OpenRaiser/Intern-Atlas huggingface dataset:
Dealing with Bias and Fairness in Data Science Systems: A Practical Hands-on Tutorial
Authors: Pedro Saleiro, Kit T. Rodolfa, Rayid Ghani
Venue: KDD
Year: 2020

Tackling issues of bias and fairness when building and deploying data science systems has received increased attention from the research community in recent years, yet a lot of the research has focused on theoretical aspects and very limited set of application areas and data sets. There is a lack of 1) practical training materials, 2) methodologies, and 3) tools for researchers and developers working on real-world algorithmic decision making system to deal with issues of bias and fairness. Today, treating bias and fairness as primary metrics of interest, and building, selecting, and validating models using those metrics is not standard practice for data scientists. In this hands-on tutorial we will try to bridge the gap between research and practice, by deep diving into algorithmic fairness, from metrics and definitions to practical case studies, including bias audits using the Aequitas toolkit (http://github.com/dssg/aequitas). By the end of this hands-on tutorial, the audience will be familiar with bias mitigation frameworks and tools to help them making decisions during a project based on intervention and deployment contexts in which their system will be used.
Using Machine Learning to Detect Cancer Early
Authors: Jan Schellenberger
Venue: KDD
Year: 2020

GRAIL's mission is to detect cancer early, when it can be cured. Building a classifier that can detect cancer early in a clinical setting is a complicated endeavor with unique challenges: data acquisition and stabilization can take years; cancer status (labels) can be ambiguous, noisy, and changing; sequencing data can be enormous and presents scaling issues. Because the cancer early detection machine learning classifier is being built in the context of a clinical trial environment, an extra level of rigor and planning is required.
Introduction to Computer Vision and Real Time Deep Learning-based Object Detection
Authors: James G. Shanahan, Liang Dai 0003
Venue: KDD
Year: 2020

Computer vision (CV) is a field of artificial intelligence that trains computers to interpret and understand the visual world for a variety of exciting downstream tasks such as self-driving cars, checkout-less shopping, smart cities, cancer detection, and more. The field of CV has been revolutionized by deep learning over the last decade. This tutorial looks under the hood of modern day CV systems, and builds out some of these tech pipelines in a Jupyter Notebook using Python, OpenCV, Keras and Tensorflow. While the primary focus is on digital images from cameras and videos, this tutorial will also introduce 3D point clouds, and classification and segmentation algorithms for processing them.
In Search for a Cure: Recommendation With Knowledge Graph on CORD-19
Authors: Iris Shen, Le Zhang, Jianxun Lian, Chieh-Han Wu, Miguel González-Fierro, Andreas Argyriou, Tao Wu
Venue: KDD
Year: 2020

The whole globe has cranked up for coping with the COVID-19 situation. The hands-on tutorial targets at providing a comprehensive and pragmatic end-to-end walk-through for building an academic research paper recommender for the use case of COVID-19 related study, with the help of knowledge graph technology. The code examples that demonstrate the theories are reproducible and can hopefully provide value for researchers to build tools that support conducting research to find a cure to COVID-19.
Semi-Supervised Multi-Label Learning from Crowds via Deep Sequential Generative Model
Authors: Wanli Shi, Victor S. Sheng, Xiang Li 0012, Bin Gu 0001
Venue: KDD
Year: 2020

Multi-label classification (MLC) is pervasive in real-world applications. Conventional MLC algorithms assume that enough ground truth labels are available for training a classifier. While in reality, obtaining ground truth labels is expensive and time-consuming. In the field of data mining, it is more efficient to use crowdsourcing for label collection. In this setting, an MLC algorithm needs to deal with the noisiness of the crowdsourced labels as well as the remaining massive unlabeled data. In this paper, we propose a deep generative model to describe the label generation process for this semi-supervised multi-label learning problem. Although deep generative models are widely used for MLC problems, no previous work could address the noisy crowdsourced multi-labels and unlabeled data simultaneously. To address this challenging problem, our novel generative model incorporates latent variables to describe the labeled/unlabeled data as well as the labeling process of crowdsourcing. We introduce an efficient sequential inference model to approximate the model posterior and infer the ground truth labels. Our experimental results on various scales of datasets demonstrate the effectiveness of our proposed model. It performs favorably against four state-of-the-art deep generative models.
Simulating the Impact of Hospital Capacity and Social Isolation to Minimize the Propagation of Infectious Diseases
Authors: Shaon Bhatta Shuvo, Bonaventure C. Molokwu, Ziad Kobti
Venue: KDD
Year: 2020

