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Beyond Sparsity: Tree Regularization of Deep Models for Interpretability.

, , , , , and . AAAI, page 1670-1678. AAAI Press, (2018)

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A comparison of three computational modelling methods for the prediction of virological response to combination HIV therapy., , , , , , , , , and 6 other author(s). Artif. Intell. Medicine, 47 (1): 63-74 (2009)A graph neural network-based model with Out-of-Distribution Robustness for enhancing Antiretroviral Therapy Outcome Prediction for HIV-1., , , , , , , , and . CoRR, (2023)Structured Proportional Jump Processes., , , , and . UAI, page 172-181. AUAI Press, (2014)The Health Gym: Synthetic Health-Related Datasets for the Development of Reinforcement Learning Algorithms., , , , , , , , and . CoRR, (2022)Regional Tree Regularization for Interpretability in Deep Neural Networks., , , , , , , and . AAAI, page 6413-6421. AAAI Press, (2020)Incorporating temporal dynamics of mutations to enhance the prediction capability of antiretroviral therapy's outcome for HIV-1., , , , , , , , and . CoRR, (2023)Statistical Comparison of Machine Learning Techniques for Treatment Optimisation of Drug-Resistant HIV-1., , and . CBMS, page 427-432. IEEE Computer Society, (2007)Regional Tree Regularization for Interpretability in Black Box Models., , , , , , , and . CoRR, (2019)High CD45 expression of CD8+ and CD4+ T cells correlates with the size of HIV-1 reservoir in blood, , , , , , and . Scientific Reports, 10 (1): 20425-- (2020)A Prognostic Model for Estimating the Time to Virologic Failure in HIV-1 Infected Patients Undergoing a New Combination Antiretroviral Therapy Regimen., , , , , , , , , and 6 other author(s). BMC Medical Informatics Decis. Mak., (2011)