"shallow" architectures, where the hidden constant may decrease (exponentially) with respect to the depth.

Our algorithm termed deterministic schedule PSRL (DS-PSRL) is efficient in terms of time, sample, and space complexity. We prove a Bayesian regret bound under mild assumptions. Our result is more generally applicable to multiple parameters and continuous state action problems. We compare our algorithm with state-of-the-art PSRL algorithms on standard discrete and continuous problems from the literature.

As a case study, we present such a system that learns to parse Newtonian physics problems in textbooks.

However, sEM has a slow asymptotic convergence rate due to the high variance of each update.

Boltzmann machines (BMs) are appealing candidates for powerful priors in varia-tional autoencoders (V AEs), as they are capable of capturing nontrivial and multi-modal distributions over discrete variables.

Deriving conditional and marginal distributions using conjugacy relationships can be time consuming and error prone. In this paper, we propose a strategy for automating such derivations. Unlike previous systems which focus on relationships between pairs of random variables, our system (which we call Autoconj) operates directly on Python functions that compute log-joint distribution functions. Autoconj provides support for conjugacy-exploiting algorithms in any Python-embedded PPL. This paves the way for accelerating development of novel inference algorithms and structure-exploiting modeling strategies.

Entropy estimation is one of the prototypical problems in distribution property testing.

When applied to distributions defined on a constrained space the time-discretization error can dominate when we are near the boundary of the space.

Implicit feedback is widely used in collaborative filtering methods for recommendation.

Recent work on generating adversarial attacks have shown that it is computationally feasible for a bad actor to fool a DRL policy into behaving sub optimally.