In this section, we examine our theoretical results with controlled experiments via synthetic data. We do not have a complete explanation for such spikes. At first glance, overfitting could happen when the number of linear measurements is less than the size of the groundtruth matrix. Moreover, when the measurements satisfy RIP, Li et al. Soltanolkotabi [ 45 ] show that GD exactly recovers the ground truth. To our best knowledge, most existing generalization analysis for flat regularization are for two-layer models, e.g., Li et al.

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Westudy linear regression under covariate shift, where themarginal distribution over the input covariates differs in the source and the target domains, while the conditional distribution of the output given the input covariates is similar across thetwodomains.

Suppose that 0 <1/kGk2, M <1/kHk2.

Consider a data-generation process that transforms low-dimensional latent causally-related variables to high-dimensional observed variables. Causal representation learning (CRL) is the process of using the observed data to recover the latent causal variables and the causal structure among them.

This gives rise to a rich collection oftopological relationships and conditions under which VE models are optimal for planning. Despite this effort, relatively little is known about the planning performance of models that fail to satisfy these conditions.

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Solving OPE is often the starting point in many RL applications. To tackle the problem of OPE, the idea of importance sampling (IS) corrects the mismatch in the distributions under the behavior policy and target policy.