The Elements of Statistical Learning

A maxent predictive model for hunter-gatherer sites in the southern pampas, argentina

Rafuse, D. J. (2021) · Open Quaternary · doi:10.5334/oq.97 · Cited by 17

Regularised Manova for high-dimensional data

Ullah, I., & Jones, B. (2015) · Australian & New Zealand Journal of Statistics · doi:10.1111/anzs.12126 · Cited by 14

Stacking machine learning algorithms for biomarker-based preoperative diagnosis of a pelvic mass

Shaw, R., Lokshin, A. E., Miller, M. C., Messerlian-Lambert, G., & Moore, R. G. (2022) · Cancers · doi:10.3390/cancers14051291 · Cited by 16

Coverage-based search result diversification

Zheng, W., Wang, X., Fang, H., & Cheng, H. (2012) · Information Retrieval · doi:10.1007/s10791-011-9178-4 · Cited by 16

Detecting online child grooming conversation

Gunawan, F. E., Ashianti, L., Candra, S., & Soewito, B. (2016) · 2016 11th International Conference on Knowledge, Information and Creativity Support Systems (KICSS) · doi:10.1109/kicss.2016.7951413 · Cited by 17

Predicting need for hospital-specific interventional care after surgery using electronic health record data

van de Sande, D., van Genderen, M. E., Verhoef, C., van Bommel, J., Gommers, D., van Unen, E., Huiskens, J., & Grünhagen, D. J. (2021) · Surgery · doi:10.1016/j.surg.2021.05.005 · Cited by 15

From bias to black boxes: understanding and managing the risks of AI – an actuarial perspective

du Preez, V., Bennet, S., Byrne, M., Couloumy, A., Das, A., Dessain, J., Galbraith, R., King, P., Mutanga, V., Schiller, F., et al. (2024) · British Actuarial Journal · doi:10.1017/s1357321724000060 · Cited by 15

Combined Physics- and Machine-Learning-Based Method to Identify Druggable Binding Sites Using SILCS-Hotspots

Nordquist, E. B., Zhao, M., Kumar, A., & MacKerell, A. D. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01189 · Cited by 15

Penalized logistic regression for classification and feature selection with its application to detection of two official species of Ganoderma

Zhu, Y., Tan, T. L., & Cheang, W. K. (2017) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2017.09.019 · Cited by 13

Machine learning for precision dermatology: Advances, opportunities, and outlook

Lee, E. Y., Maloney, N. J., Cheng, K., & Bach, D. Q. (2021) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2020.06.1019 · Cited by 14

Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment

Wang, Y. (2019) · Political Analysis · doi:10.1017/pan.2018.40 · Cited by 12

Machine learning analyses of highly-multiplexed immunofluorescence identifies distinct tumor and stromal cell populations in primary pancreatic tumors1

Vance, K., Alitinok, A., Winfree, S., Jensen-Smith, H., Swanson, B. J., Grandgenett, P. M., Klute, K. A., Crichton, D. J., & Hollingsworth, M. A. (2022) · Cancer Biomarkers · doi:10.3233/cbm-210308 · Cited by 14

Identification of biotypes in Attention-Deficit/Hyperactivity Disorder, a report from a randomized, controlled trial

Leikauf, J. E., Griffiths, K. R., Saggar, M., Hong, D. S., Clarke, S., Efron, D., Tsang, T. W., Hermens, D. F., Kohn, M. R., & Williams, L. M. (2017) · Personalized Medicine in Psychiatry · doi:10.1016/j.pmip.2017.02.001 · Cited by 14

Bus Arrival Time Prediction: A Comprehensive Review

Kumar, B. A., Singh, R., Shaji, H. E., & Vanajakshi, L. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3545695 · Cited by 13

Rationales for Sequential Predictions

Vafa, K., Deng, Y., Blei, D., & Rush, A. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.807 · Cited by 14

When Do Learners Study?