Infectious diseases can spread from an infected person to a susceptible person through direct or indirect physical contact, consequently controlling such types of spread is difficult. However, a proper decision at the initial stage can help control the disease's propagation before it turns into a pandemic. Social distancing and hospital capacity are considered among the most critical parameters to manage these types of conditions. In this paper, we used artificial agent-based simulation modeling to identify the importance of social distancing and hospitals' capacity in terms of the number of beds to shorten the length of an outbreak and reduce the total number of infections and deaths during an epidemic. After simulating the model based on different scenarios in a small artificial society, we learned that shorter social isolation activation delay has a higher impact on reducing the catastrophe. Increasing the hospital's treatment capacity, i.e., the number of isolation beds in the hospitals can become handy when social isolation cannot be activated shortly. The model can be considered a prototype to take proper steps based on the simulations on different parameter settings towards the control of an epidemic.
Unsupervised Paraphrasing via Deep Reinforcement Learning
Authors: A. B. Siddique 0001, Samet Oymak, Vagelis Hristidis
Venue: KDD
Year: 2020

Paraphrasing is expressing the meaning of an input sentence in different wording while maintaining fluency (i.e., grammatical and syntactical correctness). Most existing work on paraphrasing use supervised models that are limited to specific domains (e.g., image captions). Such models can neither be straightforwardly transferred to other domains nor generalize well, and creating labeled training data for new domains is expensive and laborious. The need for paraphrasing across different domains and the scarcity of labeled training data in many such domains call for exploring unsupervised paraphrase generation methods. We propose Progressive Unsupervised Paraphrasing (PUP): a novel unsupervised paraphrase generation method based on deep reinforcement learning (DRL). PUP uses a variational autoencoder (trained using a non-parallel corpus) to generate a seed paraphrase that warm-starts the DRL model. Then, PUP progressively tunes the seed paraphrase guided by our novel reward function which combines semantic adequacy, language fluency, and expression diversity measures to quantify the quality of the generated paraphrases in each iteration without needing parallel sentences. Our extensive experimental evaluation shows that PUP outperforms unsupervised state-of-the-art paraphrasing techniques in terms of both automatic metrics and user studies on four real datasets. We also show that PUP outperforms domain-adapted supervised algorithms on several datasets. Our evaluation also shows that PUP achieves a great trade-off between semantic similarity and diversity of expression.
Imputing Various Incomplete Attributes via Distance Likelihood Maximization
Authors: Shaoxu Song, Yu Sun 0027
Venue: KDD
Year: 2020

Missing values may appear in various attributes. By "various", we mean (1) different types of values in a tuple, such as numerical or categorical, and (2) different attributes in a tuple, either the dependent or determinant attributes of regression models or dependency rules. Such varieties unfortunately prevent the imputation performing. In this paper, we propose to study the distance models that predict distances between tuples for missing data imputation. The immediate benefits are in two aspects, (1) uniformly processing and collaboratively utilizing the distances on all the attributes with various types of values, and (2) rather than enumerating the combinations of imputation candidates on various attributes, we can directly calculate the most likely distances of missing values to other complete ones and thus infer the corresponding imputations. Our major technical highlights include (1) introducing the imputation statistically explainable by the likelihood on distances, (2) proving NP-hardness of finding the maximum likelihood imputation, and (3) devising the approximation algorithm with performance guarantees. Experiments over datasets with real missing values demonstrate the superiority of the proposed method compared to 11 existing approaches in 5 categories. Our proposal improves not only the imputation accuracy but also the downstream applications such as classification, clustering and record matching.
Re-identification Attack to Privacy-Preserving Data Analysis with Noisy Sample-Mean
Authors: Du Su, Hieu Tri Huynh, Ziao Chen, Yi Lu 0001, Wenmiao Lu
Venue: KDD
Year: 2020

