We consider the problem of learning causal models from observational data generated by linear non-Gaussian acyclic causal models with latent variables. Without considering the effect of latent variables, one usually infers wrong causal relationships among the observed variables. Under faithfulness assumption, we propose a method to check whether there exists a causal path between any two observed variables. From this information, we can obtain the causal order among them. The next question is then whether or not the causal effects can be uniquely identified as well. It can be shown that causal effects among observed variables cannot be identified uniquely even under the assumptions of faithfulness and non-Gaussianity of exogenous noises. However, we will propose an efficient method to identify the set of all possible causal effects that are compatible with the observational data. Furthermore, we present some structural conditions on the causal graph under which we can learn causal effects among observed variables uniquely. We also provide necessary and sufficient graphical conditions for unique identification of the number of variables in the system. Experiments on synthetic data and real-world data show the effectiveness of our proposed algorithm on learning causal models.

W e tackle the problem of unsupervised visual descriptors compression, which is a key ingredient of large-scale image retrieval systems. While the deep learning machinery has benefited literally all computer vision pipelines, the existing state-of-the-art compression methods employ shallow architectures, and we aim to close this gap by our paper . In more detail, we introduce a DNN architecture for the unsupervised compressed-domain retrieval, based on multi-codebook quantization. The proposed architecture is designed to incorporate both fast data encoding and efficient distances computation via lookup tables. W e demonstrate the exceptional advantage of our scheme over existing quantization approaches on several datasets of visual descriptors via outperforming the previous state-of-the-art by a large margin.

Many recent developments in network analysis have focused on multilayer networks, which one can use to encode time-dependent interactions, multiple types of interactions, and other complications that arise in complex systems. Like their monolayer counterparts, multilayer networks in applications often have mesoscale features, such as community structure. A prominent type of method for inferring such structures is the employment of multilayer stochastic block models (SBMs). A common (but inadequate) assumption of these models is the sampling of edges in different layers independently, conditioned on community labels of the nodes. In this paper, we relax this assumption of independence by incorporating edge correlations into an SBM-like model. We derive maximum-likelihood estimates of the key parameters of our model, and we propose a measure of layer correlation that reflects the similarity between connectivity patterns in different layers. Finally, we explain how to use correlated models for edge prediction in multilayer networks. By taking into account edge correlations, prediction accuracy improves both in synthetic networks and in a temporal network of shoppers who are connected to previously-purchased grocery products.

Anomaly detection aims to distinguish observations that are rare and different from the majority. While most existing algorithms assume that instances are i.i.d., in many practical scenarios, links describing instance-to-instance dependencies and interactions are available. Such systems are called attributed networks. Anomaly detection in attributed networks has various applications such as monitoring suspicious accounts in social media and financial fraud in transaction networks. However, it remains a challenging task since the definition of anomaly becomes more complicated and topological structures are heterogeneous with nodal attributes. In this paper, we propose a spectral convolution and deconvolution based framework -- SpecAE, to project the attributed network into a tailored space to detect global and community anomalies. SpecAE leverages Laplacian sharpening to amplify the distances between representations of anomalies and the ones of the majority. The learned representations along with reconstruction errors are combined with a density estimation model to perform the detection. They are trained jointly as an end-to-end framework. Experiments on real-world datasets demonstrate the effectiveness of SpecAE.

Anomaly detection is a fundamental problem in data mining field with many real-world applications. A vast majority of existing anomaly detection methods predominately focused on data collected from a single source. In real-world applications, instances often have multiple types of features, such as images (ID photos, finger prints) and texts (bank transaction histories, user online social media posts), resulting in the so-called multi-modal data. In this paper, we focus on identifying anomalies whose patterns are disparate across different modalities, i.e., cross-modal anomalies. Some of the data instances within a multi-modal context are often not anomalous when they are viewed separately in each individual modality, but contains inconsistent patterns when multiple sources are jointly considered. The existence of multi-modal data in many real-world scenarios brings both opportunities and challenges to the canonical task of anomaly detection. On the one hand, in multi-modal data, information of different modalities may complement each other in improving the detection performance. On the other hand, complicated distributions across different modalities call for a principled framework to characterize their inherent and complex correlations, which is often difficult to capture with conventional linear models. To this end, we propose a novel deep structured anomaly detection framework to identify the cross-modal anomalies embedded in the data. Experiments on real-world datasets demonstrate the effectiveness of the proposed framework comparing with the state-of-the-art.

