The increasing deployment of machine learning as well as legal regulations such as EU's GDPR cause a need for user-friendly explanations of decisions proposed by machine learning models. Counterfactual explanations are considered as one of the most popular techniques to explain a specific decision of a model. While the computation of "arbitrary" counterfactual explanations is well studied, it is still an open research problem how to efficiently compute plausible and feasible counterfactual explanations. We build upon recent work and propose and study a formal definition of plausible counterfactual explanations. In particular, we investigate how to use density estimators for enforcing plausibility and feasibility of counterfactual explanations. For the purpose of efficient computations, we propose convex density constraints that ensure that the resulting counterfactual is located in a region of the data space of high density.

Recent research on reinforcement learning in pure-conflict and pure-common interest games has emphasized the importance of population heterogeneity. In contrast, studies of reinforcement learning in mixed-motive games have primarily leveraged homogeneous approaches. Given the defining characteristic of mixed-motive games--the imperfect correlation of incentives between group members--we study the effect of population heterogeneity on mixed-motive reinforcement learning. We draw on interdependence theory from social psychology and imbue reinforcement learning agents with Social Value Orientation (SVO), a flexible formalization of preferences over group outcome distributions. We subsequently explore the effects of diversity in SVO on populations of reinforcement learning agents in two mixed-motive Markov games. We demonstrate that heterogeneity in SVO generates meaningful and complex behavioral variation among agents similar to that suggested by interdependence theory. Empirical results in these mixed-motive dilemmas suggest agents trained in heterogeneous populations develop particularly generalized, high-performing policies relative to those trained in homogeneous populations.

According to a new scientific study, a nuclear attack of 100 bombs could harm the entire planet including the aggressor nation. Since the creation of the atom bomb, the threat of nuclear war has loomed. Endless films and books have dealt with the nuclear apocalypse and its aftermath, but what would a nuclear apocalypse really look like? Rutgers University Professor Alan Robock spoke with Fox News about the Armageddon and his team's new study regarding a nuclear war's effects on ocean life. If you live in a major city when a nuke hits, needless to say, you're in big trouble.

AWS is pleased to announce a new feature in Amazon Polly called Brand Voice, a capability in which you can work with the Amazon Polly team of AI research scientists and linguists to build an exclusive, high-quality, Neural Text-to-Speech (NTTS) voice that represents your brand's persona. Brand Voice allows you to differentiate your brand by incorporating a unique vocal identity into your products and services. Amazon Polly has been working with Kentucky Fried Chicken (KFC) Canada and National Australia Bank (NAB) to create two unique Brand Voices, using the same deep learning technology that powers the voice of Alexa. The Amazon Polly team has built a voice for KFC Canada in a Southern US English accent for the iconic Colonel Sanders to voice KFC's latest Alexa skill. The voice-activated skill available through any Alexa-enabled Amazon device allows KFC lovers in Canada to chat all things chicken with Colonel Sanders himself, including re-ordering their favorite KFC.

Existing graph convolutional networks focus on the neighborhood aggregation scheme. When applied to semi-supervised learning, they often suffer from the overfitting problem as the networks are trained with the cross-entropy loss on a small potion of labeled data. In this paper, we propose an unsupervised manifold smoothness loss defined with respect to the graph structure, which can be added to the loss function as a regularization. We draw connections between the proposed loss with an iterative diffusion process, and show that minimizing the loss is equivalent to aggregate neighbor predictions with infinite layers. We conduct experiments on multi-layer perceptron and existing graph networks, and demonstrate that adding the proposed loss can improve the performance consistently.

Recent work has increased the performance of Generative Adversarial Networks (GANs) by enforcing a consistency cost on the discriminator. We improve on this technique in several ways. We first show that consistency regularization can introduce artifacts into the GAN samples and explain how to fix this issue. We then propose several modifications to the consistency regularization procedure designed to improve its performance. We carry out extensive experiments quantifying the benefit of our improvements. For unconditional image synthesis on CIFAR-10 and CelebA, our modifications yield the best known FID scores on various GAN architectures. For conditional image synthesis on CIFAR-10, we improve the state-of-the-art FID score from 11.48 to 9.21. Finally, on ImageNet-2012, we apply our technique to the original BigGAN model and improve the FID from 6.66 to 5.38, which is the best score at that model size.

This article proposes a generalisation of the delete-$d$ jackknife to solve hyperparameter selection problems for time series. This novel technique is compatible with dependent data since it substitutes the jackknife removal step with a fictitious deletion, wherein observed datapoints are replaced with artificial missing values. In order to emphasise this point, I called this methodology artificial delete-$d$ jackknife. As an illustration, it is used to regulate vector autoregressions with an elastic-net penalty on the coefficients. A software implementation, ElasticNetVAR.jl, is available on GitHub.

We propose a novel technique which addresses the challenge of learning accurate and robust models of code in a principled way. Our method consists of three key components: (i) learning to abstain from making a prediction if uncertain, (ii) adversarial training, and (iii) representation refinement which learns the program parts relevant for the prediction and abstracts the rest. These components are used to iteratively train multiple models, each of which learns a suitable program representation necessary to make robust predictions on a different subset of the dataset. We instantiated our approach to the task of type inference for dynamically typed languages and demonstrate its effectiveness by learning a model that achieves 88% accuracy and 84% robustness. Further, our evaluation shows that using the combination of all three components is key to obtaining accurate and robust models.

The reasons behind good DNN generalisation remain an open question. In this paper we explore the problem by looking at the Signal-to-Noise Ratio of nodes in the network. Starting from information theory principles, it is possible to derive an expression for the SNR of a DNN node output. Using this expression we construct figures-of-merit that quantify how well the weights of a node optimise SNR (or, equivalently, information rate). Applying these figures-of-merit, we give examples indicating that weight sets that promote good SNR performance also exhibit good generalisation. In addition, we are able to identify the qualities of weight sets that exhibit good SNR behaviour and hence promote good generalisation. This leads to a discussion of how these results relate to network training and regularisation. Finally, we identify some ways that these observations can be used in training design.

The analysis of multidimensional data is becoming a more and more relevant topic in statistical and machine learning research. Given their complexity, such data objects are usually reshaped into matrices or vectors and then analysed. However, this methodology presents several drawbacks. First of all, it destroys the intrinsic interconnections among datapoints in the multidimensional space and, secondly, the number of parameters to be estimated in a model increases exponentially. We develop a model that overcomes such drawbacks. In particular, we proposed a parsimonious tensor regression based model that retains the intrinsic multidimensional structure of the dataset. Tucker structure is employed to achieve parsimony and a shrinkage penalization is introduced to deal with over-fitting and collinearity. An Alternating Least Squares (ALS) algorithm is developed to estimate the model parameters. A simulation exercise is produced to validate the model and its robustness. Finally, an empirical application to Foursquares spatio-temporal dataset and macroeconomic time series is also performed. Overall, the proposed model is able to outperform existing models present in forecasting literature.