To overcome this limitation, we propose a novel blockwise parallel decoding scheme in which we makepredictions for multiple time steps inparallel then back offtothe longest prefix validated byascoring model.

Causal effect identification considers whether an interventional probability distribution can be uniquely determined from a passively observed distribution in a given causal structure.

However, only the smooth setting was considered. To close this gap, we introduce and analyze O-ZD, the first structured finite-difference algorithm for non-smooth black-box optimization.

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In real-world sequential prediction scenarios, the features (or attributes) of examples are typically high-dimensional and construction of the all features for each example may be expensive or impossible.

Our nonconvex optimization formulation solves for a filter h on the unit sphere that produces sparse outputyi ~h.