We present a novel form of explanation for Reinforcement Learning, based around the notion of intended outcome.

In many machine learning problems the output should not depend on the order of the input. Such "permutation invariant" functions have been studied extensively recently. Here we argue that temporal architectures such as RNNs are highly relevant for such problems, despite the inherent dependence of RNNs on order. We show that RNNs can be regularized towards permutation invariance, and that this can result in compact models, as compared to non-recurrent architectures. We implement this idea via a novel form of stochastic regularization. Existing solutions mostly suggest restricting the learning problem to hypothesis classes which are permutation invariant by design [Zaheer et al., 2017, Lee et al., 2019, Murphy et al., 2018]. Our approach of enforcing permutation invariance via regularization gives rise to models which are semi permutation invariant (e.g.

To achieve this, most modern GNNs follow a "message passing" mechanism: at

Work done during these two authors' internship at Microsoft Research Asia.

When a model's predicted number of events within any time interval is similar to the observed number, it is called well-calibrated . A survival model's calibration can be measured using, for instance, distributional calibration (

Despite their successes, modern neural networks (NNs) still suffer from several shortcomings that limit their applicability in some settings.