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Highly Accurate and Efficient Data-Driven Methods for Genotype Imputation., , и . IEEE ACM Trans. Comput. Biol. Bioinform., 16 (4): 1107-1116 (2019)Safe learning-based observers for unknown nonlinear systems using Bayesian optimization., и . Autom., (2021)Safe Learning-based Observers for Unknown Nonlinear Systems using Bayesian Optimization., и . CoRR, (2020)Output-Tracking Quantized Explicit Nonlinear Model Predictive Control Using Multiclass Support Vector Machines., , и . IEEE Trans. Ind. Electron., 64 (5): 4130-4138 (2017)Data-Driven Identification of Dynamic Quality Models in Drinking Water Networks., , и . CoRR, (2022)Robust Data-Driven Neuro-Adaptive Observers With Lipschitz Activation Functions., , , и . CDC, стр. 2862-2867. IEEE, (2019)Treating acute myeloid leukemia via HSC transplantation: A preliminary study of multi-objective personalization strategies., , , и . ACC, стр. 3790-3795. IEEE, (2013)Learning Disagreement Regions with Deep Neural Networks to Reduce Practical Complexity of Mixed-Integer MPC., , , и . SMC, стр. 3238-3244. IEEE, (2021)Data-Driven Estimation of Backward Reachable and Invariant Sets for Unmodeled Systems via Active Learning., , , и . CDC, стр. 372-377. IEEE, (2018)Distributed unknown input observers for interconnected nonlinear systems., , , , , и . ACC, стр. 101-106. IEEE, (2016)