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Found 3 papers, 1 papers with code
Human-in-the-Loop Optimization for Deep Stimulus Encoding in Visual Prostheses
We show that our approach quickly learns a personalized stimulus encoder, leads to dramatic improvements in the quality of restored vision, and is robust to noisy patient feedback and misspecifications in the underlying forward model.
Contextual Bayesian optimization with binary outputs
For example, one could directly alter the 'difficulty' of the test that is used to evaluate a system's performance.
Efficient Exploration in Binary and Preferential Bayesian Optimization
Bayesian optimization (BO) is an effective approach to optimize expensive black-box functions, that seeks to trade-off between exploitation (selecting parameters where the maximum is likely) and exploration (selecting parameters where we are uncertain about the objective function).
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