Sher, V., Hatala, M., & Gašević, D. (2022) · Journal of Learning Analytics · doi:10.18608/jla.2022.6697 · Cited by 12

Pairing Machine Learning and Clinical Psychology: How You Evaluate Predictive Performance Matters

Jacobucci, R., Littlefield, A. K., Millner, A. J., Kleiman, E., & Steinley, D. (2020) · doi:10.31234/osf.io/2yber · Cited by 12

Replication-based regularization approaches to diagnose Reinke’s edema by using voice recordings

Naranjo, L., Pérez, C. J., Campos-Roca, Y., & Madruga, M. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102162 · Cited by 11

Diagnosis of disease affecting gait with a body acceleration-based model using reflected marker data for training and a wearable accelerometer for implementation

Takallou, M. A., Fallahtafti, F., Hassan, M., Al-Ramini, A., Qolomany, B., Pipinos, I., Myers, S., & Alsaleem, F. (2024) · Scientific Reports · doi:10.1038/s41598-023-50727-8 · Cited by 11

Calibration and high fidelity measurement of a quantum photonic chip

Li, H. W., Wabnig, J., Bitauld, D., Shadbolt, P., Politi, A., Laing, A., O'Brien, J. L., & Niskanen, A. O. (2013) · New Journal of Physics · doi:10.1088/1367-2630/15/6/063017 · Cited by 11

Machine-learnt versus analytical models of TCP throughput

El Khayat, I., Geurts, P., & Leduc, G. (2007) · Computer Networks · doi:10.1016/j.comnet.2006.11.017 · Cited by 10

Coloring Panchromatic Nighttime Satellite Images: Comparing the Performance of Several Machine Learning Methods

Rybnikova, N., Portnov, B. A., Mirkes, E. M., Zinovyev, A., Brook, A., & Gorban, A. N. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3076011 · Cited by 10

Online gambling policy effects on tax revenue and irresponsible gambling

Taylor, W., McCarthy, D., & Wilbur, K. C. (2024) · doi:10.2139/ssrn.4856684 · Cited by 10

Learning the syntax of plant assemblages

Leblanc, C., Bonnet, P., Servajean, M., Thuiller, W., Chytrý, M., Aćić, S., Argagnon, O., Biurrun, I., Bonari, G., Bruelheide, H., et al. (2025) · Nature Plants · doi:10.1038/s41477-025-02105-7 · Cited by 10

Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular Disease

Linardos, A., Kushibar, K., Walsh, S., Gkontra, P., & Lekadir, K. (2021) · doi:10.21203/rs.3.rs-688924/v1 · Cited by 9

Activity detection in uncontrolled free-living conditions using a single accelerometer

Lee, S. I., Ozsecen, M. Y., Della Toffola, L., Daneault, J.-F., Puiatti, A., Patel, S., & Bonato, P. (2015) · 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) · doi:10.1109/bsn.2015.7299372 · Cited by 9

Towards a methodology for measuring the true degree of efficiency in a speculative market

Lessmann, S., Sung, M.-C., & Johnson, J. E. V. (2011) · Journal of the Operational Research Society · doi:10.1057/jors.2010.192 · Cited by 7

Agent-as-a-Judge: Evaluate Agents with Agents

Zhuge, M., Zhao, C., Ashley, D., Wang, W., Khizbullin, D., Xiong, Y., Liu, Z., Chang, E., Krishnamoorthi, R., Tian, Y., et al. (2024) · arXiv · doi:10.48550/arxiv.2410.10934 · Cited by 5

Machine learning approach to classifying declines of physical function and muscle strength associated with cognitive function in older women: gait characteristics based on three speeds

Kim, B., Youm, C., Park, H., Choi, H., & Shin, S. (2024) · Frontiers in Public Health · doi:10.3389/fpubh.2024.1376736 · Cited by 4

InpherNet accelerates monogenic disease diagnosis using patients’ candidate genes’ neighbors

Yoo, B., Birgmeier, J., Bernstein, J. A., & Bejerano, G. (2021) · Genetics in Medicine · doi:10.1038/s41436-021-01238-2 · Cited by 4

Machine learning and law

Porębski, A. (2023) · Research Handbook on Law and Technology · doi:10.4337/9781803921327.00037 · Cited by 4

Clinical Features and Physiological Signals Fusion Network for Mechanical Circulatory Support Need Prediction in Pediatric Cardiac Intensive Care Unit

Mendoza, A., Tume, S., Puri, K., Acosta, S., & Cavallaro, J. R. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3510217 · Cited by 4

Turnaround Time Prediction for a Medical Laboratory Using Artificial Neural Networks

Eminağaoğlu, M., & Vahaplar, A. (2018) · Bilişim Teknolojileri Dergisi · doi:10.17671/gazibtd.444826 · Cited by 4

Parsimonious statistical learning models for low flow estimation

Laimighofer, J., Melcher, M., & Laaha, G. (2021) · doi:10.5194/hess-2021-481 · Cited by 30