In mining sensitive databases, access to sensitive class attributes of individual records is often prohibited by enforcing field-level security, while only aggregate class-specific statistics are allowed to be released. We consider a common privacy-preserving data analytics scenario where only a noisy sample mean of the class of interest can be queried. Such practice is widely found in medical research and business analytics settings. This paper studies the hazard of re-identification of entire class caused by revealing a noisy sample mean of the class. With a novel formulation of the re-identification attack as a generalized positive-unlabeled learning problem, we prove that the risk function of the re-identification problem is closely related to that of learning with complete data. We demonstrate that with a one-sided noisy sample mean, an effective re-identification attack can be devised with existing PU learning algorithms. We then propose a novel algorithm, growPU, that exploits the unique property of sample mean and consistently outperforms existing PU learning algorithms on the re-identification task. GrowPU achieves re-identification accuracy of 93.6% on the MNIST dataset and 88.1% on an online behavioral dataset with noiseless sample mean. With noise that guarantees 0.01-differential privacy, growPU achieves 91.9% on the MNIST dataset and 84.6% on the online behavioral dataset.
A Framework for Recommending Accurate and Diverse Items Using Bayesian Graph Convolutional Neural Networks
Authors: Jianing Sun, Wei Guo 0006, Dengcheng Zhang, Yingxue Zhang 0001, Florence Regol, Yaochen Hu 0001, Huifeng Guo, Ruiming Tang, Han Yuan, Xiuqiang He 0001, Mark Coates
Venue: KDD
Year: 2020

Personalized recommender systems are playing an increasingly important role for online consumption platforms. Because of the multitude of relationships existing in recommender systems, Graph Neural Networks (GNNs) based approaches have been proposed to better characterize the various relationships between a user and items while modeling a user's preferences. Previous graph-based recommendation approaches process the observed user-item interaction graph as a ground-truth depiction of the relationships between users and items. However, especially in the implicit recommendation setting, all the unobserved user-item interactions are usually assumed to be negative samples. There are missing links that represent a user's future actions. In addition, there may be spurious or misleading positive interactions. To alleviate the above issue, in this work, we take a first step to introduce a principled way to model the uncertainty in the user-item interaction graph using the Bayesian Graph Convolutional Neural Network framework. We discuss how inference can be performed under our framework and provide a concrete formulation using the Bayesian Probabilistic Ranking training loss. We demonstrate the effectiveness of our proposed framework on four benchmark recommendation datasets. The proposed method outperforms state-of-the-art graph-based recommendation models. Furthermore, we conducted an offline evaluation on one industrial large-scale dataset. It shows that our proposed method outperforms the baselines, with the potential gain being more significant for cold-start users. This illustrates the potential practical benefit in real-world recommender systems.
TAdaNet: Task-Adaptive Network for Graph-Enriched Meta-Learning
Authors: Qiuling Suo, Jingyuan Chou, Weida Zhong, Aidong Zhang 0001
Venue: KDD
Year: 2020

Annotated data samples in real-world applications are often limited. Meta-learning, which utilizes prior knowledge learned from related tasks and generalizes to new tasks of limited supervised experience, is an effective approach for few-shot learning. However, standard meta-learning with globally shared knowledge cannot handle the task heterogeneity problem well, i.e., tasks lie in different distributions. Recent advances have explored several ways to trigger task-dependent initial parameters or metrics, in order to customize task-specific information. These approaches learn task contextual information from data, but ignore external domain knowledge that can help in the learning process. In this paper, we propose a task-adaptive network (TAdaNet) that makes use of a domain-knowledge graph to enrich data representations and provide task-specific customization. Specifically, we learn a task embedding that characterizes task relationships and tailors task-specific parameters, resulting in a task-adaptive metric space for classification. Experimental results on a few-shot image classification problem show the effectiveness of the proposed method. We also apply it on a real-world disease classification problem, and show promising results for clinical decision support.
Hyperbolic Distance Matrices
Authors: Puoya Tabaghi, Ivan Dokmanic
Venue: KDD
Year: 2020

