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cs.LG2026
Active Learning Guided Design Space Refinement for Scalable Multi-Objective Bayesian Optimization in Materials Discovery
Alexandros Ntagiantas, Panagiotis Tsilimidos, George Giannakopoulos +2
Advanced materials discovery increasingly relies on machine learning and Bayesian optimization to explore large discrete design spaces under limited evaluation budgets. However, co…
cs.LG2026
Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery
Panagiotis Krokidas, Christoforos Rekatsinas, Vassilis Sioros +3
Bayesian Optimization (BO) is widely adopted for data-efficient optimization in scientific and engineering applications, yet its computational cost is rarely evaluated alongside op…