We study the problem of minimizing a strongly convex and smooth function when we have noisy estimates of its gradient. We propose a novel multistage accelerated algorithm that is universally optimal in the sense that it achieves the optimal rate both in the deterministic and stochastic case and operates without knowledge of noise characteristics. The algorithm consists of stages that use a stochastic version of Nesterov's accelerated algorithm with a specific restart and parameters selected to achieve the fastest reduction in the bias-variance terms in the convergence rate bounds.

Ensuring that classifiers are non-discriminatory or fair with respect to a sensitive feature (e.g., race or gender) is a topical problem. Progress in this task requires fixing a definition of fairness, and there have been several proposals in this regard over the past few years. Several of these, however, assume either binary sensitive features (thus precluding categorical or real-valued sensitive groups), or result in non-convex objectives (thus adversely affecting the optimisation landscape). In this paper, we propose a new definition of fairness that generalises some existing proposals, while allowing for generic sensitive features and resulting in a convex objective. The key idea is to enforce that the expected losses (or risks) across each subgroup induced by the sensitive feature are commensurate. We show how this relates to the rich literature on risk measures from mathematical finance. As a special case, this leads to a new convex fairness-aware objective based on minimising the conditional value at risk (CVaR).

There is much debate in machine ethics about the most appropriate way to introduce ethical reasoning capabilities into intelligent autonomous machines. Recent incidents involving autonomous vehicles in which humans have been killed or injured have raised questions about how we ensure that such vehicles have an ethical dimension to their behaviour and are therefore trustworthy. The main problem is that hardwiring such machines with rules not to cause harm or damage is not consistent with the notion of autonomy and intelligence. Also, such ethical hardwiring does not leave intelligent autonomous machines with any course of action if they encounter situations or dilemmas for which they are not programmed or where some harm is caused no matter what course of action is taken. Teaching machines so that they learn ethics may also be problematic given recent findings in machine learning that machines pick up the prejudices and biases embedded in their learning algorithms or data. This paper describes a fuzzy reasoning approach to machine ethics. The paper shows how it is possible for an ethics architecture to reason when taking over from a human driver is morally justified. The design behind such an ethical reasoner is also applied to an ethical dilemma resolution case. One major advantage of the approach is that the ethical reasoner can generate its own data for learning moral rules (hence, autometric) and thereby reduce the possibility of picking up human biases and prejudices. The results show that a new type of metric-based ethics appropriate for autonomous intelligent machines is feasible and that our current concept of ethical reasoning being largely qualitative in nature may need revising if want to construct future autonomous machines that have an ethical dimension to their reasoning so that they become moral machines.

For example, in the context of target tracking using a decentralized sensor network, the sensor cooperation can compensate for the effect of the misdetection, false-alarms and even the failure of the local sensor and extends their fields of view, eventually resulting in improved estimation accuracy and improved robustness [1, 2, 3, 4, 5, 6]. Particular interest in distributed data fusion has been paid to calculating the "average" over the information owned by locally netted sensors/agents via peer-to-peer communication in an efficient, flexible and scalable way [7, 8, 9, 6, 10]. Fundamentally, the average can be defined in two manners including, the arithmetic average (AA) and the geometric average (GA). Simply put, the former is a type of linear/convex fusion, akin to the linear opinion pool approach, while the latter is nonlinear/logarithmic fusion akin to the logarithmic opinion pool approach [11, 12], or to say, linear versus log-linear pools [13]. In the context of multi-sensor/multi-agent target tracking, the two most important types of information for fusion among local sensors/agents are random variables (representing parameters such as the number of targets, clutter rate, target existing probability, etc.) and probability density functions (PDFs), for which the fusion is referred to as v-fusion and f -fusion, respectively. While it seems that the AA fusion is more common in the former [7, 8, 4, 9], the GA fusion is vibrant in the latter [3, 14, 15], which coincides with the Chernoff fusion [16, 17, 18, 19] and is also known as covariance intersection (CI) when Gaussian functions that are uniquely characterized by the first and second order statistics are particularly considered [20, 21, 22, 23, 24, 25]. The CI approach was originally proposed for fusing unknown-correlated estimates produced at distinct but not necessarily independent sensors to avoid information double accounting in the fusion. Likewise, the AA fusion can also avoid information double accounting [22]. Further approaches to combining probability distributions of unknown cross-correlation can be found in the literature [26, 27, 28, 29, 30].

