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Model learning with personalized interpretability estimation (ML-PIE)., , , , и . GECCO Companion, стр. 1355-1364. ACM, (2021)Deep Generative Symbolic Regression with Monte-Carlo-Tree-Search., , , и . ICML, том 202 из Proceedings of Machine Learning Research, стр. 15655-15668. PMLR, (2023)Evolvability degeneration in multi-objective genetic programming for symbolic regression., , , и . GECCO, стр. 973-981. ACM, (2022)Surrogate-free machine learning-based organ dose reconstruction for pediatric abdominal radiotherapy., , , , , , , , , и 4 other автор(ы). CoRR, (2020)The five Is: Key principles for interpretable and safe conversational AI., и . CIIS, стр. 50-54. ACM, (2021)On the robustness of sparse counterfactual explanations to adverse perturbations., и . Artif. Intell., (марта 2023)Coefficient mutation in the gene-pool optimal mixing evolutionary algorithm for symbolic regression., и . GECCO Companion, стр. 2289-2297. ACM, (2022)DAISY: An Implementation of Five Core Principles for Transparent and Accountable Conversational AI., и . Int. J. Hum. Comput. Interact., 39 (9): 1856-1873 (мая 2023)Local Search is a Remarkably Strong Baseline for Neural Architecture Search., , , и . CoRR, (2020)Scalable genetic programming by gene-pool optimal mixing and input-space entropy-based building-block learning., , , и . GECCO, стр. 1041-1048. ACM, (2017)