OpenRaiser/Intern-Atlas huggingface dataset:
Detection of Apathy in Alzheimer Patients by Analysing Visual Scanning Behaviour with RNNs
Authors: Jonathan Chung, Sarah A. Chau, Nathan Herrmann, Krista L. Lanctôt, Moshe Eizenman
Venue: KDD
Year: 2018

Assessment of apathy in patients with Alzheimer's disease (AD) relies heavily on interviews with caregivers and patients, which can be ambiguous and time consuming. More precise and objective methods of evaluation can better inform treatment decisions. In this study, visual scanning behaviours (VSBs) on emotional and non-emotional stimuli were used to detect apathy in patients with AD. Forty-eight AD patients participated in the study. Sixteen of the patients were apathetic. Patients looked at 48 slides with non-emotional images and 32 slides with emotional images. We described two methods that use recurrent neural networks (RNNs) to learn differences between the VSBs of apathetic and non-apathetic AD patients. Method 1 uses two separate RNNs to learn group differences between visual scanning sequences on emotional and non-emotional stimuli. The outputs of the RNNs are then combined and used by a logistic regression classifier to characterise patients as either apathetic or non-apathetic. Method 1 achieved an AUC gain of 0.074 compared to a previously presented handcrafted feature method of detecting emotional blunting (AUC handcrafted = 0.646). Method 2 assumes that each individual's "style of scanning" (stereotypical eye movements) is independent of the content of the visual stimuli and uses the "style of scanning" to normalise the individual's VSBs on emotional and non-emotional stimuli. Method 2 uses RNNs in a sequence-to-sequence configuration to learn the individual's "style of scanning". The trained model is then used to create vector representations that contain information on the individual's "style of scanning" (content independent) and her/his VSBs (content dependent) on emotional and non-emotional stimuli. The distance between these vector representations is used by a logistic regression classifier to characterise patients as either apathetic or non-apathetic. Using Method 2 the AUC of the classifier improved to 0.814. The results presented suggest that using RNNs to analyse differences between VSBs on emotional and non-emotional stimuli (a measure of emotional blunting) can improve objective detection of apathy in individual patients with AD.
Assessing Candidate Preference through Web Browsing History
Authors: Giovanni Comarela, Ramakrishnan Durairajan, Paul Barford, Dino P. Christenson, Mark Crovella
Venue: KDD
Year: 2018

Predicting election outcomes is of considerable interest to candidates, political scientists, and the public at large. We propose the use of Web browsing history as a new indicator of candidate preference among the electorate, one that has potential to overcome a number of the drawbacks of election polls. However, there are a number of challenges that must be overcome to effectively use Web browsing for assessing candidate preference - including the lack of suitable ground truth data and the heterogeneity of user populations in time and space. We address these challenges, and show that the resulting methods can shed considerable light on the dynamics of voters' candidate preferences in ways that are difficult to achieve using polls.
Pangloss: Fast Entity Linking in Noisy Text Environments
Authors: Michael D. Conover, Matthew Hayes, Scott Blackburn, Pete Skomoroch, Sam Shah
Venue: KDD
Year: 2018

Entity linking is the task of mapping potentially ambiguous terms in text to their constituent entities in a knowledge base like Wikipedia. This is useful for organizing content, extracting structured data from textual documents, and in machine learning relevance applications like semantic search, knowledge graph construction, and question answering. Traditionally, this work has focused on text that has been well-formed, like news articles, but in common real world datasets such as messaging, resumes, or short-form social media, non-grammatical, loosely-structured text adds a new dimension to this problem. This paper presents Pangloss, a production system for entity disambiguation on noisy text. Pangloss combines a probabilistic linear-time key phrase identification algorithm with a semantic similarity engine based on context-dependent document embeddings to achieve better than state-of-the-art results (>5% in F1) compared to other research or commercially available systems. In addition, Pangloss leverages a local embedded database with a tiered architecture to house its statistics and metadata, which allows rapid disambiguation in streaming contexts and on-device disambiguation in low-memory environments such as mobile phones.
Node Similarity with q -Grams for Real-World Labeled Networks
Authors: Alessio Conte, Gaspare Ferraro, Roberto Grossi, Andrea Marino, Kunihiko Sadakane, Takeaki Uno
Venue: KDD
Year: 2018

