Learning-enabled controllers used in cyber-physical systems (CPS) are known to be susceptible to adversarial attacks. Such attacks manifest as perturbations to the states generated by the controller's environment in response to its actions. We consider state perturbations that encompass a wide variety of adversarial attacks and describe an attack scheme for discovering adversarial states. To be useful, these attacks need to be natural, yielding states in which the controller can be reasonably expected to generate a meaningful response. We consider shield-based defenses as a means to improve controller robustness in the face of such perturbations. Our defense strategy allows us to treat the controller and environment as black-boxes with unknown dynamics. We provide a two-stage approach to construct this defense and show its effectiveness through a range of experiments on realistic continuous control domains such as the navigation control-loop of an F16 aircraft and the motion control system of humanoid robots.

Generalized additive models (GAMs) have become a leading model class for data bias discovery and model auditing. However, there are a variety of algorithms for training GAMs, and these do not always learn the same things. Statisticians originally used splines to train GAMs, but more recently GAMs are being trained with boosted decision trees. It is unclear which GAM model(s) to believe, particularly when their explanations are contradictory. In this paper, we investigate a variety of different GAM algorithms both qualitatively and quantitatively on real and simulated datasets. Our results suggest that inductive bias plays a crucial role in model explanations and tree-based GAMs are to be recommended for the kinds of problems and dataset sizes we worked with.

Mixtures of experts have become an indispensable tool for flexible modelling in a supervised learning context, and sparse Gaussian processes (GP) have shown promise as a leading candidate for the experts in such models. In the present article, we propose to design the gating network for selecting the experts from such mixtures of sparse GPs using a deep neural network (DNN). This combination provides a flexible, robust, and efficient model which is able to significantly outperform competing models. We furthermore consider efficient approaches to computing maximum a posteriori (MAP) estimators of these models by iteratively maximizing the distribution of experts given allocations and allocations given experts. We also show that a recently introduced method called Cluster-Classify- Regress (CCR) is capable of providing a good approximation of the optimal solution extremely quickly. This approximation can then be further refined with the iterative algorithm.

Online hate speech is a newborn problem in our modern society which is growing at a steady rate exploiting weaknesses of the corresponding regimes that characterise several social media platforms. Therefore, this phenomenon is mainly cultivated through such comments, either during users' interaction or on posted multimedia context. Nowadays, giant companies own platforms where many millions of users log in daily. Thus, protection of their users from exposure to similar phenomena for keeping up with the corresponding law, as well as for retaining a high quality of offered services, seems mandatory. Having a robust and reliable mechanism for identifying and preventing the uploading of related material would have a huge effect on our society regarding several aspects of our daily life. On the other hand, its absence would deteriorate heavily the total user experience, while its erroneous operation might raise several ethical issues. In this work, we present a protocol for creating a more suitable dataset, regarding its both informativeness and representativeness aspects, favouring the safer capture of hate speech occurrence, without at the same time restricting its applicability to other classification problems. Moreover, we produce and publish a textual dataset with two variants: binary and multi-label, called `ETHOS', based on YouTube and Reddit comments validated through figure-eight crowdsourcing platform. Our assumption about the production of more compatible datasets is further investigated by applying various classification models and recording their behaviour over several appropriate metrics.

We analyze the adaptive first order algorithm AMSGrad, for solving a constrained stochastic optimization problem with a weakly convex objective. We prove the $\mathcal{\tilde O}(t^{-1/4})$ rate of convergence for the norm of the gradient of Moreau envelope, which is the standard stationarity measure for this class of problems. It matches the known rates that adaptive algorithms enjoy for the specific case of unconstrained smooth stochastic optimization. Our analysis works with mini-batch size of $1$, constant first and second order moment parameters, and possibly unbounded optimization domains. Finally, we illustrate the applications and extensions of our results to specific problems and algorithms.

