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stat.ML2026
Learning What to Evaluate: Correlation-Aware Decoupling for Multiobjective Bayesian Optimization
Ashwin Renganathan, Peter Bachman
Multiobjective Bayesian optimization (MOBO) with Gaussian process (GP) surrogates is a sample efficient approach to solving multiobjective optimization problems. In MOBO, a Bayesia…
stat.ML2026
A Bayesian latent Gaussian process framework for aerodynamic uncertainty quantification
Geoffrey Davis, Ashwin Renganathan
Predicting the aerodynamic performance (e.g. lift, drag, and moment coefficients) of an aircraft is challenging -- computational models are biased and direct simulations are prohib…