In collaboration with CEPR and the Brevan Howard Centre, Imperial College, the Bank of England is hosting a research conference on "the impact of machine learning and AI on the UK economy." The purpose of the conference is to stimulate academic research and public debate on how machine learning and AI will impact issues that matter to the Bank of England's policy objectives. The programme will include Deputy Governor Dave Ramsden and other senior Bank members. The call for papers is below and the submission deadline is 30 September 2019.

People always talk about artificial intelligence (AI), but they remain reluctant to dig into what it means or how it can implicate their businesses. Enterprises need to go beyond a basic understanding and learn how AI can unearth what they don't know about their own company. For a retailer, decisions regarding stock levels, price points, distribution volumes or even promotions all fall under this "what you don't know" umbrella. Most decisions are largely made by gut feeling -- an individual who believes their experience will always outperform a computer. When unhindered by human interference or pride, AI and machine learning (ML) will instead ensure that all these decisions are made off the back of statistics and data pulled from every action taking place within a retailer's stores.

A new patent filing in the U.K. aims to find out. An international team led by AI activist Ryan Abbott, a law professor at the University of Surrey, has filed the first-ever patent applications for two inventions created autonomously by artificial intelligence without a human inventor. The AI inventor, named DABUS by its creator, Stephen Thaler, was previously best known for creating surreal art, but it was designed to come up with new ideas and then assess those ideas for consequences, novelty, and salience. So far the series of neural networks that makes up DABUS has come up with two ideas that may be worth patenting. According to a press release, one patent application is for "a new type of beverage container based on fractal geometry," which sounds pretty sweet, while the other is for a device that can help attract attention that could be useful in search and rescue operations.

Guided by artificial intelligence and powered by a robotic platform, a system developed by MIT researchers moves a step closer to automating the production of small molecules that could be used in medicine, solar energy, and polymer chemistry. The system, described in the August 8 issue of Science, could free up bench chemists from a variety of routine and time-consuming tasks, and may suggest possibilities for how to make new molecular compounds, according to the study co-leaders Klavs F. Jensen, the Warren K. Lewis Professor of Chemical Engineering, and Timothy F. Jamison, the Robert R. Taylor Professor of Chemistry and associate provost at MIT. The technology "has the promise to help people cut out all the tedious parts of molecule building," including looking up potential reaction pathways and building the components of a molecular assembly line each time a new molecule is produced, says Jensen. "And as a chemist, it may give you inspirations for new reactions that you hadn't thought about before," he adds. The new system combines three main steps.

A salesperson arranges Nike basketball shoes on display at the House Of Hoops by Foot Locker retail store at the Beverly Center in Los Angeles, California, U.S. Photographer: Patrick T. Fallon/Bloomberg When thinking of data and the broader customer experience, we often think of a very impersonal set of numbers and figures. However, branded data that is analyzed through artificial intelligence can tell a compelling story that brings a human element to a brand's relationship with their customer base. Here are 3 great ways that data can be used to create a more pleasantly human experience for larger brands like Foot Locker, Home Depot and Silicon Labs. For anyone who has ever run a business, when there is a flood of questions from customers, it's nearly impossible to keep up manually. Using a solution like Query Service in the AEM (Adobe Experience Platform) allows the brand to answer complex questions that require diving into the data and allows for scaling both the number of responses and allows for deep personalization.