materials Science.” Npj Computational Materials 5 (1): 1–36.
Scholbeck, Christian A., Christoph Molnar, Christian Heumann, Bernd Bischl, and Giuseppe Casalicchio. 2020. “Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model-Agnostic Interpretations.” In Machine Learning and Knowledge Discovery in Databases, edited by Peggy Cellier and Kurt Driessens, 205–16. Communications in Computer and Information Science. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-43823-4_18.
Schölkopf, Bernhard. 2022. “Causality for Machine Learning.” In Probabilistic and Causal Inference: The Works of Judea Pearl, 765–804.
Schölkopf, Bernhard, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, and Joris Mooij. 2012. “On Causal and Anticausal Learning.” arXiv Preprint arXiv:1206.6471.
Schölkopf, Bernhard, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, and Yoshua Bengio. 2021. “Toward Causal Representation Learning.” Proceedings of the IEEE 109 (5): 612–34.
Sen, Rajat, Ananda Theertha Suresh, Karthikeyan Shanmugam, Alexandros G Dimakis, and Sanjay Shakkottai. 2017. “Model-Powered Conditional Independence Test.” Advances in Neural Information Processing Systems 30.
Settles, Burr. 2009. “Active Learning Literature Survey.” Computer Sciences Technical Report 1648. University of Wisconsin–Madison.
Shah, RAJEN D, and JONAS Peters. 2020. “THE HARDNESS OF CONDITIONAL INDEPENDENCE TESTING AND THE GENERALISED COVARIANCE MEASURE.” The Annals of Statistics 48 (3): 1514–38.
Shalev-Shwartz, Shai, and Shai Ben-David. 2014. Understanding Machine Learning: From Theory to Algorithms. Cambridge university press.
Sharma, Amit, and Emre Kiciman. 2020. “DoWhy: An End-to-End Library for Causal Inference.” arXiv Preprint arXiv:2011.04216.
Shmueli, Galit. 2010. To Explain or to Predict? Statistical Science 25 (3): 289–310. https://doi.org/10.1214/10-STS330.
Shorten, Connor, and Taghi M Khoshgoftaar. 2019. “A Survey on Image Data Augmentation for Deep Learning.” Journal of Big Data 6 (1): 1–48.
Smith, Samuel L., Benoit Dherin, David Barrett, and Soham De. 2020. “On the Origin of Implicit Regularization in Stochastic Gradient Descent.” In. https://openreview.net/forum?id=rq_Qr0c1Hyo&ref=inference.vc.
Spirtes, Peter, Clark N Glymour, and Richard Scheines. 2000. Causation, Prediction, and Search. MIT press.
Sterkenburg, Tom F, and Peter D Grünwald. 2021. “The No-Free-Lunch Theorems of Supervised Learning.” Synthese 199 (3): 9979–10015.
Štrumbelj, Erik, and Igor Kononenko. 2014. “Explaining Prediction Models and Individual Predictions with Feature Contributions.” Knowledge and Information Systems 41 (3): 647–65. https://doi.org/10.1007/s10115-013-0679-x.
Swanson, Alexandra, Margaret Kosmala, Chris Lintott, Robert Simpson, Arfon Smith, and Craig Packer. 2015. “Snapshot Serengeti, High-Frequency Annotated Camera Trap Images of 40 Mammalian Species in an African Savanna.” Scientific Data 2 (1): 1–14.
Szegedy, Christian, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. 2013. “Intriguing Properties of Neural Networks.” arXiv Preprint arXiv:1312.6199.
Taleb, Nassim. 2005. “The Black Swan: Why Don’t We Learn That We Don’t Learn.” NY: Random House 1145.
Tanay, Thomas, and Lewis Griffin. 2016. “A Boundary Tilting Persepective on the Phenomenon of Adversarial Examples.” arXiv Preprint arXiv:1608.07690.
Tang, Ying-Peng, Guo-Xiang Li, and Sheng-Jun Huang. 2019. “ALiPy: Active Learning in Python.” arXiv Preprint arXiv:1901.03802.
“The California Almond.” n.d. Accessed February 16, 2024. https://www.waterfordnut.com/almond.html.
