Given 2 (0,12] and 2 (0,1), letT bethesamplesetgiventothe learner.

In this paper we adapt the nearest neighbour rule to the contextual bandit problem.

It has several appealing benefits that prior arts donothave.

Specifically, we explore VPG transfer across different LLM sizes ( e .

In this analysis, we assume that evaluating the GP prior mean and kernel functions (and the corresponding derivatives) takesO(1)time. For each fantasy model, we need to compute the posterior mean and covariance matrix for the L points (x,w1:L), on which we draw the sample paths. This results in a total cost ofO(KML2)to generate all samples. The SAA approach trades a stochastic optimization problem with a deterministic approximation, which can be efficiently optimized. Suppose that we are interested in the optimization problemminxEω[h(x,ω)].