We study node similarity in labeled networks, using the label sequences found in paths of bounded length q leading to the nodes. (This recalls the q-grams employed in document resemblance, based on the Jaccard distance.) When applied to networks, the challenge is two-fold: the number of q-grams generated from labeled paths grows exponentially with q, and their frequency should be taken into account: this leads to a variation of the Jaccard index known as Bray-Curtis index for multisets. We describe nSimGram, a suite of fast algorithms for node similarity with q-grams, based on a novel blend of color coding, probabilistic counting, sketches, and string algorithms, where the universe of elements to sample is exponential. We provide experimental evidence that our measure is effective and our running times scale to deal with large real-world networks.
An Empirical Evaluation of Sketching for Numerical Linear Algebra
Authors: Yogesh Dahiya, Dimitris Konomis, David P. Woodruff
Venue: KDD
Year: 2018

Over the last ten years, tremendous speedups for problems in randomized numerical linear algebra such as low rank approximation and regression have been made possible via the technique of randomized data dimensionality reduction, also known as sketching. In theory, such algorithms have led to optimal input sparsity time algorithms for a wide array of problems. While several scattered implementations of such methods exist, the goal of this work is to provide a comprehensive comparison of such methods to alternative approaches. We investigate least squares regression, iteratively reweighted least squares, logistic regression, robust regression with Huber and Bisquare loss functions, leverage score computation, Frobenius norm low rank approximation, and entrywise $\ell_1$-low rank approximation. We give various implementation techniques to speed up several of these algorithms, and the resulting implementations demonstrate the tradeoffs of such techniques in practice.
Automated Audience Segmentation Using Reputation Signals
Authors: Maria Daltayanni, Ali Dasdan, Luca de Alfaro
Venue: KDD
Year: 2018

Selecting the right audience for an advertising campaign is one of the most challenging, time-consuming and costly steps in the advertising process. To target the right audience, advertisers usually have two options: a) market research to identify user segments of interest and b) sophisticated machine learning models trained on data from past campaigns. In this paper we study how demand-side platforms (DSPs) can leverage the data they collect (demographic and behavioral) in order to learn reputation signals about end user convertibility and advertisement (ad) quality. In particular, we propose a reputation system which learns interest scores about end users, as an additional signal of ad conversion, and quality scores about ads, as a signal of campaign success. Then our model builds user segments based on a combination of demographic, behavioral and the new reputation signals and recommends transparent targeting rules that are easy for the advertiser to interpret and refine. We perform an experimental evaluation on industry data that showcases the benefits of our approach for both new and existing advertiser campaigns.
Data Science at Flipkart - An Indian E-Commerce company
Authors: Mayur Datar
Venue: KDD
Year: 2018

In this talk we will give a very brief overview of Flipkart, highlighting the major milestones in its journey so far and some latest market size numbers. We will enumerate some of the important data science challenges in an e-commerce company like ours. Finally we will cover one of the problems, demand forecasting, in some details to highlight some of our recent work: Accurate demand forecasts can help on-line retail organizations better plan their supply-chain processes. The challenge, however, is the large number of associative factors that result in large, non-stationary shifts in demand, which traditional time series and regression approaches fail to model. We propose a Neural Network architecture called AR-MDN, that simultaneously models associative factors, time-series trends and the variance in the demand
Adaptive Paywall Mechanism for Digital News Media
Authors: Heidar Davoudi, Aijun An, Morteza Zihayat, Gordon Edall
Venue: KDD
Year: 2018

Many online news agencies utilize the paywall mechanism to increase reader subscriptions. This method offers a non-subscribed reader a fixed number of free articles in a period of time (e.g., a month), and then directs the user to the subscription page for further reading. We argue that there is no direct relationship between the number of paywalls presented to readers and the number of subscriptions, and that this artificial barrier, if not used well, may disengage potential subscribers and thus may not well serve its purpose of increasing revenue. Moreover, the current paywall mechanism neither considers the user browsing history nor the potential articles which the user may visit in the future. Thus, it treats all readers equally and does not consider the potential of a reader in becoming a subscriber. In this paper, we propose an adaptive paywall mechanism to balance the benefit of showing an article against that of displaying the paywall (i.e., terminating the session). We first define the notion of cost and utility that are used to define an objective function for optimal paywall decision making. Then, we model the problem as a stochastic sequential decision process. Finally, we propose an efficient policy function for paywall decision making. The experimental results on a real dataset from a major newspaper in Canada show that the proposed model outperforms the traditional paywall mechanism as well as the other baselines.
Transfer Learning via Feature Isomorphism Discovery
Authors: Shimin Di, Jingshu Peng, Yanyan Shen, Lei Chen
Venue: KDD
Year: 2018

