OpenRaiser/Intern-Atlas huggingface dataset:
Catalysis Clustering with GAN by Incorporating Domain Knowledge
Authors: Olga Andreeva, Wei Li 0121, Wei Ding 0003, Marieke L. Kuijjer, John Quackenbush, Ping Chen 0001
Venue: KDD
Year: 2020

Clustering is an important unsupervised learning method with serious challenges when data is sparse and high-dimensional. Generated clusters are often evaluated with general measures, which may not be meaningful or useful for practical applications and domains. Using a distance metric, a clustering algorithm searches through the data space, groups close items into one cluster, and assigns far away samples to different clusters. In many real-world applications, the number of dimensions is high and data space becomes very sparse. Selection of a suitable distance metric is very difficult and becomes even harder when categorical data is involved. Moreover, existing distance metrics are mostly generic, and clusters created based on them will not necessarily make sense to domain-specific applications. One option to address these challenges is to integrate domain-defined rules and guidelines into the clustering process. In this work we propose a GAN-based approach called Catalysis Clustering to incorporate domain knowledge into the clustering process. With GANs we generate catalysts, which are special synthetic points drawn from the original data distribution and verified to improve clustering quality when measured by a domain-specific metric. We then perform clustering analysis using both catalysts and real data. Final clusters are produced after catalyst points are removed. Experiments on two challenging real-world datasets clearly show that our approach is effective and can generate clusters that are meaningful and useful for real-world applications.
Learning Instrument Invariant Characteristics for Generating High-resolution Global Coral Reef Maps
Authors: Ata Akbari Asanjan, Kamalika Das, Alan S. Li, Ved Chirayath, Juan Torres-Perez, Soroosh Sorooshian
Venue: KDD
Year: 2020

Coral reefs are one of the most biologically complex and diverse ecosystems within the shallow marine environment. Unfortunately, these underwater ecosystems are threatened by a number of anthropogenic challenges, including ocean acidification and warming, overfishing, and the continued increase of marine debris in oceans. This requires a comprehensive assessment of the world's coastal environments, including a quantitative analysis on the health and extent of coral reefs and other associated marine species, as a vital Earth Science measurement. However, limitations in observational and technological capabilities inhibit global sustained imaging of the marine environment. Harmonizing multimodal data sets acquired using different remote sensing instruments presents additional challenges, thereby limiting the availability of good quality labeled data for analysis. In this work, we develop a deep learning model for extracting domain invariant features from multimodal remote sensing imagery and creating high-resolution global maps of coral reefs by combining various sources of imagery and limited hand-labeled data available for certain regions. This framework allows us to generate, for the first time, coral reef segmentation maps at 2-meter resolution, which is a significant improvement over the kilometer-scale state-of-the-art maps. Additionally, this framework doubles accuracy and IoU metrics over baselines that do not account for domain invariance.
USAD: UnSupervised Anomaly Detection on Multivariate Time Series
Authors: Julien Audibert, Pietro Michiardi, Frédéric Guyard, Sébastien Marti, Maria A. Zuluaga
Venue: KDD
Year: 2020

The automatic supervision of IT systems is a current challenge at Orange. Given the size and complexity reached by its IT operations, the number of sensors needed to obtain measurements over time, used to infer normal and abnormal behaviors, has increased dramatically making traditional expert-based supervision methods slow or prone to errors. In this paper, we propose a fast and stable method called UnSupervised Anomaly Detection for multivariate time series (USAD) based on adversely trained autoencoders. Its autoencoder architecture makes it capable of learning in an unsupervised way. The use of adversarial training and its architecture allows it to isolate anomalies while providing fast training. We study the properties of our methods through experiments on five public datasets, thus demonstrating its robustness, training speed and high anomaly detection performance. Through a feasibility study using Orange's proprietary data we have been able to validate Orange's requirements on scalability, stability, robustness, training speed and high performance.
Block Model Guided Unsupervised Feature Selection
Authors: Zilong Bai, Hoa Nguyen, Ian Davidson
Venue: KDD
Year: 2020

Feature selection is a core area of data mining with a recent innovation of graph-driven unsupervised feature selection for linked data. In this setting we have a dataset Y consisting of n instances each with m features and a corresponding n node graph (whose adjacency matrix is A) with an edge indicating that the two instances are similar. Existing efforts for unsupervised feature selection on attributed networks have explored either directly regenerating the links by solving for f such that f(yi,yj) ~ Ai,j or finding community structure in A and using the features in Y to predict these communities. However, graph-driven unsupervised feature selection remains an understudied area with respect to exploring more complex guidance. Here we take the novel approach of first building a block model on the graph and then using the block model for feature selection. That is, we discover FMFT ~ A and then find a subset of features S that induces another graph to preserve both F and M. We call our approach Block Model Guided Unsupervised Feature Selection (BMGUFS). Experimental results show that our method outperforms the state of the art on several real-world public datasets in finding high-quality features for clustering.
Data Science for the Real Estate Industry
Authors: Ron Bekkerman, Vanja Josifovski, Foster J. Provost
Venue: KDD
Year: 2020

