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AnInformation-theoreticApproachtoDistribution Shifts

Neural Information Processing Systems

From our theoretical analysis and empirical evaluation, we conclude that the model selection procedure needs tobe guided by careful considerations regardingtheobserveddata,thefactorsusedforcorrection,andthestructureofthe data-generatingprocess.





In this section, we present detailed proofs for the theoretical derivation of Thm. 1, which aims to solvethefollowingoptimizationproblem: min

Neural Information Processing Systems

These assumptions are not strong and can be satisfied in most of environments includes MuJoCo, Atarigamesandsoon. Let f be an Lebesgue integrable function, P and Q are two probability distributions, |f| C,then EP(x)f(x) EQ(x)f(x) CDTV(P,Q) (5) Proof. Suppose there are two actions a1, a2 under state s, and let Q1(s,a1) = u, Q1(s,a2) = v. In this way, we can derive the upper bound of Ea ฯ€2Q1(s,a) Ea ฯ€1Q1(s,a)asabove. Since both sides of the above equation have the same minimum (here the minima are given by Qk = Q), we can replace the objective in Problem 2 with the upper bound in Eq. (10) and solve therelaxedoptimizationproblem.



Towards Efficient Pre-Trained Language Model via Feature Correlation Distillation

Neural Information Processing Systems

Therefore, a series of attempts Chung et al. [2020], Wu et al. [2020], Wang et al. [2020c], Gordon et al. [2020a], Tang et al. [2019], Aguilar et al. [2019] have been made to review the techniques for effective