Our regret bound generalizes that of GP-UCB [38] which always selects all variables, aswellasthatofDropout [21]whichselectsdvariables randomly ineachiteration.

Recent work in scientific machine learning has developed so-called physicsinformed neural network(PINN) models.

Neural Information Processing Systems http://nips.cc/

We study the statistical-computational tradeoffs in a high dimensional single index modelY = f(X>β)+, where f is unknown,X is a Gaussian vector and β is s-sparse with unit norm. WhenCov(Y,X>β) 6= 0, [43] shows that the direction and support ofβ can be recovered using a generalized version of Lasso.

Theorem 3 presents boundedness and = 0L , when( , T, 0)ischosen( T) !1 and 0 ! The (mean, sd, 99.9%-quantile) are (178.90,