Author

Thomas Wedenig

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Preprint Oct 2026

RaMMOBO: Random Max-Margin Multi-Objective Bayesian Optimization

Multi-objective Bayesian optimization (MOBO) maximizes $M$ expensive black-box objectives with the goal of approximating the Pareto set, whose objective vectors cannot be improved in one component without sacrificing another. A common quality measure is hypervolume, the volume in objective space dominated by these vect...

Thomas Wedenig, Paul Plantosar, Robert Peharz · 0 citations

#machine learning Preprint Oct 2026

Acquisition Function-Guided Generation for Bayesian Optimization over Discrete Spaces

Bayesian optimization (BO) over discrete spaces is challenging due to missing gradients and the sparsity of valid solutions. Latent-space BO sidesteps both issues by fitting a surrogate in the latent space of a generative model, which however substantially complicates the direct incorporation of domain knowledge that i...

Tommaso Ancilli, Thomas Wedenig, Henry Moss et al. · 0 citations