Proposes a trajectory-oriented Bayesian optimization method using Gaussian process surrogates on parameters and seeds with adaptive Thompson sampling for efficient discovery of data-consistent trajectories in stochastic epidemic models.
Jones, Matthias Schonlau, and William J
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LEAP uses a domain-specialized LLM and Bayesian optimization to prioritize perovskite additives, achieving average PCEs of 20.13% and 20.87% in later screening rounds versus 19.25% control, with a champion of 21.32%.
Delight-gated exploration spends actions only when expected improvement times surprisal exceeds a gate price, recovers Pandora's reservation rule, and shows weaker regret growth than Thompson sampling or epsilon-greedy across bandits and MDPs with transferable hyperparameters.
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Staying on Track: Efficient Trajectory Discovery with Adaptive Batch Sampling
Proposes a trajectory-oriented Bayesian optimization method using Gaussian process surrogates on parameters and seeds with adaptive Thompson sampling for efficient discovery of data-consistent trajectories in stochastic epidemic models.