Hyperbolic space is a natural setting for mining and visualizing data with hierarchical structure. In order to compute a hyperbolic embedding from comparison or similarity information, one has to solve a hyperbolic distance geometry problem. In this paper, we propose a unified framework to compute hyperbolic embeddings from an arbitrary mix of noisy metric and non-metric data. Our algorithms are based on semidefinite programming and the notion of a hyperbolic distance matrix, in many ways parallel to its famous Euclidean counterpart. A central ingredient we put forward is a semidefinite characterization of the hyperbolic Gramian---a matrix of Lorentzian inner products. This characterization allows us to formulate a semidefinite relaxation to efficiently compute hyperbolic embeddings in two stages: first, we complete and denoise the observed hyperbolic distance matrix; second, we propose a spectral factorization method to estimate the embedded points from the hyperbolic distance matrix. We show through numerical experiments how the flexibility to mix metric and non-metric constraints allows us to efficiently compute embeddings from arbitrary data.
Identifying Homeless Youth At-Risk of Substance Use Disorder: Data-Driven Insights for Policymakers
Authors: Maryam Tabar, Heesoo Park, Stephanie Winkler, Dongwon Lee 0001, Anamika Barman-Adhikari, Amulya Yadav
Venue: KDD
Year: 2020

Substance Use Disorder (SUD) is a devastating disease that leads to significant mental and behavioral impairments. Its negative effects damage the homeless youth population more severely (as compared to stably housed counterparts) because of their high-risk behaviors. To assist policymakers in devising effective and accurate long-term strategies to mitigate SUD, it is necessary to critically analyze environmental, psychological, and other factors associated with SUD among homeless youth. Unfortunately, there is no definitive data-driven study on analyzing factors associated with SUD among homeless youth. While there have been a few prior studies in the past, they (i) do not analyze variation in the associated factors for SUD with geographical heterogeneity in their studies; and (ii) only consider a few contributing factors to SUD in relatively small samples. This work aims to fill this gap by making the following three contributions: (i) we use a real-world dataset collected from ~1,400 homeless youth (across six American states) to build accurate Machine Learning (ML) models for predicting the susceptibility of homeless youth to SUD; (ii) we find a representative set of factors associated with SUD among this population by analyzing feature importance values associated with our ML models; and (iii) we investigate the effect of geographical heterogeneity on the factors associated with SUD. Our results show that our system using adaptively boosted decision trees achieves the best predictive accuracy out of several algorithms on the SUD prediction task, achieving an Area Under the ROC Curve of 0.85. Further, among other things, we also find that both Post-Traumatic Stress Disorder (PTSD) and depression are very strongly associated with SUD among homeless youth because of their propensity to self-medicate to alleviate stress. This work is done in collaboration with social work scientists, who are currently evaluating the results for potential future deployment.
From Online to Non-i.i.d. Batch Learning
Authors: Yufei Tao 0001, Shangqi Lu
Venue: KDD
Year: 2020

This paper initializes the study of online-to-batch conversion when the samples in batch learning are not i.i.d. Our motivation originated from two facts. First, sample sets in reality are seldom i.i.d., thus preventing the application of the existing conversions. Second, the online model of learning permits an adversarial stream of samples that almost for sure violates the i.i.d. assumption, raising the possibility of adapting an online algorithm effectively to learn from a non-i.i.d. sample set. We present a set of techniques to utilize an online algorithm as a black box to perform batch learning in the absence of the i.i.d. assumption. Our techniques are generic, and are applicable to virtually any online algorithms on classification. This provides strong evidence that the great variety of known algorithms in the online-learning literature can indeed be harnessed to learn from sufficiently-representative non-i.i.d. samples.
Data Sketching for Real Time Analytics: Theory and Practice
Authors: Daniel Ting, Jonathan Malkin, Lee Rhodes
Venue: KDD
Year: 2020

