OpenRaiser/Intern-Atlas huggingface dataset:
The Illusion of Inclusion: Large Scale Genomic Data Sovereignty and Indigenous Populations
Authors: Keolu Fox
Venue: KDD
Year: 2020

Raw genomic data has emerged as a top global commodity in the the past several years. This shift is so new that data science experts are still evaluating what such information is worth in a global market. In 2018, the direct-to-consumer genetic-testing company 23andMe sold access to its database containing digital sequence information from approximately 5 million people to GlaxoSmithKline for 300 million dollars. Clearly there is a growing market for these products and like a goldrush, organizations are seeking to find rare samples to package up for analysis and reward. Indigenous peoples are legitimately concerned about the potential for commodification of drugs derived from research on their genomes, and as a consequence, they are sometimes reluctant to participate in genomics research. All of Us, a program of federally funded investigators are interested in recruiting participants from native groups, but given the fraught history of genetic studies involving Indigenous peoples - including examples such as the Havasupai v. Arizona State University, in which the tribe successfully sued the university for improperly using its members' blood samples - tribal communities continue to be wary about participating in the federal government's newest endeavor. Here I contextualize data sovereignty issues and identify strategies to create equity in federal government genomic data collection efforts.
Mutually Beneficial Collaborations to Broaden Participation of Hispanics in Data Science
Authors: Patricia Ordóñez Franco
Venue: KDD
Year: 2020

Representation of Hispanics, especially Hispanic women, is notoriously low in data science programs in higher education and in the tech industry. The engagement of undergraduate students in research, often and early in their path towards degree completion, has been championed as one of the principal reforms necessary to increase the number of capable professionals in STEM. The benefits attributed to undergraduate research experiences have been reported to disproportionately benefit individuals from groups that have been historically underrepresented in STEM. The IDI-BD2K (Increasing Diversity in Interdisciplinary Big Data to Knowledge) Program funded by the NIH at the University of Puerto Rico Río Piedras (UPRRP) was designed to bridge the increasing digital and data divide at the university. The college's population is 98 percent Hispanic and yet there is no formal data science program. There also exists a gender imbalance in computing at the College of Natural Sciences at the UPRRP. Over 60 percent of the undergraduate students in Biology are women. However, the percentage of women in Computer Science hovers around 15 percent. The IDI-BD2K was created to address both these concerns and increase the participation of Hispanics in interdisciplinary computational and quantitative research. In this talk, I will highlight the need for mutually beneficial university collaborations to reduce the digital and data divide, create greater awareness of the growing disparities and increase the number of future faculty with experience teaching diverse students.
Deep Learning for Search and Recommender Systems in Practice
Authors: Zhoutong Fu, Huiji Gao, Weiwei Guo, Sandeep Kumar Jha, Jun Jia, Xiaowei Liu, Bo Long, Jun Shi, Sida Wang 0002, Mingzhou Zhou
Venue: KDD
Year: 2020

In this talk, we will go over the components of personalized search and recommender systems and demonstrate the applications of various deep learning techniques along the way.
Efficient Algorithm for the b-Matching Graph
Authors: Yasuhiro Fujiwara, Atsutoshi Kumagai, Sekitoshi Kanai, Yasutoshi Ida, Naonori Ueda
Venue: KDD
Year: 2020

The b-matching graph is a useful approach to computing a graph from high-dimensional data. Unlike the k-NN graph that greedily connects each data point to its k nearest neighbors and typically has more than k edges, each data point in the b-matching graph uniformly has b edges; the idea is reduce edges between cross-clusters that have different semantics. In addition, edge weights are obtained from regression results of each data pointand restricted to be non-negative to improve the robustness for data noise. The b-matching graph can more effectively model high-dimensional data than the traditional k-NN graph. However, the construction cost of the b-matching graph is impractical for large-scale data sets. This is because, to determine edges in the graph, it needs to iteratively update messages between all pairs of data points until convergence, and it computes non-negative edge weights of each data point by applying a solver intended for quadratic programming problems. Our proposal, b-dash, can efficiently construct a b-matching graph because of its two key techniques: (1) it prunes unnecessary update messages in determining edges and (2) it incrementally computes edge weights by exploiting the Sherman-Morrison formula. Experiments show that our approach is up to 58.6 times faster than the previous approaches while guaranteeing result optimality.
Multimodal Deep Learning Based Crop Classification Using Multispectral and Multitemporal Satellite Imagery
Authors: Krishna Karthik Gadiraju, Bharathkumar Ramachandra, Zexi Chen, Ranga Raju Vatsavai
Venue: KDD
Year: 2020

