InthecaseofSEkernel, the regularity assumption implies the existence ofall weak derivativesoff. The third equation follows from the definition of Zn(x). The first inequality holds by Assumption 2. We utilize Proposition 1 to conclude thatkZn(x)k2 σ Thesecond inequality holds bydefinition oflight-tailed distributions. Notice thatthecareful choice ofτ andθ ensures θζi(x) h0, which will be validated next. The seventh line is obtained by replacing the valueof θ.

Our sequential optimization setting is as follows. Consider an objective functionf defined over a domainX Rd, whered N is the dimension of the input.

The domain-specific representation is optimized through the meta-learning framework to adapt from source domains, targeting a robust generalization on unseen domains. We empirically show that mDSDI provides competitive results with state-of-the-art techniques in DG.

We consider the problem of a mediator learning to coordinate a group of strategic agents via recommendations of actions to take without knowledge of their underlying utility functions (e.g.

The expected improvement (EI) is a popular technique to handle the tradeoff between exploration andexploitation underuncertainty. Thistechnique hasbeen widely used in Bayesian optimization but it is not applicable for the contextual bandit problem which is a generalization of the standard bandit and Bayesian optimization.