A learned Transformer-based acquisition policy for multi-objective Bayesian optimization, trained on synthetic Gaussian processes, achieves best or near-best hypervolume on most tested synthetic and 3D Gaussian Splatting tuning tasks without retraining.
Q-Transformer: Scalable offline reinforcement learning via autoregressive q-functions
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BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL
A learned Transformer-based acquisition policy for multi-objective Bayesian optimization, trained on synthetic Gaussian processes, achieves best or near-best hypervolume on most tested synthetic and 3D Gaussian Splatting tuning tasks without retraining.