Neuro-Symbolic World Models for Adapting to Open World Novelty

Balloch, J., Lin, Z., Wright, R., Peng, X., Hussain, M., Srinivas, A., Kim, J., & Riedl, M. O. (2023) · arXiv · doi:10.48550/arxiv.2301.06294 · Cited by 8

High value correlates of caregiver reported counseling service need and utilization for adolescents at-risk for childhood maltreatment and neglect

Vázquez, A. L., Chou, T., Navarro Flores, C. M., Barrett, T. S., Villodas, M. T., & Domenech Rodríguez, M. M. (2021) · PLOS ONE · doi:10.1371/journal.pone.0258082 · Cited by 2

NextTopDocker: A Large-Scale Docking-Power Benchmark Reveals Limitations of Current End-to-End Machine-Learning Docking and the Strength of Hybrid Rescoring

Truong, C.-M., Ballester, P. J., Taboureau, O., & Tran-Nguyen, V.-K. (2026) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.6c01143 · Cited by 2

Learning Robust Controllers Via Probabilistic Model-Based Policy Search

Charvet, V., Jensen, B. S., & Murray-Smith, R. (2021) · arXiv · doi:10.48550/arxiv.2110.13576 · Cited by 1

Machine learning-driven risk prediction for post-hospitalization diabetes case management: Integrating clinical and social determinants of health

Lee, S.-Y., Saleem, M., Land, A. M., DeLaney, E. W., Garretson, A. R., Patel, M., Hall, A. G., Westrick, S., Ovalle, F., Cherrington, A. L., et al. (2026) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2026.106604

It's All Connected: A Journey Through Test-Time Memorization, Attentional Bias, Retention, and Online Optimization

Behrouz, A., Razaviyayn, M., Zhong, P., & Mirrokni, V. (2025) · arXiv · doi:10.48550/arxiv.2504.13173 · Cited by 65

Explainable AI Isn't Enough! Rethinking Algorithmic Contestability

Freiesleben, T., Meding, K., & König, G. (2026) · arXiv · doi:10.48550/arxiv.2605.16041

Fast probabilistic whitening transformation for ultra-high dimensional genetic data

Hoffman, G. E., Dillard, C. P., Girdhar, K., & Roussos, P. (2026) · PLOS Genetics · doi:10.1371/journal.pgen.1012320

Subtype-Aware Registration of Longitudinal Electronic Health Records

Gai, X., Jiang, S., & Zhang, A. R. (2026) · Journal of the American Statistical Association · doi:10.1080/01621459.2026.2613464

Learning the syntax of plant assemblages

(2025) · doi:10.21203/rs.3.rs-6304381/v1

Memory Mosaics at scale

Zhang, J., & Bottou, L. (2025) · arXiv · doi:10.48550/arxiv.2507.03285 · Cited by 4

Spectral Networks and Locally Connected Networks on Graphs

Bruna, J., Zaremba, W., Szlam, A., & LeCun, Y. (2013) · arXiv · doi:10.48550/arxiv.1312.6203 · Cited by 3,983

Untitled work

(2011) · Cited by 675

Out-of-Distribution Generalization via Risk Extrapolation (REx)

Krueger, D., Caballero, E., Jacobsen, J.-H., Zhang, A., Binas, J., Zhang, D., Priol, R. L., & Courville, A. (2020) · arXiv · doi:10.48550/arxiv.2003.00688 · Cited by 407

The Elements of Statistical Learning -- Data Mining, . . .

Hastie, T. J., Tibshirani, R. J., & Friedman, J. H. (2013)

The Elements of Statistical Learning: Data Mining, Inference and Prediction. By

Beezer, R. A., Hastie, T., Tibshirani, R., & Springer, J. F. (2002)

The Elements of Statistical Learning: Data Mining, Inference, and Prediction by HASTIE, T., TIBSHIRANI, R., and FRIEDMAN, J.

Rosa, G. J. M. (2010) · Biometrics · doi:10.1111/j.1541-0420.2010.01516.x · Cited by 31

An Introduction to Statistical Learning

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013) · Springer Texts in Statistics · doi:10.1007/978-1-4614-7138-7 · Cited by 12,106

Recommended Reading

Irizarry, R. A. (2026) · Chapman & Hall/CRC eBooks · doi:10.1201/9781003360704-38