Design of experiments for nonlinear models

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Overcoming Forgetting in Federated Learning on Non-IID Data (2019, background): “It is also well known that under some regularity conditions, the information matrix approximates the Hessian H L of L ( θ ) , at θ = θ ∗ [8].”
Minimax and maximin space-filling designs: some properties and methods for construction (2017, unknown): “…p parameters, design measures maximising an information criterion based on the Fisher information matrix need no more than p ( p + 1 ) / 2 + 1 support points, see, e.g., Fedorov (1972), Silvey (1980), Pukelsheim (1993), Atkinson et al. (2007), Pronzato and Pázman (2013), Fedorov and Leonov (2014).”