Neural Information Processing Systems http://nips.cc/

Query embedding (QE)--which aims to embed entities and first-order logical (FOL) queries inlow-dimensional spaces--has showngreatpowerinmulti-hop reasoningoverknowledgegraphs.

A major challenge is thus in designing sample-efficient agents that can transfer their existing knowledge to solve new tasks quickly.

This paper introduces a class of mixed-integer formulations for trained ReLU neuralnetworks.

Perhaps the most prominent current definition of (actual) causality is due to Halpern and Pearl. It is defined using causal models (also known as structural equations models). We abstract the definition, extracting its key features, so that it can be applied to any other model where counterfactuals are defined. By abstracting the definition, we gain a number of benefits. Not only can we apply the definition in a wider range of models, including ones that allow, for example, backtracking, but we can apply the definition to determine if A is a cause of B even if A and B are formulas involving disjunctions, negations, beliefs, and nested counterfactuals (none of which can be handled by the Halpern-Pearl definition). Moreover, we can extend the ideas to getting an abstract definition of explanation that can be applied beyond causal models. Finally, we gain a deeper understanding of features of the definition even in causal models.

We show that several online combinatorial optimization problems that admit efficient no-regret algorithms become computationally hard in the sleeping setting where a subset of actions becomes unavailable in each round. Specifically, we show that the sleeping versions of these problems are at least as hard as P AC learning DNF expressions, a long standing open problem.

With such a representation, we can design branching strategies by inferring the marginal probabilities of each variable assignment.