OpenRaiser/Intern-Atlas huggingface dataset:
Glaucoma Progression Prediction Using Retinal Thickness via Latent Space Linear Regression
Authors: Yuhui Zheng, Linchuan Xu, Taichi Kiwaki, Jing Wang, Hiroshi Murata, Ryo Asaoka, Kenji Yamanishi
Venue: KDD
Year: 2019

Prediction of glaucomatous visual field loss has significant clinical benefits because it can help with early detection of glaucoma as well as decision-making for treatments. Glaucomatous visual loss is conventionally captured through visual field sensitivity (VF ) measurement, which is costly and time-consuming. Thus, existing approaches mainly predict future VF utilizing limited VF data collected in the past. Recently, optical coherence tomography (OCT) has been adopted to measure retinal layers thickness (RT ) for considerably more low-cost treatment assistance. There then arises an important question in the context of ophthalmology: are RT measurements beneficial for VF prediction? In this paper, we propose a novel method to demonstrate the benefits provided by RT measurements. The challenge is management of the two heterogeneities of VF data and RT data as RT data are collected according to different clinical schedules and lie in a different space to VF data. To tackle these heterogeneities, we propose latent progression patterns (LPPs), a novel type of representations for glaucoma progression. Along with LPPs, we propose a method to transform VF series to an LPP based on matrix factorization and a method to transform RT series to an LPP based on deep neural networks. Partial VF and RT information is integrated in LPPs to provide accurate prediction. The proposed framework is named deeply-regularized latent-space linear regression (\em DLLR). We empirically demonstrate that our proposed method outperforms the state-of-the-art technique by 12% for the best case in terms of the mean of the root mean square error on a real dataset.
Dual Averaging Method for Online Graph-structured Sparsity
Authors: Baojian Zhou, Feng Chen, Yiming Ying
Venue: KDD
Year: 2019

Online learning algorithms update models via one sample per iteration, thus efficient to process large-scale datasets and useful to detect malicious events for social benefits, such as disease outbreak and traffic congestion on the fly. However, existing algorithms for graph-structured models focused on the offline setting and the least square loss, incapable for online setting, while methods designed for online setting cannot be directly applied to the problem of complex (usually non-convex) graph-structured sparsity model. To address these limitations, in this paper we propose a new algorithm for graph-structured sparsity constraint problems under online setting, which we call GraphDA. The key part in GraphDA is to project both averaging gradient (in dual space) and primal variables (in primal space) onto lower dimensional subspaces, thus capturing the graph-structured sparsity effectively. Furthermore, the objective functions assumed here are generally convex so as to handle different losses for online learning settings. To the best of our knowledge, GraphDA is the first online learning algorithm for graph-structure constrained optimization problems. To validate our method, we conduct extensive experiments on both benchmark graph and real-world graph datasets. Our experiment results show that, compared to other baseline methods, GraphDA not only improves classification performance, but also successfully captures graph-structured features more effectively, hence stronger interpretability.
Gold Panning from the Mess: Rare Category Exploration, Exposition, Representation, and Interpretation
Authors: Dawei Zhou, Jingrui He
Venue: KDD
Year: 2019

In contrast to the massive volume of data, it is often the rare categories that are of great importance in many high impact domains, ranging from financial fraud detection in online transaction networks to emerging trend detection in social networks, from spam image detection in social media to rare disease diagnosis in the medical decision support system. The unique challenges of rare category analysis include: (1) the highly-skewed class-membership distribution; (2) the non-separability nature of the rare categories from the majority classes; (3) the data and task heterogeneity, e.g., the multi-modal representation of examples, and the analysis of similar rare categories across multiple related tasks. This tutorial aims to provide a concise review of state-of-the-art techniques on complex rare category analysis, where the majority classes have a smooth distribution, while the minority classes exhibit a compactness property in the feature space or subspace. In particular, we start with the context, problem definition and unique challenges of complex rare category analysis; then we present a comprehensive overview of recent advances that are designed for this problem setting, from rare category exploration without any label information to the exposition step that characterizes rare examples with a compact representation, from representing rare patterns in a salient embedding space to interpreting the prediction results and providing relevant clues for the end users' interpretation; at last, we will discuss the potential challenges and shed light on the future directions of complex rare category analysis.
Understanding Consumer Journey using Attention based Recurrent Neural Networks
Authors: Yichao Zhou, Shaunak Mishra, Jelena Gligorijevic, Tarun Bhatia, Narayan Bhamidipati
Venue: KDD
Year: 2019