Next-generation wireless networks (NGWN) have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of machine learning by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning, respectively. Furthermore, we investigate their employment in the compelling applications of NGWNs, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various machine learning algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.

Anomaly detection (AD) task corresponds to identifying the true anomalies from a given set of data instances. AD algorithms score the data instances and produce a ranked list of candidate anomalies, which are then analyzed by a human to discover the true anomalies. However, this process can be laborious for the human analyst when the number of false-positives is very high. Therefore, in many real-world AD applications including computer security and fraud prevention, the anomaly detector must be configurable by the human analyst to minimize the effort on false positives. In this paper, we study the problem of active learning to automatically tune ensemble of anomaly detectors to maximize the number of true anomalies discovered. We make four main contributions towards this goal. First, we present an important insight that explains the practical successes of AD ensembles and how ensembles are naturally suited for active learning. Second, we present several algorithms for active learning with tree-based AD ensembles. These algorithms help us to improve the diversity of discovered anomalies, generate rule sets for improved interpretability of anomalous instances, and adapt to streaming data settings in a principled manner. Third, we present a novel algorithm called GLocalized Anomaly Detection (GLAD) for active learning with generic AD ensembles. GLAD allows end-users to retain the use of simple and understandable global anomaly detectors by automatically learning their local relevance to specific data instances using label feedback. Fourth, we present extensive experiments to evaluate our insights and algorithms. Our results show that in addition to discovering significantly more anomalies than state-of-the-art unsupervised baselines, our active learning algorithms under the streaming-data setup are competitive with the batch setup.

To improve the performance of Intensive Care Units (ICUs), the field of bio-statistics has developed scores which try to predict the likelihood of negative outcomes. These help evaluate the effectiveness of treatments and clinical practice, and also help to identify patients with unexpected outcomes. However, they have been shown by several studies to offer sub-optimal performance. Alternatively, Deep Learning offers state of the art capabilities in certain prediction tasks and research suggests deep neural networks are able to outperform traditional techniques. Nevertheless, a main impediment for the adoption of Deep Learning in healthcare is its reduced interpretability, for in this field it is crucial to gain insight on the why of predictions, to assure that models are actually learning relevant features instead of spurious correlations. To address this, we propose a deep multi-scale convolutional architecture trained on the Medical Information Mart for Intensive Care III (MIMIC-III) for mortality prediction, and the use of concepts from coalitional game theory to construct visual explanations aimed to show how important these inputs are deemed by the network. Our results show our model attains state of the art performance while remaining interpretable. Supporting code can be found at https://github.com/williamcaicedo/ISeeU.

Bayesian online changepoint detection (BOCPD) (Adams & MacKay, 2007) offers a rigorous and viable way to identity changepoints in complex systems. In this work, we introduce a Stein variational online changepoint detection (SVOCD) method to provide a computationally tractable generalization of BOCPD beyond the exponential family of probability distributions. We integrate the recently developed Stein variational Newton (SVN) method (Detommaso et al., 2018) and BOCPD to offer a full online Bayesian treatment for a large number of situations with significant importance in practice. We apply the resulting method to two challenging and novel applications: Hawkes processes and long short-term memory (LSTM) neural networks. In both cases, we successfully demonstrate the efficacy of our method on real data.

We present an online algorithm that induces decision trees using gradient information as the source of supervision. In contrast to previous approaches to gradient-based tree learning, we do not require soft splits or construction of a new tree for every update. In experiments, our method performs comparably to standard incremental classification trees and outperforms state of the art incremental regression trees. We also show how the method can be used to construct a novel type of neural network layer suited to learning representations from tabular data and find that it increases accuracy of multiclass and multi-label classification.

Local competition among neighboring neurons is a common procedure taking place in biological systems. This finding has inspired research on more biologically plausible deep networks that comprise competing linear units, as opposed to nonlinear units that do not entail any form of (local) competition. This paper revisits this modeling paradigm, with the aim of enabling inference of networks that retain state-of-the-art accuracy for the least possible model complexity; this includes the needed number of connections or locally competing sets of units, as well as the required floating-point precision for storing the network weights. To this end, we leverage solid arguments from the field of Bayesian nonparametrics. Specifically, we introduce auxiliary discrete latent variables of model component utility, and perform Bayesian inference over them. Then, we impose appropriate stick-breaking priors over the introduced discrete latent variables; these give rise to a well-established sparsity-inducing mechanism. As we experimentally show using benchmark datasets, our approach yields networks with less memory footprint than the state-of-the-art, and with no compromises in predictive accuracy.