LONDON – Britain decided Tuesday to allow Chinese tech giant Huawei to supply new high-speed network equipment, ignoring the U.S. government's warnings that it would sever intelligence cooperation if the company was not banned. Britain's decision is the first by a major U.S. ally in Europe, and follows intense lobbying from the Trump administration and China as the two vie for technological dominance. It sets up a diplomatic clash with the Americans, who claim that British sovereignty is at risk because the company could give the Chinese government access to data, an allegation Huawei denies. "We would never take decisions that threaten our national security or the security of our Five Eyes partners," Foreign Secretary Dominic Raab said, referring a security arrangement in which Britain, the United States, Australia, Canada and New Zealand, share intelligence. "We know more about Huawei and the risks that it poses than any other country in the world."

Optimal asset allocation is a key topic in modern finance theory. To realize the optimal asset allocation on investor's risk aversion, various portfolio construction methods have been proposed. Recently, the applications of machine learning are rapidly growing in the area of finance. In this article, we propose the Student's $t$-process latent variable model (TPLVM) to describe non-Gaussian fluctuations of financial timeseries by lower dimensional latent variables. Subsequently, we apply the TPLVM to minimum-variance portfolio as an alternative of existing nonlinear factor models. To test the performance of the proposed portfolio, we construct minimum-variance portfolios of global stock market indices based on the TPLVM or Gaussian process latent variable model. By comparing these portfolios, we confirm the proposed portfolio outperforms that of the existing Gaussian process latent variable model.

Recent work in the domain of misinformation detection has leveraged rich signals in the text and user identities associated with content on social media. But text can be strategically manipulated and accounts reopened under different aliases, suggesting that these approaches are inherently brittle. In this work, we investigate an alternative modality that is naturally robust: the pattern in which information propagates. Can the veracity of an unverified rumor spreading online be discerned solely on the basis of its pattern of diffusion through the social network? Using graph kernels to extract complex topological information from Twitter cascade structures, we train accurate predictive models that are blind to language, user identities, and time, demonstrating for the first time that such "sanitized" diffusion patterns are highly informative of veracity. Our results indicate that, with proper aggregation, the collective sharing pattern of the crowd may reveal powerful signals of rumor truth or falsehood, even in the early stages of propagation.

We use distributionally-robust optimization for machine learning to mitigate the effect of data poisoning attacks. We provide performance guarantees for the trained model on the original data (not including the poison records) by training the model for the worst-case distribution on a neighbourhood around the empirical distribution (extracted from the training dataset corrupted by a poisoning attack) defined using the Wasserstein distance. We relax the distributionally-robust machine learning problem by finding an upper bound for the worst-case fitness based on the empirical sampled-averaged fitness and the Lipschitz-constant of the fitness function (on the data for given model parameters) as regularizer. For regression models, we prove that this regularizer is equal to the dual norm of the model parameters. We use the Wine Quality dataset, the Boston Housing Market dataset, and the Adult dataset for demonstrating the results of this paper.

Machine learning models have been shown to be vulnerable to membership inference attacks, i.e., inferring whether individuals' data have been used for training models. The lack of understanding about factors contributing success of these attacks motivates the need for modelling membership information leakage using information theory and for investigating properties of machine learning models and training algorithms that can reduce membership information leakage. We use conditional mutual information leakage to measure the amount of information leakage from the trained machine learning model about the presence of an individual in the training dataset. We devise an upper bound for this measure of information leakage using Kullback--Leibler divergence that is more amenable to numerical computation. We prove a direct relationship between the Kullback--Leibler membership information leakage and the probability of success for a hypothesis-testing adversary examining whether a particular data record belongs to the training dataset of a machine learning model. We show that the mutual information leakage is a decreasing function of the training dataset size and the regularization weight. We also prove that, if the sensitivity of the machine learning model (defined in terms of the derivatives of the fitness with respect to model parameters) is high, more membership information is potentially leaked. This illustrates that complex models, such as deep neural networks, are more susceptible to membership inference attacks in comparison to simpler models with fewer degrees of freedom. We show that the amount of the membership information leakage is reduced by $\mathcal{O}(\log^{1/2}(\delta^{-1})\epsilon^{-1})$ when using Gaussian $(\epsilon,\delta)$-differentially-private additive noises.

A BSTRACT One-shot neural architecture search (NAS) has played a crucial role in making NAS methods computationally feasible in practice. Nevertheless, there is still a lack of understanding on how these weight-sharing algorithms exactly work due to the many factors controlling the dynamics of the process. In order to allow a scientific study of these components, we introduce a general framework for one-shot NAS that can be instantiated to many recently-introduced variants and introduce a general benchmarking framework that draws on the recent large-scale tabular benchmark NAS-Bench-101 for cheap anytime evaluations of one-shot NAS methods. The most crucial concept which led to a reduction in search costs to the order of a single function evaluation is certainly the weight-sharing paradigm: Training only a single large architecture (the one-shot model) subsuming all the possible architectures in the search space (Brock et al., 2018; Pham et al., 2018). Despite the great advancements of these methods, the exact results of many NAS papers are often hard to reproduce (Li & Talwalkar, 2019; Y u et al., 2020; Y ang et al., 2020). This is a result of several factors, such as unavailable original implementations, differences in the employed search spaces, training or evaluation pipelines, hyperparameter settings, and even pseudorandom number seeds (Lindauer & Hutter, 2019). One solution to guard against these problems would be a common library of NAS methods that provides primitives to construct different algorithm variants, similar to what as RLlib (Liang et al., 2017) offers for the field of reinforcement learning. Our paper makes a first step into this direction. Furthermore, experiments in NAS can be computationally extremely costly, making it virtually impossible to perform proper scientific evaluations with many repeated runs to draw statistically robust conclusions. To address this issue, Ying et al. (2019) introduced NAS-Bench-101, a large tabular benchmark with 423k unique cell architectures, trained and fully evaluated using a onetime extreme amount of compute power (several months on thousands of TPUs), which now allows to cheaply simulate an arbitrary number of runs of NAS methods, even on a laptop. NAS-Bench-101 enabled a comprehensive benchmarking of many discrete NAS optimizers (Zoph & Le, 2017; Real et al., 2019), using the exact same settings.

