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Creating Powerful and Interpretable Models with Regression Networks
While some methods for combining these exist in the literature, our architecture generalizes these approaches by taking interactions into account, offering the power of a dense neural network without forsaking interpretability.
The IMP game: Learnability, approximability and adversarial learning beyond $Σ^0_1$
We introduce a problem set-up we call the Iterated Matching Pennies (IMP) game and show that it is a powerful framework for the study of three problems: adversarial learnability, conventional (i. e., non-adversarial) learnability and approximability.
On the universality of cognitive tests
The notion of universal test has recently emerged in the context of machine intelligence evaluation as a way to define and use the same cognitive test for a variety of systems, using some principled tasks and adapting the interface to each particular subject.
