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The University of Edinburgh
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Modeling human actions as executable programs not only improves action recognition, but also unlocks more data-efficient and interpretable models compared to standard monolithic approaches.
Stop assuming a single utility function: modeling preferences as a mixture of archetypes unlocks better Bayesian optimization in complex, many-objective spaces.