Computational models in fields such as computational neuroscience are often evaluated via stochastic simulation or numerical approximation. Fitting these models implies a difficult optimization problem over complex, possibly noisy parameter landscapes. Bayesian optimization (BO) has been successfully applied to solving expensive black-box problems in engineering and machine learning. Here we explore whether BO can be applied as a general tool for model fitting. First, we present a novel hybrid BO algorithm, Bayesian adaptive direct search (BADS), that achieves competitive performance with an affordable computational overhead for the running time of typical models. We then perform an extensive benchmark of BADS vs. many common and state-of-the-art nonconvex, derivative-free optimizers, on a set of model-fitting problems with real data and models from six studies in behavioral, cognitive, and computational neuroscience. With default settings, BADS consistently finds comparable or better solutions than other methods, including `vanilla' BO, showing great promise for advanced BO techniques, and BADS in particular, as a general model-fitting tool.
Luigi Acerbi (University of Geneva)
Assistant professor Luigi Acerbi leads the *Machine and Human Intelligence* group at the Department of Computer Science of the University of Helsinki. His research spans Bayesian machine learning and computational and cognitive neuroscience. He is member of the *Finnish Centre for Artificial Intelligence* (FCAI), of the *International Brain Laboratory*, of ELLIS (*European Laboratory for Learning and Intelligent Systems*), and an off-site visiting scholar at New York University.
Wei Ji (New York University)
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