Toledo-Marı́n, J Quetzalcóatl, Geoffrey Fox, James P Sluka, and James A Glazier. 2021. “Deep Learning Approaches to Surrogates for Solving the Diffusion Equation for Mechanistic Real-World Simulations.” Frontiers in Physiology 12: 667828.
Tsipras, Dimitris, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry. 2018. “Robustness May Be at Odds with Accuracy.” arXiv Preprint arXiv:1805.12152.
Uhler, Caroline, Garvesh Raskutti, Peter Bühlmann, and Bin Yu. 2013. “Geometry of the Faithfulness Assumption in Causal Inference.” The Annals of Statistics, 436–63.
Van Noorden, Richard, and Jeffrey M. Perkel. 2023. AI and Science: What 1,600 Researchers Think.” Nature 621 (7980): 672–75. https://doi.org/10.1038/d41586-023-02980-0.
Vapnik, Vladimir N. 1999. “An Overview of Statistical Learning Theory.” IEEE Transactions on Neural Networks 10 (5): 988–99.
Vens, Celine, Jan Struyf, Leander Schietgat, Sašo Džeroski, and Hendrik Blockeel. 2008. “Decision Trees for Hierarchical Multi-Label Classification.” Machine Learning 73: 185–214.
Vigen, Tyler. 2015. Spurious Correlations. Hachette UK.
Wachter, Sandra, Brent Mittelstadt, and Chris Russell. 2017. “Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR.” SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3063289.
Wang, Siyue, Xiao Wang, Pu Zhao, Wujie Wen, David Kaeli, Peter Chin, and Xue Lin. 2018. “Defensive Dropout for Hardening Deep Neural Networks Under Adversarial Attacks.” In 2018 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 1–8. IEEE.
Watson, David S., and Marvin N. Wright. 2021. “Testing Conditional Independence in Supervised Learning Algorithms.” Machine Learning 110 (8): 2107–29. https://doi.org/10.1007/s10994-021-06030-6.
Wolpert, David H. 1996. “The Lack of a Priori Distinctions Between Learning Algorithms.” Neural Computation 8 (7): 1341–90.
Wu, Zonghan, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. 2020. “A Comprehensive Survey on Graph Neural Networks.” IEEE Transactions on Neural Networks and Learning Systems 32 (1): 4–24.
Wynants, Laure, Ben Van Calster, Gary S. Collins, Richard D. Riley, Georg Heinze, Ewoud Schuit, Marc M. J. Bonten, et al. 2020. “Prediction Models for Diagnosis and Prognosis of Covid-19: Systematic Review and Critical Appraisal.” BMJ (Clinical Research Ed.) 369 (April): m1328. https://doi.org/10.1136/bmj.m1328.
Yang, Jingkang, Kaiyang Zhou, Yixuan Li, and Ziwei Liu. 2021. “Generalized Out-of-Distribution Detection: A Survey.” arXiv Preprint arXiv:2110.11334.
Yoon, Jinsung, James Jordon, and Mihaela Van Der Schaar. 2018. “GANITE: Estimation of Individualized Treatment Effects Using Generative Adversarial Nets.” In International Conference on Learning Representations.
Yu, Kui, Xianjie Guo, Lin Liu, Jiuyong Li, Hao Wang, Zhaolong Ling, and Xindong Wu. 2020. “Causality-Based Feature Selection: Methods and Evaluations.” ACM Computing Surveys (CSUR) 53 (5): 1–36.
Zeileis, Achim, Torsten Hothorn, and Kurt Hornik. 2008. “Model-Based Recursive Partitioning.” Journal of Computational and Graphical Statistics 17 (2): 492–514. https://doi.org/10.1198/106186008X319331.
Zhang, Huan, Hongge Chen, Zhao Song, Duane Boning, Inderjit S Dhillon, and Cho-Jui Hsieh. 2019. “The Limitations of Adversarial Training and the Blind-Spot Attack.” arXiv Preprint arXiv:1901.04684.
Zhang, Zhou, Yufang Jin, Bin Chen, and Patrick Brown. 2019. “California Almond Yield Prediction at the Orchard Level with a Machine Learning Approach.” Frontiers in Plant Science 10: 809.