Brain-Computer Interface (BCI) is a powerful communication tool between users and systems, which enhances the capability of the human brain in communicating and interacting with the environment directly. Advances in neuroscience and computer science in the past decades have led to exciting developments in BCI, thereby making BCI a top interdisciplinary research area in computational neuroscience and intelligence. Recent technological advances such as wearable sensing devices, real-time data streaming, machine learning, and deep learning approaches have increased interest in electroencephalographic (EEG) based BCI for translational and healthcare applications. Many people benefit from EEG-based BCIs, which facilitate continuous monitoring of fluctuations in cognitive states under monotonous tasks in the workplace or at home. In this study, we survey the recent literature of EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensated for the gaps in the systematic summary of the past five years (2015-2019). In specific, we first review the current status of BCI and its significant obstacles. Then, we present advanced signal sensing and enhancement technologies to collect and clean EEG signals, respectively. Furthermore, we demonstrate state-of-art computational intelligence techniques, including interpretable fuzzy models, transfer learning, deep learning, and combinations, to monitor, maintain, or track human cognitive states and operating performance in prevalent applications. Finally, we deliver a couple of innovative BCI-inspired healthcare applications and discuss some future research directions in EEG-based BCIs.

Causal explanations present an intuitive way to understand the course of events through causal chains, and are widely accepted in cognitive science as the prominent model humans use for explanation. Importantly, causal models can generate opportunity chains, which take the form of `A enables B and B causes C'. We ground the notion of opportunity chains in human-agent experimental data, where we present participants with explanations from different models and ask them to provide their own explanations for agent behaviour. Results indicate that humans do in-fact use the concept of opportunity chains frequently for describing artificial agent behaviour. Recently, action influence models have been proposed to provide causal explanations for model-free reinforcement learning (RL). While these models can generate counterfactuals---things that did not happen but could have under different conditions---they lack the ability to generate explanations of opportunity chains. We introduce a distal explanation model that can analyse counterfactuals and opportunity chains using decision trees and causal models. We employ a recurrent neural network to learn opportunity chains and make use of decision trees to improve the accuracy of task prediction and the generated counterfactuals. We computationally evaluate the model in 6 RL benchmarks using different RL algorithms, and show that our model performs better in task prediction. We report on a study with 90 participants who receive explanations of RL agents behaviour in solving three scenarios: 1) Adversarial; 2) Search and rescue; and 3) Human-Agent collaborative scenarios. We investigate the participants' understanding of the agent through task prediction and their subjective satisfaction of the explanations and show that our distal explanation model results in improved outcomes over the three scenarios compared with two baseline explanation models.

An Adaptive and Near Parameter-free Evolutionary Computation Approach Towards True Automation in AutoML Benjamin Patrick Evans, Bing Xue, and Mengjie Zhang School of Engineering and Computer Science Victoria University of Wellington New Zealand { benjamin.evans,bing.xue,mengjie.zhang}@ecs.vuw.ac.nz Abstract A common claim of evolutionary computation methods is that they can achieve good results without the need for human intervention. However, one criticism of this is that there are still hyperparameters which must be tuned in order to achieve good performance. In this work, we propose a near "parameter-free" genetic programming approach, which adapts the hyperparameter values throughout evolution without ever needing to be specified manually. We apply this to the area of automated machine learning (by extending TPOT), to produce pipelines which can effectively be claimed to be free from human input, and show that the results are competitive with existing state-of-the-art which use hand-selected hyper-parameter values. Pipelines begin with a randomly chosen estimator and evolve to competitive pipelines automatically. This work moves towards a truly automatic approach to AutoML. 1 Introduction In recent years, machine learning has made its way into many application areas, which has attracted a wide variety of interest from many users from outside the machine learning world. This demand for machine learning has spurred the area of automated machine learning (AutoML), which aims to make machine learning accessible to non-experts [1], or allows experts to focus on other aspects of the machine learning process rather than pipeline design [2]. However, while two of the goals of AutoML are automation and ease of use, most current state-of-the-art methods become a new optimisation problem themselves: rather than searching for pipelines, one must search for appropriate 1 arXiv:2001.10178v1 Granted, this is a simpler search space than the original one, but is still an undesirable property and prevents true human-free automation.

Online learning (aka E-Learning) is now considered to be an integral part of the education sector. In simple words, online learning refers to the type of learning where the learning process is mediated by the internet i.e. the learners use the internet to learn. Online learning is gaining tremendous popularity. It is also said to increase the knowledge retention rates from 25-60% in comparison to face-to-face training. Online learning owes much of its popularity and efficiency to machine learning (ML) and artificial intelligence (AI).