Transfer learning has gained increasing attention due to the inferior performance of machine learning algorithms with insufficient training data. Most of the previous homogeneous or heterogeneous transfer learning works aim to learn a mapping function between feature spaces based on the inherent correspondence across the source and target domains or labeled instances. However, in many real world applications, existing methods may not be robust when the correspondence across domains is noisy or labeled instances are not representative. In this paper, we develop a novel transfer learning framework called Transfer Learning via Feature Isomorphism Discovery (abbreviated to TLFid), which owns high tolerance for noisy correspondence between domains as well as scarce or non-existing labeled instances. More specifically, we propose a feature isomorphism approach to discovering common substructures across feature spaces and learning a feature mapping function from the target domain to the source domain. We evaluate the performance of TLFid on the cross-lingual sentiment classification tasks. The results show that our method achieves significant improvement in terms of accuracy compared with the state-of-the-art methods.
Releasing eHealth Analytics into the Wild: Lessons Learnt from the SPHERE Project
Authors: Tom Diethe, Mike Holmes, Meelis Kull, Miquel Perelló-Nieto, Kacper Sokol, Hao Song, Emma Tonkin, Niall Twomey, Peter A. Flach
Venue: KDD
Year: 2018

The SPHERE project is devoted to advancing eHealth in a smart-home context, and supports full-scale sensing and data analysis to enable a generic healthcare service. We describe, from a data-science perspective, our experience of taking the system out of the laboratory into more than thirty homes in Bristol, UK. We describe the infrastructure and processes that had to be developed along the way, describe how we train and deploy Machine Learning systems in this context, and give a realistic appraisal of the state of the deployed systems.
Semi-Supervised Generative Adversarial Network for Gene Expression Inference
Authors: Kamran Ghasedi Dizaji, Xiaoqian Wang, Heng Huang
Venue: KDD
Year: 2018

Gene expression profiling provides comprehensive characterization of cellular states under different experimental conditions, thus contributes to the prosperity of many fields of biomedical research. Although the rapid development of gene expression profiling has been observed, genome-wide profiling of large libraries is still expensive and difficult. Due to the fact that there are significant correlations between gene expression patterns, previous studies introduced regression models for predicting the target gene expressions from the landmark gene profiles. These models formulate the gene expression inference in a completely supervised manner, which require a large labeled dataset (i.e. paired landmark and target gene expressions). However, collecting the whole gene expressions is much more expensive than the landmark genes. In order to address this issue and take advantage of cheap unlabeled data (i.e. landmark genes), we propose a novel semi-supervised deep generative model for target gene expression inference. Our model is based on the generative adversarial network (GAN) to approximate the joint distribution of landmark and target genes, and an inference network to learn the conditional distribution of target genes given the landmark genes. We employ the reliable generated data by our GAN model as the extra training pairs to improve the training of our inference model, and utilize the trustworthy predictions of the inference network to enhance the adversarial training of our GAN network. We evaluate our model on the prediction of two types of gene expression data and identify obvious advantage over the counterparts.
Multi-view Adversarially Learned Inference for Cross-domain Joint Distribution Matching
Authors: Changying Du, Changde Du, Xingyu Xie, Chen Zhang, Hao Wang
Venue: KDD
Year: 2018

Many important data mining problems can be modeled as learning a (bidirectional) multidimensional mapping between two data domains. Based on the generative adversarial networks (GANs), particularly conditional ones, cross-domain joint distribution matching is an increasingly popular kind of methods addressing such problems. Though significant advances have been achieved, there are still two main disadvantages of existing models, i.e., the requirement of large amount of paired training samples and the notorious instability of training. In this paper, we propose a multi-view adversarially learned inference (ALI) model, termed as MALI, to address these issues. Unlike the common practice of learning direct domain mappings, our model relies on shared latent representations of both domains and can generate arbitrary number of paired faking samples, benefiting from which usually very few paired samples (together with sufficient unpaired ones) is enough for learning good mappings. Extending the vanilla ALI model, we design novel discriminators to judge the quality of generated samples (both paired and unpaired), and provide theoretical analysis of our new formulation. Experiments on image-to-image translation, image-to-attribute generation (multi-label classification), attribute-to-image generation tasks demonstrate that our semi-supervised learning framework yields significant performance improvements over existing ones. Results on cross-modality retrieval show that our latent space based method can achieve competitive similarity search performance in relative fast speed, compared to those methods that compute similarities in the high-dimensional data space.
FASTEN: Fast Sylvester Equation Solver for Graph Mining
Authors: Boxin Du, Hanghang Tong
Venue: KDD
Year: 2018

