But we are not interested in hypothetical compressors.

Thompson sampling (Russo et al., 2018) is a popular approach in bandit problems based on

Our approach builds upon variational autoencoder (V AE) models for sequential data and combines them with recent work on neural image compression.

We propose a projected Stein variational Newton (pSVN) method for high-dimensional Bayesian inference.

From infancy, humans have expectations about how objects will move and interact.

We use this metric to evaluate the performance versus resource trade-off relevant to the application of interest, and we propose a strategy that selects the device settings that can optimally meet users' requirements.

We would like to thank the reviewers for their thoughtful comments and valuable suggestions. We will clarify this point in the paper. Our algorithms are agnostic to the leaf distributions used. Thanks for this valuable feedback, we will improve the pseudocode as you suggest. As such, there is memory overhead but no computational overhead.

This paper considers a new family of variational distributions motivated by Sklar's theorem. This family is based on new copula-like densities on the hypercube with non-uniform marginals which can be sampled efficiently, i.e. with a complexity linear in the dimension d of the state space. Then, the proposed variational densities that we suggest can be seen as arising from these copula-like densities used as base distributions on the hypercube with Gaussian quantile functions and sparse rotation matrices as normalizing flows. The latter correspond to a rotation of the marginals with complexity O (d log d) . We provide some empirical evidence that such a variational family can also approximate non-Gaussian posteriors and can be beneficial compared to Gaussian approximations. Our method performs largely comparably to state-of-the-art variational approximations on standard regression and classification benchmarks for Bayesian Neural Networks.

This paper concerns, more specifically, the inconsistency of separating sets used to remove dispensable edges, iteratively, based on conditional independence tests.

We consider a finite-horizon multi-armed bandit (MAB) problem in a Bayesian setting, for which we propose an information relaxation sampling framework.