This paper proposes a fast and scalable method for uncertainty quantification of machine learning models' predictions.

Our goal is to learn an optimal policy that is also permutation invariant.

Approximate nearest neighbor (ANN) search is a fundamental algorithmic problem.

Observations (DMSO) framework introduced in Foster et al.

We propose a general framework to design posterior sampling methods for model-based RL. We show that the proposed algorithms can be analyzed by reducing regret to Hellinger distance in conditional probability estimation. We further show that optimistic posterior sampling can control this Hellinger distance, when we measure model error via data likelihood. This technique allows us to design and analyze unified posterior sampling algorithms with state-of-the-art sample complexity guarantees for many model-based RL settings. We illustrate our general result in many special cases, demonstrating the versatility of our framework.

QB construction for the one-dimensional marginals and model the dependence using highly expressive vine copulas.

The results from one experiment are presented in Figure 2. We plot the resulting variational approximation of the posterior for The conclusions we draw from this experiment are the same as those in Section 4. With the optimal On the left-hand side of Figure 3, we also report the computation times of the methods, and we highlight that the true posterior takes much longer than any of the variational approximations. Again, similar conclusions can be drawn as in the previous sections. Computed T omography (CT) Imaging and Medical Single Photon Emission Computed T omography (SPECT): In CT scans, X-ray measurements are taken from different angles around a patient, and the Radon transform is used to reconstruct a cross-sectional image (slice) of the patient's body. This helps doctors visualize internal structures and diagnose various medical conditions. The Radon transform is used in the image reconstruction process for SPECT.

In this paper, we tackle the important yet under-investigated problem of making long-horizon prediction of event sequences. Existing state-of-the-art models do not perform well at this task due to their autoregressive structure.