Thepartially observableMarkovdecision process (POMDP) provides ageneral framework for modeling an agent's decision process with state uncertainty, and online planning plays a pivotal role in solving it. A belief is a distribution of states representing state uncertainty. Methods forlarge-scale POMDP problems rely on the same idea of sampling both states and observations.

Tensor decompositions (TDs) serve as a powerful tool for analyzing multiway data.

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The sampling problem has attracted considerable attention recently within the machine learning and statistics communities. This renewed interest in sampling is spurred, on one hand, by a wide breadth of applications ranging from Bayesian inference [RC04, DM+19] and its use in inverse problems [DS17], to neural networks [GPAM+14, TR20].

The sampling problem has attracted considerable attention recently within the machine learning and statistics communities. This renewed interest in sampling is spurred, on one hand, by a wide breadth of applications ranging from Bayesian inference [RC04, DM+19] and its use in inverse problems [DS17], to neural networks [GPAM+14, TR20].

Tocircumvent thisissue,wepropose anovelmulti-task learning method, whichsimultaneously learns latent task representations and a block-diagonal Latent Task Assignment Matrix (LTAM).

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We study the Stochastic Shortest Path (SSP) problem in which an agent has to reach a goal state in minimum total expected cost. In the learning formulation ofthe problem, the agent has no prior knowledge about the costs and dynamics of the model. She repeatedly interacts with the model forK episodes, and has to minimize her regret. In this work we show that the minimax regret for this setting is eO( p (B2?+B?)|S||A|K)whereB?