Furthermore, AugSelf can easily be incorporated into recent state-of-the-art representation learning methods with a negligible additional training cost.

However, existing methods assume a reliable and unbiased forecasting environment, which isnot always available inthe wild. Inthis work, we investigate the vulnerability ofspatiotemporal trafficforecasting models andpropose apractical adversarial spatiotemporal attack framework.

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 π2Q1(s,a) Ea π1Q1(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.

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

Vision-language pretrained models have seen remarkable success, but their application to safety-critical settings is limited by their lack of interpretability.

Reducing the variance of the gradient estimator is known to improve the convergence rate of stochastic gradient-based optimization and sampling algorithms.

In this work, we build on this framework and proposeAvare, a simple and efficient algorithm for adaptive importance sampling for finite-sum optimization and sampling with decreasing step-sizes.