Paths of online users towards a purchase event (conversion) can be very complex, and guiding them through their journey is an integral part of online advertising. Studies in marketing indicate that a conversion event is typically preceded by one or more purchase funnel stages, viz., unaware, aware, interest, consideration, and intent. Intuitively, some online activities, including web searches, site visits and ad interactions, can serve as markers for the user's funnel stage. Identifying such markers can potentially refine conversion prediction, guide the design of ad creatives (text and images), and lead to higher ad effectiveness. We explore this hypothesis through a set of experiments designed for two tasks: (i) conversion prediction given a user's activity trail, and (ii) funnel stage specific targeting and creatives. To address challenges in the two tasks, we propose an attention based recurrent neural network (RNN) which ingests a user activity trail, and predicts the user's conversion probability along with attention weights for each activity (analogous to its position in the funnel). Specifically, we propose novel attention mechanisms, which maintain a global weight for each activity across all user trails, and also indicate the activity's funnel stage. Use of the proposed attention mechanisms for the first task of conversion prediction shows significant AUC lifts of 0.9% on a public dataset (RecSys 2015 challenge), and up to 3.6% on three proprietary datasets from a major advertising platform (Yahoo Gemini). To address the second task, the activity weights from the proposed mechanisms are used to automatically assign users to funnel stages via a scalable scoring method. Offline evaluation shows that such activity weights are more aligned with editorially tagged activity-funnel stages compared to weights from existing attention mechanisms and simpler conversion models like logistic regression. In addition, results of online ad campaigns in Yahoo Gemini with funnel specific user targeting and ad creatives show strong performance lifts further validating the connection across online activities, purchase funnel stages, stage-specific custom creatives, and conversions.
Robust Graph Convolutional Networks Against Adversarial Attacks
Authors: Dingyuan Zhu, Ziwei Zhang, Peng Cui, Wenwu Zhu
Venue: KDD
Year: 2019

Graph Convolutional Networks (GCNs) are an emerging type of neural network model on graphs which have achieved state-of-the-art performance in the task of node classification. However, recent studies show that GCNs are vulnerable to adversarial attacks, i.e. small deliberate perturbations in graph structures and node attributes, which poses great challenges for applying GCNs to real world applications. How to enhance the robustness of GCNs remains a critical open problem. To address this problem, we propose Robust GCN (RGCN), a novel model that "fortifies'' GCNs against adversarial attacks. Specifically, instead of representing nodes as vectors, our method adopts Gaussian distributions as the hidden representations of nodes in each convolutional layer. In this way, when the graph is attacked, our model can automatically absorb the effects of adversarial changes in the variances of the Gaussian distributions. Moreover, to remedy the propagation of adversarial attacks in GCNs, we propose a variance-based attention mechanism, i.e. assigning different weights to node neighborhoods according to their variances when performing convolutions. Extensive experimental results demonstrate that our proposed method can effectively improve the robustness of GCNs. On three benchmark graphs, our RGCN consistently shows a substantial gain in node classification accuracy compared with state-of-the-art GCNs against various adversarial attack strategies.
Finding Users Who Act Alike: Transfer Learning for Expanding Advertiser Audiences
Authors: Stephanie deWet, Jiafan Ou
Venue: KDD
Year: 2019

Audience Look-alike Targeting is an online advertising technique in which an advertiser specifies a set of seed customers and tasks the advertising platform with finding an expanded audience of similar users. We will describe a two-stage embedding-based audience expansion model that is deployed in production at Pinterest. For the first stage we trained a global user embedding model on sitewide user activity logs. In the second stage, we use transfer learning and statistical techniques to create lightweight seed list representations in the embedding space for each advertiser. We create a (user, seed list) affinity scoring function that makes use of these lightweight advertiser representations. We describe the end-to-end system that computes and serves this model at scale. Finally, we propose an ensemble approach that combines single-advertiser classifiers with the embedding-based technique. We show offline evaluation and online experiments to prove that the expanded audience generated by the ensemble model has the best results for all seed list sizes.
Deep Learning for Industrial AI: Challenges, New Methods and Best Practices
Authors: Chetan Gupta 0001, Ahmed K. Farahat
Venue: KDD
Year: 2020