Speed, cost, and scale. These are 3 of the biggest challenges in analyzing big data. While modern data systems continue to push the boundaries of scale, the problems of speed and cost are fundamentally tied to the size of data being scanned or processed. Processing thousands of queries that each access terabytes of data with sub-second latency remains infeasible. Data sketching techniques provide means to drastically reduce this size, allowing for real-time or interactive data analysis with reduced costs but with approximate answers.
Isolation Distributional Kernel: A New Tool for Kernel based Anomaly Detection
Authors: Kai Ming Ting, Bi-Cun Xu, Takashi Washio, Zhi-Hua Zhou
Venue: KDD
Year: 2020

We introduce Isolation Distributional Kernel as a new way to measure the similarity between two distributions. Existing approaches based on kernel mean embedding, which converts a point kernel to a distributional kernel, have two key issues: the point kernel employed has a feature map with intractable dimensionality; and it is data independent. This paper shows that Isolation Distributional Kernel (IDK), which is based on a data dependent point kernel, addresses both key issues. We demonstrate IDK's efficacy and efficiency as a new tool for kernel based anomaly detection. Without explicit learning, using IDK alone outperforms existing kernel based anomaly detector OCSVM and other kernel mean embedding methods that rely on Gaussian kernel. We reveal for the first time that an effective kernel based anomaly detector based on kernel mean embedding must employ a characteristic kernel which is data dependent.
Statistically Significant Pattern Mining with Ordinal Utility
Authors: Thien Q. Tran, Kazuto Fukuchi, Youhei Akimoto, Jun Sakuma
Venue: KDD
Year: 2020

Statistically significant patterns mining (SSPM) is an essential and challenging data mining task in the field of knowledge discovery in databases (KDD), in which each pattern is evaluated via a hypothesis test. Our study aims to introduce a preference relation into patterns and to discover the most preferred patterns under the constraint of statistical significance, which has never been considered in existing SSPM problems. We propose an iterative multiple testing procedure that can alternately reject a hypothesis and safely ignore the hypotheses that are less useful than the rejected hypothesis. One advantage of filtering out patterns with low utility is that it avoids consumption of the significance budget by rejection of useless (that is, uninteresting) patterns. This allows the significance budget to be focused on useful patterns, leading to more useful discoveries. We show that the proposed method can control the familywise error rate (FWER) under certain assumptions, that can be satisfied by a realistic problem class in SSPM. We also show that the proposed method always discovers a set of patterns that is at least equally or more useful than those discovered using the standard Tarone-Bonferroni method SSPM. Finally, we conducted several experiments with both synthetic and real-world data to evaluate the performance of our method. As a result, in the experiments with real-world datasets, the proposed method discovered a larger number of more useful patterns than the existing method for all five conducted tasks.
Models of Data Governance and Advancing Indigenous Genomic Data Sovereignty
Authors: Krystal S. Tsosie
Venue: KDD
Year: 2020

While there has been considerable deliberation about ownership and stewardship of genomic data, as of yet, there does not exist a singular framework that encapsulates the current and future trajectory of how these data governance models can exist for Indigenous communities. We succinctly describe two case studies in the Akimel O'odham (Pima) communities that demonstrate the spectrum of data governance structures, in which tribal members have no input to complete control of data collection and usage. We describe (1) tribal-trust relationships, (2) non-tribal partnerships, and (3) tribally-driven models in context of an Indigenous people whose genomic and health data have been widely misused and exploited by outside researchers and the new narrative in which the O'odham have begun re-asserting their sovereignty in data domains.