World's major industries, such as Financial Services, Telecom, Advertising, Healthcare, Education, etc, have attracted the attention of the KDD community for decades. Hundreds of KDD papers have been published on topics related to these industries and dozens of workshops organized---some of which have become an integral part of the conference agenda (e.g. the Health Day). Somewhat unexpectedly, the KDD conference has barely addressed the real estate industry, despite its enormous size and prominence. The reason for that apparent mismatch is two-fold: (a) until recently, the real estate industry did not appreciate the value data science methods could add (with some exceptions, such as econometrics methods for creating real-estate price indices); (b) the Data Science community has not been aware of challenging real estate problems that are perfectly suited to its methods. This tutorial provides a step towards resolving this issue. We provide an introduction to real estate for data scientists, and outline a spectrum of data science problems, many of which are being tackled by new "prop-tech" companies, while some are yet to be approached. We present concrete examples from three of these companies (where the authors work): Airbnb -- the most popular short-term rental marketplace, Cherre -- a real estate data integration platform, and Compass -- the largest independent real estate brokerage in the U.S.
Accessible Online Meetings and Presentations
Authors: Brianna Blaser
Venue: KDD
Year: 2020

In our current situation, conferences, classes, and meetings are moving online. What steps can you take to ensure that your activities are welcoming to a diverse audience, including people with disabilities? This presentation will look at proactive strategies you can take to ensure that your meetings and presentations are accessible to a wide audience. We'll talk about communication with participants, preparation, presentation materials, technology, and accommodations.
Treatment Policy Learning in Multiobjective Settings with Fully Observed Outcomes
Authors: Soorajnath Boominathan, Michael Oberst, Helen Zhou, Sanjat Kanjilal, David A. Sontag
Venue: KDD
Year: 2020

In several medical decision-making problems, such as antibiotic prescription, laboratory testing can provide precise indications for how a patient will respond to different treatment options. This enables us to "fully observe" all potential treatment outcomes, but while present in historical data, these results are infeasible to produce in real-time at the point of the initial treatment decision. Moreover, treatment policies in these settings often need to trade off between multiple competing objectives, such as effectiveness of treatment and harmful side effects. We present, compare, and evaluate three approaches for learning individualized treatment policies in this setting: First, we consider two indirect approaches, which use predictive models of treatment response to construct policies optimal for different trade-offs between objectives. Second, we consider a direct approach that constructs such a set of policies without intermediate models of outcomes. Using a medical dataset of Urinary Tract Infection (UTI) patients, we show that all approaches learn policies that achieve strictly better performance on all outcomes than clinicians, while also trading off between different objectives. We demonstrate additional benefits of the direct approach, including flexibly incorporating other goals such as deferral to physicians on simple cases.
Keynote Speaker: Emery N. Brown
Authors: Emery N. Brown
Venue: KDD
Year: 2020

Emery Brown, M.D., Ph.D. is an American statistician, neuroscientist, and anesthesiologist. He is the Warren M. Zapol Professor of Anesthesia at Harvard Medical School and at Massachusetts General Hospital(MGH), and a practicing anesthesiologist at MGH. At MIT he is the Edward Hood Taplin Professor of Medical Engineering and professor of computational neuroscience, the Associate Director of the Institute for Medical Engineering and Science, and the Director of the Harvard-MIT Program in Health Sciences and Technology. Brown is one of only 19 individuals who has been elected to all three branches of the National Academies of Sciences, Engineering, and Medicine, Brown is also the first African American and first anesthesiologist to be elected to all three National Academies.
Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data
Authors: Chloë Brown, Jagmohan Chauhan, Andreas Grammenos, Jing Han 0010, Apinan Hasthanasombat, Dimitris Spathis, Tong Xia, Pietro Cicuta, Cecilia Mascolo
Venue: KDD
Year: 2020