Industrial AI is concerned with the application of Artificial Intelligence (AI), Machine Learning (ML) and related technologies towards addressing real-world use cases in industrial and societal domains. These uses cases can be broadly categorized into the horizontal areas of maintenance and repair, operations and supply chain, quality, safety, design, and end-to-end optimization - with applications in a variety of verticals. In the last few years, we have witnessed a growing interest in applying Deep Learning (DL) techniques to Industrial AI problems, ranging from using sequence models such as Long Short-Term Memory (LSTM) for predicting failures in equipment, to using Deep Reinforcement Learning (Deep RL) for scheduling and dispatching. Applying deep learning techniques to industrial applications imposes a set of unique challenges, which include, but are not limited to, (1) limited data, highly skewed class distribution and occurrence of rare classes such as failures, (2) multi-modal data (sensors, events, images, text, etc.) indexed over space and time (3) the need for explainable decisions, (4) a need to attain consistency between different but "related" models and between multiple generations of the same model, and (5) decision making to optimize business outcomes where the cost of a mistake could be very high. This tutorial presents an overview of these challenges, along with new methods and best practices to address them. Examples of these methods include using sequence DL models and Functional Neural Networks (FNNs) for modeling sensor and spatiotemporal measurements; using multi-task learning, graph models and ensemble learning for improving consistency of DL models; using deep RL for health indicator learning and dynamic dispatching; cost-based decision making for prognostics; and using GANs for generating senor data for prognostics. Finally, we will present some open problems in Industrial AI and how the research community can shape the future of the next industrial and societal revolution.
Context-Aware Attentive Knowledge Tracing
Authors: Aritra Ghosh 0001, Neil T. Heffernan, Andrew S. Lan
Venue: KDD
Year: 2020

Knowledge tracing (KT) refers to the problem of predicting future learner performance given their past performance in educational applications. Recent developments in KT using flexible deep neural network-based models excel at this task. However, these models often offer limited interpretability, thus making them insufficient for personalized learning, which requires using interpretable feedback and actionable recommendations to help learners achieve better learning outcomes. In this paper, we propose attentive knowledge tracing (AKT), which couples flexible attention-based neural network models with a series of novel, interpretable model components inspired by cognitive and psychometric models. AKT uses a novel monotonic attention mechanism that relates a learner's future responses to assessment questions to their past responses; attention weights are computed using exponential decay and a context-aware relative distance measure, in addition to the similarity between questions. Moreover, we use the Rasch model to regularize the concept and question embeddings; these embeddings are able to capture individual differences among questions on the same concept without using an excessive number of parameters. We conduct experiments on several real-world benchmark datasets and show that AKT outperforms existing KT methods (by up to $6%$ in AUC in some cases) on predicting future learner responses. We also conduct several case studies and show that AKT exhibits excellent interpretability and thus has potential for automated feedback and personalization in real-world educational settings.
Scientific Text Mining and Knowledge Graphs
Authors: Meng Jiang 0001, Jingbo Shang
Venue: KDD
Year: 2020

Unstructured scientific text, in various forms of textual artifacts, including manuscripts, publications, patents, and proposals, is used to store the tremendous wealth of knowledge discovered after weeks, months, and years, developing hypotheses, working in the lab or clinic, and analyzing results. A grand challenge on data mining research is to develop effective methods for transforming the scientific text into well-structured forms (e.g., ontology, taxonomy, knowledge graphs), so that machine intelligent systems can build on them for hypothesis generation and validation. In this tutorial, we provide a comprehensive overview on recent research and development in this direction. First, we introduce a series of text mining methods that extract phrases, entities, scientific concepts, relations, claims, and experimental evidence. Then we discuss methods that construct and learn from scientific knowledge graphs for accurate search, document classification, and exploratory analysis. Specifically, we focus on scalable, effective, weakly supervised methods that work on text in sciences (e.g., chemistry, biology).
Cracking Tabular Presentation Diversity for Automatic Cross-Checking over Numerical Facts
Authors: Hongwei Li 0002, Qingping Yang, Yixuan Cao 0001, Jiaquan Yao, Ping Luo 0001
Venue: KDD
Year: 2020

Tabular forms of numerical facts widely exist in the disclosure documents of vertical domains, especially the financial fields. It is also quite common that the same fact might be mentioned multiple times in different tables with diverse tabular presentation. Firm's disclosure documents are the main source of accounting information for individual investors. Its authenticity is crucial for both firms' development and investors' investment decisions. However, due to large volumes of tables, frequent updates during editing, and limited time for manual cross-checking, these facts might be inconsistent with each other even after official publishing. Such errors may bring about huge reputational risk, and even economic losses even if the mistakes are made unintentionally instead of deliberately. Hence, it creates an opportunity for Automatic Numerical Cross-Checking over Tables. This paper introduces the key module of such a system, which aims to identify whether a pair of table cells are semantically equivalent, namely referring to the same fact. We observed that due to tabular presentation diversity the facts in tabular forms are difficult to be parsed into relational tuples. Thus, we present an end-to-end solution of binary classification over each pair of table cells, which does not involve with explicit semantic parsing over tables. Also, we discuss the design of this neural model to compromise between prediction accuracy and inference time for a large number of table cell pairs, and propose some practical techniques to address the issue of extreme classification imbalance among pairs. Experiments show that our model achieves macro F1 = 0.8297 in linking semantically equivalent table cells from the IPO prospectus. Finally, an auditing tool is built to support guided cross-checking over financial documents, reducing work hours by 52% ~ 68%. This system has received wide recognition in the Chinese financial community. Nine of the top ten Chinese security brokers have adopted this system to support their business of investment banking.
Fitbit for Chickens?: Time Series Data Mining Can Increase the Productivity of Poultry Farms
Authors: Alireza Abdoli, Sara Alaee, Shima Imani, Amy C. Murillo, Alec C. Gerry, Leslie Hickle, Eamonn J. Keogh
Venue: KDD
Year: 2020

