Empirical risk minimization (ERM) is the workhorse of machine learning, whether for classification and regression or for off-policy policy learning, but its model-agnostic guarantees can fail when we use adaptively collected data, such as the result of running a contextual bandit algorithm. We study a generic importance sampling weighted ERM algorithm for using adaptively collected data to minimize the average of a loss function over a hypothesis class and provide first-of-their-kind generalization guarantees and fast convergence rates. Our results are based on a new maximal inequality that carefully leverages the importance sampling structure to obtain rates with the right dependence on the exploration rate in the data. For regression, we provide fast rates that leverage the strong convexity of squared-error loss. For policy learning, we provide rate-optimal regret guarantees that close an open gap in the existing literature whenever exploration decays to zero, as is the case for bandit-collected data. An empirical investigation validates our theory.

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Africa has over 2000 languages however, these languages are not well represented in the existing Natural language processing (NLP) ecosystem. One of the challenges is the lack of useful African language datasets that can be used to solve different social and economical problems. In this article, I have compiled a list of African language datasets from across the web. These datasets can be used in numerous NLP tasks such as text classification, named entity recognition, machine translation, sentiment analysis, speech recognition, and topic modeling. This collection of datasets have been made public to give you an opportunity to use your skills and help solving different challenges.

This is the web version of Eye on A.I., a weekly newsletter on the intersection of artificial intelligence and industry. Sign up to get it delivered free to your inbox. Amazon, like other tech giants, is desperately hunting for workers who have an expertise in artificial intelligence. The online retailer has many businesses--its core e-commerce division, the Alexa voice-activated digital service, and the AWS cloud computing unit--that depend on machine learning. But there are relatively few computer scientists who know the technology, and those who do are in high demand.

Fact verification is a challenging task that requires simultaneously reasoning and aggregating over multiple retrieved pieces of evidence to evaluate the truthfulness of a claim. Existing approaches typically (i) explore the semantic interaction between the claim and evidence at different granularity levels but fail to capture their topical consistency during the reasoning process, which we believe is crucial for verification; (ii) aggregate multiple pieces of evidence equally without considering their implicit stances to the claim, thereby introducing spurious information. To alleviate the above issues, we propose a novel topic-aware evidence reasoning and stance-aware aggregation model for more accurate fact verification, with the following four key properties: 1) checking topical consistency between the claim and evidence; 2) maintaining topical coherence among multiple pieces of evidence; 3) ensuring semantic similarity between the global topic information and the semantic representation of evidence; 4) aggregating evidence based on their implicit stances to the claim. Extensive experiments conducted on the two benchmark datasets demonstrate the superiority of the proposed model over several state-of-the-art approaches for fact verification. The source code can be obtained from https://github.com/jasenchn/TARSA.

We consider the problem of embedding a dynamic network, to obtain time-evolving vector representations of each node, which can then be used to describe the changes in behaviour of a single node, one or more communities, or the entire graph. Given this open-ended remit, we wish to guarantee stability in the spatio-temporal positioning of the nodes: assigning the same position, up to noise, to nodes behaving similarly at a given time (cross-sectional stability) and a constant position, up to noise, to a single node behaving similarly across different times (longitudinal stability). These properties are defined formally within a generic dynamic latent position model. By showing how this model can be recast as a multilayer random dot product graph, we demonstrate that unfolded adjacency spectral embedding satisfies both stability conditions, allowing, for example, spatio-temporal clustering under the dynamic stochastic block model. We also show how alternative methods, such as omnibus, independent or time-averaged spectral embedding, lack one or the other form of stability.

The Ebola virus and the disease in effect tend to randomly move individuals in the population around susceptible, infected, quarantined, hospitalized, recovered, and dead sub-population. Motivated by the effectiveness in propagating the disease through the virus, a new bio-inspired and population-based optimization algorithm is proposed. This paper presents a novel metaheuristic algorithm named Ebola optimization algorithm (EOSA). To correctly achieve this, this study models the propagation mechanism of the Ebola virus disease, emphasising all consistent states of the propagation. The model was further represented using a mathematical model based on first-order differential equations. After that, the combined propagation and mathematical models were adapted for developing the new metaheuristic algorithm. To evaluate the proposed method's performance and capability compared with other optimization methods, the underlying propagation and mathematical models were first investigated to determine how they successfully simulate the EVD. Furthermore, two sets of benchmark functions consisting of forty-seven (47) classical and over thirty (30) constrained IEEE CEC-2017 benchmark functions are investigated numerically. The results indicate that the performance of the proposed algorithm is competitive with other state-of-the-art optimization methods based on scalability analysis, convergence analysis, and sensitivity analysis. Extensive simulation results indicate that the EOSA outperforms other state-of-the-art popular metaheuristic optimization algorithms such as the Particle Swarm Optimization Algorithm (PSO), Genetic Algorithm (GA), and Artificial Bee Colony Algorithm (ABC) on some shifted, high dimensional and large search range problems.

As an essential component of dialogue systems, multi-turn response selection aims to pick out the optimal response among a set of candidates to improve the dialogue fluency. In this paper, we investigate three problems of current response selection approaches, especially for generation-based conversational agents: (i) Existing approaches are often formulated as a sentence scoring problem, which does not consider relationships between responses. (ii) Existing models tend to select undesirable candidates that have large overlaps with the dialogue history. (iii) Negative instances in training are mainly constructed by random sampling from the corpus, whereas generated candidates in practice typically have a closer distribution. To address the above problems, we create a new dataset called ConvAI2+ and propose a new response selector called Global-Selector. Experimental results show that Global-Selector trained on ConvAI2+ have noticeable improvements in both accuracy and inference speed.

Given a graph or similarity matrix, we consider the problem of recovering a notion of true distance between the nodes, and so their true positions. Through new insights into the manifold geometry underlying a generic latent position model, we show that this can be accomplished in two steps: matrix factorisation, followed by nonlinear dimension reduction. This combination is effective because the point cloud obtained in the first step lives close to a manifold in which latent distance is encoded as geodesic distance. Hence, a nonlinear dimension reduction tool, approximating geodesic distance, can recover the latent positions, up to a simple transformation. We give a detailed account of the case where spectral embedding is used, followed by Isomap, and provide encouraging experimental evidence for other combinations of techniques.

A company-provided photo of a Kargu Rotary Wing Attack Drone Loitering Munition System manufactured by the STM defense company of Turkey. A U.N. report says the weapons system was used in Libya in March 2020. A company-provided photo of a Kargu Rotary Wing Attack Drone Loitering Munition System manufactured by the STM defense company of Turkey. A U.N. report says the weapons system was used in Libya in March 2020. Military-grade autonomous drones can fly themselves to a specific location, pick their own targets and kill without the assistance of a remote human operator.