Audio signals generated by the human body (e.g., sighs, breathing, heart, digestion, vibration sounds) have routinely been used by clinicians as indicators to diagnose disease or assess disease progression. Until recently, such signals were usually collected through manual auscultation at scheduled visits. Research has now started to use digital technology to gather bodily sounds (e.g., from digital stethoscopes) for cardiovascular or respiratory examination, which could then be used for automatic analysis. Some initial work shows promise in detecting diagnostic signals of COVID-19 from voice and coughs. In this paper we describe our data analysis over a large-scale crowdsourced dataset of respiratory sounds collected to aid diagnosis of COVID-19. We use coughs and breathing to understand how discernible COVID-19 sounds are from those in asthma or healthy controls. Our results show that even a simple binary machine learning classifier is able to classify correctly healthy and COVID-19 sounds. We also show how we distinguish a user who tested positive for COVID-19 and has a cough from a healthy user with a cough, and users who tested positive for COVID-19 and have a cough from users with asthma and a cough. Our models achieve an AUC of above 80% across all tasks. These results are preliminary and only scratch the surface of the potential of this type of data and audio-based machine learning. This work opens the door to further investigation of how automatically analysed respiratory patterns could be used as pre-screening signals to aid COVID-19 diagnosis.
Tutorial on Human-Centered Explainability for Healthcare
Authors: Prithwish Chakraborty, Bum Chul Kwon, Sanjoy Dey, Amit Dhurandhar, Daniel M. Gruen, Kenney Ng, Daby Sow, Kush R. Varshney
Venue: KDD
Year: 2020

In recent years, the rapid advances in Artificial Intelligence (AI) techniques along with an ever-increasing availability of healthcare data have made many novel analyses possible. Significant successes have been observed in a wide range of tasks such as next diagnosis prediction, AKI prediction, adverse event predictions including mortality and unexpected hospital re-admissions. However, there has been limited adoption and use in the clinical practice of these methods due to their black-box nature. A significant amount of research is currently focused on making such methods more interpretable or to make post-hoc explanations more accessible. However, most of this work is done at a very low level and as a result, may not have a direct impact at the point-of-care. This tutorial will provide an overview of the landscape of different approaches that have been developed for explainability in healthcare. Specifically, we will present the problem of explainability as it pertains to various personas involved in healthcare viz. data scientists, clinical researchers, and clinicians. We will chart out the requirements for such personas and present an overview of the different approaches that can address such needs. We will also walk-through several use-cases for such approaches. In this process, we will provide a brief introduction to explainability, charting its different dimensions as well as covering some relevant interpretability methods spanning such dimensions. We will touch upon some practical guides for explainability and provide a brief survey of open source tools such as the IBM AI Explainability 360 Open Source Toolkit.
Robust Deep Learning Methods for Anomaly Detection
Authors: Raghavendra Chalapathy, Nguyen Lu Dang Khoa, Sanjay Chawla
Venue: KDD
Year: 2020

Anomaly detection is an important problem that has been well-studied within diverse research areas and application domains. A robust anomaly detection system identifies rare events and patterns in the absence of labelled data. The identified patterns provide crucial insights about both the fidelity of the data and deviations in the underlying data-generating process. For example a surveillance system designed to monitor the emergence of new epidemics will use a robust anomaly detection methods to separate spurious associations from genuine indicators of an epidemic with minimal lag time.
InfiniteWalk: Deep Network Embeddings as Laplacian Embeddings with a Nonlinearity
Authors: Sudhanshu Chanpuriya, Cameron Musco
Venue: KDD
Year: 2020

The skip-gram model for learning word embeddings (Mikolov et al. 2013) has been widely popular, and DeepWalk (Perozzi et al. 2014), among other methods, has extended the model to learning node representations from networks. Recent work of Qiu et al. (2018) provides a closed-form expression for the DeepWalk objective, obviating the need for sampling for small datasets and improving accuracy. In these methods, the "window size" T within which words or nodes are considered to co-occur is a key hyperparameter. We study the objective in the limit as T goes to infinity, which allows us to simplify the expression of Qiu et al. We prove that this limiting objective corresponds to factoring a simple transformation of the pseudoinverse of the graph Laplacian, linking DeepWalk to extensive prior work in spectral graph embeddings. Further, we show that by a applying a simple nonlinear entrywise transformation to this pseudoinverse, we recover a good approximation of the finite-T objective and embeddings that are competitive with those from DeepWalk and other skip-gram methods in multi-label classification. Surprisingly, we find that even simple binary thresholding of the Laplacian pseudoinverse is often competitive, suggesting that the core advancement of recent methods is a nonlinearity on top of the classical spectral embedding approach.
Finding Effective Geo-social Group for Impromptu Activities with Diverse Demands
Authors: Lu Chen 0008, Chengfei Liu, Rui Zhou 0001, Jiajie Xu 0001, Jeffrey Xu Yu, Jianxin Li 0001
Venue: KDD
Year: 2020