The Sylvester equation offers a powerful and unifying primitive for a variety of important graph mining tasks, including network alignment, graph kernel, node similarity, subgraph matching, etc. A major bottleneck of Sylvester equation lies in its high computational complexity. Despite tremendous effort, state-of-the-art methods still require a complexity that is at least \em quadratic in the number of nodes of graphs, even with approximations. In this paper, we propose a family of Krylov subspace based algorithms (\fasten) to speed up and scale up the computation of Sylvester equation for graph mining. The key idea of the proposed methods is to project the original equivalent linear system onto a Kronecker Krylov subspace. We further exploit (1) the implicit representation of the solution matrix as well as the associated computation, and (2) the decomposition of the original Sylvester equation into a set of inter-correlated Sylvester equations of smaller size. The proposed algorithms bear two distinctive features. First, they provide the \em exact solutions without any approximation error. Second, they significantly reduce the time and space complexity for solving Sylvester equation, with two of the proposed algorithms having a \em linear complexity in both time and space. Experimental evaluations on a diverse set of real networks, demonstrate that our methods (1) are up to $10,000\times$ faster against Conjugate Gradient method, the best known competitor that outputs the exact solution, and (2) scale up to million-node graphs.
The Pinterest Approach to Machine Learning
Authors: Li Fan
Venue: KDD
Year: 2018

Pinterest's mission is to help you discover and do what you love -- whether that's finding the perfect recipe for your family dinner or pulling together an outfit. To achieve this level of personalization, and with 200M+ active users and billions of recommendations every day, we live on machine learning. From object detection and classification to ads auction model tuning, Machine learning is used in numerous components of our system. With limited resources as a medium-sized company, but fast growing demand from passionate users, we have to balance cutting edge technology advancement with practical system implementation that can be put in place within a short amount of time. In this talk, I will review Pinterest's approach of a careful balance between simplicity and functionality, and how we reached our current stage of system design.
Scalable Active Learning by Approximated Error Reduction
Authors: Weijie Fu, Meng Wang, Shijie Hao, Xindong Wu
Venue: KDD
Year: 2018

We study the problem of active learning for multi-class classification on large-scale datasets. In this setting, the existing active learning approaches built upon uncertainty measures are ineffective for discovering unknown regions, and those based on expected error reduction are inefficient owing to their huge time costs. To overcome the above issues, this paper proposes a novel query selection criterion called approximated error reduction (AER). In AER, the error reduction of each candidate is estimated based on an expected impact over all datapoints and an approximated ratio between the error reduction and the impact over its nearby datapoints. In particular, we utilize hierarchical anchor graphs to construct the candidate set as well as the nearby datapoint sets of these candidates. The benefit of this strategy is that it enables a hierarchical expansion of candidates with the increase of labels, and allows us to further accelerate the AER estimation. We finally introduce AER into an efficient semi-supervised classifier for scalable active learning. Experiments on publicly available datasets with the sizes varying from thousands to millions demonstrate the effectiveness of our approach.
Device Graphing by Example
Authors: Keith Funkhouser, Matthew Malloy, Enis Ceyhun Alp, Phillip Poon, Paul Barford
Venue: KDD
Year: 2018

Datasets that organize and associate the many identifiers produced by PCs, smartphones, and tablets accessing the internet are referred to as internet device graphs . In this paper, we demonstrate how measurement, tracking, and other internet entities can associate multiple identifiers with a single device or user after coarse associations, e.g ., based on IP-colocation , are made. We employ a Bayesian similarity algorithm that relies on examples of pairs of identifiers and their associated telemetry, including user agent, screen size, and domains visited, to establish pair-wise scores. Community detection algorithms are applied to group identifiers that belong to the same device or user. We train and validate our methodology using a unique dataset collected from a client panel with full visibility, apply it to a dataset of 700 million device identifiers collected over the course of six weeks in the United States, and show that it outperforms several unsupervised learning approaches. Results show mean precision and recall exceeding 90% for association of identifiers at both the device and user levels.
Self-Paced Network Embedding
Authors: Hongchang Gao, Heng Huang
Venue: KDD
Year: 2018

Network embedding has attracted increasing attention in recent data mining research with many real-world applications. Network embedding is to learn low-dimensional representations for nodes in a network. A popular kind of existing methods, such as DeepWalk, Node2Vec, and LINE, learn node representations by pushing positive context node to the anchor node while pushing negative context nodes away from it in the low-dimensional vector space. When sampling the negative context nodes, they usually employ a predefined sampling distribution based on the node popularity. However, this sampling distribution often fails to capture the real informativeness of each node and cannot reflect the training state. To address these important problems, in this paper, we propose a novel self-paced network embedding method. Specifically, our method can adaptively capture the informativeness of each node based on the current training state, and sample negative context nodes in terms of their informativeness. The proposed self-paced sampling strategy can gradually select difficult negative context nodes with training process going on to learn better node representations. Moreover, to better capture the node informativeness for learning node representations, we extend our method to the generative adversarial network framework, which has the larger capacity to discover node informativeness. The extensive experiments have been conducted on the benchmark network datasets to validate the effectiveness of our proposed methods.