Machine learning (ML) systems often encounter Out-of-Distribution (OoD) errors when dealing with testing data coming from a distribution different from training data. It becomes important for ML systems in critical applications to accurately quantify its predictive uncertainty and screen out these anomalous inputs. However, existing OoD detection approaches are prone to errors and even sometimes assign higher likelihoods to OoD samples. Unlike standard learning tasks, there is currently no well established guiding principle for designing OoD detection architectures that can accurately quantify uncertainty. To address these problems, we first seek to identify guiding principles for designing uncertainty-aware architectures, by proposing Neural Architecture Distribution Search (NADS). NADS searches for a distribution of architectures that perform well on a given task, allowing us to identify common building blocks among all uncertainty-aware architectures. With this formulation, we are able to optimize a stochastic OoD detection objective and construct an ensemble of models to perform OoD detection. We perform multiple OoD detection experiments and observe that our NADS performs favorably, with up to 57% improvement in accuracy compared to state-of-the-art methods among 15 different testing configurations.

Stochastic optimization is the process of minimizing a deterministic objective function via the simulation of random elements, and it is one of the most successful methods for optimizing complex or unknown objectives. Relatively simple stochastic optimization procedures--in particular, stochastic gradient descent (SGD)--have become the backbone of modern machine learning (ML) [50]. To improve understanding of stochastic optimization in ML, and particularly why SGD and its extensions work so well, recent theoretical work has sought to study its properties and dynamics [47]. Such analyses typically approach the problem through one of two perspectives. The first perspective, an optimization (or quenching) perspective, examines convergence either in expectation [11, 20, 28, 60, 84] or with some positive (high) probability [19, 41, 66, 77] through the lens of a deterministic counterpart. This perspective inherits some limitations of deterministic optimizers, including assumptions (e.g., convexity, Polyak-Łojasiewicz criterion, etc.) that are either not satisfied by state-of-the-art problems, or not strong enough to imply convergence to a quality (e.g., global) optimum. More concerning, however, is the inability to explain what has come to be known as the "generalization gap" phenomenon: increasing stochasticity by reducing batch size appears to improve generalization performance [38, 55]. Empirically, existing strategies do tend to break down for inference tasks when using large batch sizes [27].

The government could ban loot boxes amid accusations they allow children to gamble. Officials have launched a new consultation into the technology and whether it is damaging children who play games like Fifa, which include them. Loot boxes – which are known under a variety of different names in individual games – allow people to buy a collection of items without knowing what will be inside of them. After a person has bought one, either with real money or by playing, they receive whatever is inside, which could include in-game items that can be sold for real currency. Critics argue that the technology allows children to gamble and encourages such behaviour. A number of child welfare organisations, charities and other groups have warned that the technology could lead to addiction later in life.

Simulated warfare between artificial intelligence participants has revealed that "extraordinary forms" of extreme weaponry evolve when combatants fight each other in one-to-one in duels. Researchers at the University of Auckland in New Zealand pitted AI players against each other in a war game to better understand how animals evolve weapons. They found that combatants with improved weapons had a large advantage when fighting in duels, but that this advantage deteriorated when there were more rivals to fight against. The findings suggest that arms races between animals and in other types of conflict are more likely to be accelerated when there are only two opponents. The study was based on a current evolutionary hypothesis that predicts the evolution of elaborate weaponry in duel-based systems, such as the exaggerated horns wielded by male dung beetles and stag deer when fighting over females.

Mourners pay respects to George Floyd in Houston; reaction and analysis on'The Five.' IBM has quit the facial recognition technology business, citing concerns that it can be used for mass surveillance and racial profiling. The move comes amid ongoing protests following the death of George Floyd on May 25--while in police custody in Minneapolis--that have thrust racial injustice and police monitoring technology into the spotlight. The tech giant's CEO Arvind Krishna explained IBM's decision in a letter sent to U.S. lawmakers Monday. "IBM no longer offers general purpose IBM facial recognition or analysis software," he wrote. "IBM firmly opposes and will not condone uses of any technology, including facial recognition technology offered by other vendors, for mass surveillance, racial profiling, violations of basic human rights and freedoms, or any purpose which is not consistent with our values and Principles of Trust and Transparency."