Chickens are the most important poultry species in the world. Globally, industrial-scale production systems account for most of the poultry meat and eggs produced. The welfare of these birds matters for both ethical and economic reasons. From an ethical perspective, poultry have a sufficient degree of awareness to suffer pain if their health is poor, or deprivation if poorly housed. From an economic viewpoint, consumers increasingly value poultry welfare, so better market access can be obtained by producers who demonstrate concern for their flocks. Recent advances in sensor technology has allowed the opportunity to record behavioral patterns in chickens, and several research groups have shown that such data can be exploited to enhance chicken welfare. However, classifying chicken behaviors poses several unique challenges which are not observed in the UCR archive or other classic benchmark collections. In particular, some behaviors are manifested in the shape of the subsequences, whereas others only in more abstract features. Most algorithms only work well for one such modality. In addition, our data of interest has classes that greatly differ in duration, and are only weakly labeled, again defying the assumptions of the classic benchmark datasets. In this work, we propose a general-purpose framework to robustly learn and classify from datasets exhibiting these issues. While our experience is with fowl, the lessons we have learned may be more generally applicable to real-world datasets in other domains including manufacturing and human health.
Physics Inspired Models in Artificial Intelligence
Authors: Muhammad Aurangzeb Ahmad, Sener Özönder
Venue: KDD
Year: 2020

Ideas originating in physics have informed progress in artificial intelligence and machine learning for many decades. However the pedigree of many such ideas is oft neglected in the Computer Science community. The tutorial focuses on current and past ideas from physics that have helped in furthering AI and machine learning. Recent advances in physics inspired ideas in AI are also explored especially how insights from physics may hold the promise of opening the black box of deep learning. Lastly, current and future trends in this area and outlines of a research agenda on how physics-inspired models can benefit AI machine learning is given.
WeightGrad: Geo-Distributed Data Analysis Using Quantization for Faster Convergence and Better Accuracy
Authors: Syeda Nahida Akter, Muhammad Abdullah Adnan
Venue: KDD
Year: 2020

High network communication cost for synchronizing weights and gradients in geo-distributed data analysis consumes the benefits of advancement in computation and optimization techniques. Many quantization methods for weight, gradient or both have been proposed in recent years where weight-quantized model suffers from error related to weight dimension and gradient-quantized method suffers from slow convergence rate by a factor related to the gradient quantization resolution and gradient dimension. All these methods have been proved to be infeasible in terms of distributed training across multiple data centers all over the world. Moreover recent studies show that communicating over WANs can significantly degrade DNN model performance by upto 53.7x because of unstable and limited WAN bandwidth. Our goal in this work is to design a geo-distributed Deep-Learning system that (1) ensures efficient and faster communication over LAN and WAN and (2) maintain accuracy and convergence for complex DNNs with billions of parameters. In this paper, we introduce WeightGrad which acknowledges the limitations of quantization and provides loss-aware weight-quantized networks with quantized gradients for local convergence and for global convergence it dynamically eliminates insignificant communication between data centers while still guaranteeing the correctness of DNN models. Our experiments on our developed prototypes of WeightGrad running across 3 Amazon EC2 global regions and on a cluster that emulates EC2 WAN bandwidth show that WeightGrad provides 1.06% gain in top-1 accuracy, 5.36x speedup over baseline and 1.4x-2.26x over the four state-of-the-art distributed ML systems.
Toward Responsible AI by Planning to Fail
Authors: Saleema Amershi
Venue: KDD
Year: 2020

The potential for AI technologies to enhance human capabilities and improve our lives is of little debate; yet, neither is their potential to cause harm and social disruption. While preventing or minimizing AI biases and harms is justifiably the subject of intense study in academic, industrial and even legal communities, an approach centered on acknowledging and planning for AI-based failures has the potential to shed new light on how to develop and deploy responsible AI-based systems.
Diversity and Inclusion, a Perspective from a Four Years MSI Faculty Member
Authors: Eliana Valenzuela Andrade
Venue: KDD
Year: 2020

This presentation provides information on the path taken by a faculty member at a Minority Serving Institution (MSI). She presents the difficulties and opportunities that have been presented to her and, as a role model she has managed to improve the quality of life of her students.