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How Far Out of Distribution Can We Go With ELA Features and Still Be Able to Rank Algorithms?, and . SSCI, page 341-346. IEEE, (2023)Four algorithms to solve symmetric multi-type non-negative matrix tri-factorization problem., , , , , , and . CoRR, (2020)Dealing with zero-inflated data: achieving SOTA with a two-fold machine learning approach., , , , , , , and . CoRR, (2023)On Suitability of the Customized Measuring Device for Electric Motor., , , , , and . IECON, page 1-6. IEEE, (2022)Deep statistics: more robust performance statistics for single-objective optimization benchmarking., , , , , and . GECCO Companion, page 5-6. ACM, (2020)Preferred Solutions of the Ground Station Scheduling Problem using NSGA-III with Weighted Reference Points Selection., , and . CEC, page 1840-1847. IEEE, (2021)SciFoodNER: Food Named Entity Recognition for Scientific Text., , , and . IEEE Big Data, page 4065-4073. IEEE, (2022)PerformViz: a machine learning approach to visualize and understand the performance of single-objective optimization algorithms., , , , , and . GECCO Companion, page 7-8. ACM, (2020)TransOpt: Transformer-based Representation Learning for Optimization Problem Classification., , and . CoRR, (2023)DynamoRep: Trajectory-Based Population Dynamics for Classification of Black-box Optimization Problems., , , , and . GECCO, page 813-821. ACM, (2023)