Geo-social group search aims to find a group of people proximate to a location while socially related. One of the driven applications for geo-social group search is organizing an impromptu activity. This is because the social cohesiveness of a found geo-social group ensures a good communication atmosphere for the activity and the spatial closeness of the geo-social group reduces the preparation time for the activity. Most existing works treat geo-social group search as a problem that finds a group satisfying a single social constraint while optimizing the spatial proximity. However, since different impromptu activities have diverse demands on attendees, e.g. an activity could require (or prefer) the attendees to have skills (or favorites) related to the activity, the existing works cannot find this kind of geo-social groups effectively. In this paper, we propose a novel geo-social group model, equipped with elegant keyword constraints, to fill this gap. We propose a novel search framework which first significantly narrows down the search space with theoretical guarantees and then efficiently finds the optimum result. To evaluate the effectiveness, we conduct experiments on real datasets, demonstrating the superiority of our proposed model. We conduct extensive experiments on large semi-synthetic datasets for justifying the efficiency of the proposed search algorithms.
Improving Recommendation Quality in Google Drive
Authors: Suming J. Chen, Zhen Qin 0001, Zac Wilson, Brian Calaci, Michael Rose, Ryan Evans, Sean Abraham, Donald Metzler, Sandeep Tata, Mike Colagrosso
Venue: KDD
Year: 2020

Quick Access is a machine-learned system in Google Drive that predicts which files a user wants to open. Adding Quick Access recommendations to the Drive homepage cut the amount of time that users spend locating their files in half. Aggregated over the ~1 billion users of Drive, the time saved up adds up to ~1000 work weeks every day. In this paper, we discuss both the challenges of iteratively improving the quality of a personal recommendation system as well as the variety of approaches that we took in order to improve this feature. We explored different deep network architectures, novel modeling techniques, additional data sources, and the effects of latency and biases in the UX. We share both pitfalls as well as successes in our attempts to improve this product, and also discuss how we scaled and managed the complexity of the system. We believe that these insights will be especially useful to those who are working with private corpora as well as those who are building a large-scale production recommendation system.
Handling Information Loss of Graph Neural Networks for Session-based Recommendation
Authors: Tianwen Chen, Raymond Chi-Wing Wong
Venue: KDD
Year: 2020

Recently, graph neural networks (GNNs) have gained increasing popularity due to their convincing performance in various applications. Many previous studies also attempted to apply GNNs to session-based recommendation and obtained promising results. However, we spot that there are two information loss problems in these GNN-based methods for session-based recommendation, namely the lossy session encoding problem and the ineffective long-range dependency capturing problem. The first problem is the lossy session encoding problem. Some sequential information about item transitions is ignored because of the lossy encoding from sessions to graphs and the permutation-invariant aggregation during message passing. The second problem is the ineffective long-range dependency capturing problem. Some long-range dependencies within sessions cannot be captured due to the limited number of layers. To solve the first problem, we propose a lossless encoding scheme and an edge-order preserving aggregation layer based on GRU that is dedicatedly designed to process the losslessly encoded graphs. To solve the second problem, we propose a shortcut graph attention layer that effectively captures long-range dependencies by propagating information along shortcut connections. By combining the two kinds of layers, we are able to build a model that does not have the information loss problems and outperforms the state-of-the-art models on three public datasets.
Contagious Chain Risk Rating for Networked-guarantee Loans
Authors: Dawei Cheng, Zhibin Niu, Yiyi Zhang
Venue: KDD
Year: 2020

The small and medium-sized enterprises (SMEs) are allowed to guarantee each other and form complex loan networks to receive loans from banks during the economic expansion stage. However, external shocks may weaken the robustness, and an accidental default may spread across the network and lead to large-scale defaults, even systemic crisis. Thus, predicting and rating the default contagion chains in the guarantee network in order to reduce or prevent potential systemic financial risk, attracts a grave concern from the Regulatory Authority and the banks. Existing credit risk models in the banking industry utilize machine learning methods to generate a credit score for each customer. Such approaches dismiss the contagion risk from guarantee chains and need extensive feature engineering with deep domain expertise. To this end, we propose a novel approach to rate the risk of contagion chains in the bank industry with the deep neural network. We employed the temporal inter-chain attention network on graph-structured loan behavior data to compute risk scores for the contagion chains. We show that our approach is significantly better than the state-of-the-art baselines on the dataset from a major financial institution in Asia. Besides, we conducted empirical studies on the real-world loan dataset for risk assessment. The proposed approach enabled loan managers to monitor risks in a boarder view and avoid significant financial losses for the financial institution.
Interpreting and Explaining Deep Neural Networks: A Perspective on Time Series Data
Authors: Jaesik Choi
Venue: KDD
Year: 2020

Explainable and interpretable machine learning models and algorithms are important topics which have received growing attention from research, application and administration. Many complex Deep Neural Networks (DNNs) are often perceived as black-boxes. Researchers would like to be able to interpret what the DNN has learned in order to identify biases and failure models and improve models. In this tutorial, we will provide a comprehensive overview on methods to analyze deep neural networks and an insight how those interpretable and explainable methods help us understand time series data.