Noémie Jaquier

Robots can learn faster and generalize better by composing symmetries across their configuration and task spaces, instead of treating them in isolation.

By respecting the intrinsic geometry of the probability simplex, $\alpha$-GaBO significantly outperforms standard Bayesian optimization in tasks involving probabilities and mixtures.