The Food and Agriculture Organization (FAO) of the United Nations predicts that in order to meet the needs of the expected 3 billion population growth by 2050, food production has to increase by 60%. Therefore, monitoring and mapping crops accurately is essential for estimating food production during each crop growing season across the globe. Traditionally, multispectral remote sensing imagery has been widely used for mapping crops worldwide. However, single date imagery does not capture temporal characteristics (phenology) of growing crops, leading to imprecise crop maps and food estimates. On the other hand, purely temporal classification approaches also produce inaccurate crop maps as they do not account for spatial autocorrelations. In this paper, we present a multimodal deep learning solution that jointly exploits spatial-spectral and phenological properties to identify major crop types. Using a two stream architecture, spatial characteristics are captured via a spatial stream consisting of very high resolution images (single date, 1m, 3-spectral bands, USDA NAIP) with a CNN and the phenological characteristics via a temporal stream images (biweekly, 250m, MODIS NDVI) with an LSTM. Experimental results show that the proposed multimodal solution reduces prediction error by 60%.
COMPOSE: Cross-Modal Pseudo-Siamese Network for Patient Trial Matching
Authors: Junyi Gao, Cao Xiao, Lucas M. Glass, Jimeng Sun 0001
Venue: KDD
Year: 2020

Clinical trials play important roles in drug development but often suffer from expensive, inaccurate and insufficient patient recruitment. The availability of massive electronic health records (EHR) data and trial eligibility criteria (EC) bring a new opportunity to data driven patient recruitment. One key task named patient-trial matching is to find qualified patients for clinical trials given structured EHR and unstructured EC text (both inclusion and exclusion criteria). How to match complex EC text with longitudinal patient EHRs? How to embed many-to-many relationships between patients and trials? How to explicitly handle the difference between inclusion and exclusion criteria? In this paper, we proposed CrOss-Modal PseudO-SiamEse network (COMPOSE) to address these challenges for patient-trial matching. One path of the network encodes EC using convolutional highway network. The other path processes EHR with multi-granularity memory network that encodes structured patient records into multiple levels based on medical ontology. Using the EC embedding as query, COMPOSE performs attentional record alignment and thus enables dynamic patient-trial matching. COMPOSE also introduces a composite loss term to maximize the similarity between patient records and inclusion criteria while minimize the similarity to the exclusion criteria. Experiment results show COMPOSE can reach 98.0% AUC on patient-criteria matching and 83.7% accuracy on patient-trial matching, which leads 24.3% improvement over the best baseline on real-world patient-trial matching tasks.
Multimodal Machine Learning for Video and Image Analysis
Authors: Shalini Ghosh
Venue: KDD
Year: 2020

In this talk, we will first discuss multimodal ML for video content analysis. Videos typically have data in multiple modalities like audio, video, and text (captions). Understanding and modeling the interaction between different modalities is key for video analysis tasks like categorization, object detection, activity recognition, etc. However, data modalities are not always correlated -- so, learning when modalities are correlated and using that to guide the influence of one modality on the other is crucial. Another salient feature of videos is the coherence between successive frames due to continuity of video and audio, a property that we refer to as temporal coherence. We show how using non-linear guided cross-modal signals and temporal coherence can improve the performance of multimodal ML models for video analysis tasks like categorization. We also created a hierarchical taxonomy of categories internally. Our experiments on the large-scale YouTube-8M dataset show how our approach significantly outperforms state-of-the-art multimodal ML model for video categorization using our taxonomy, as well as generalizes well to an internal dataset of video segments from actual TV programs. The next part of the talk will briefly discuss our work on explainability of multimodal ML models. We will conclude the talk by outlining other multimodal ML applications like incremental object detection and visual dialog, and discuss potential applications of multimodal ML to various domains.
Keynote Speaker: Yolanda Gil
Authors: Yolanda Gil
Venue: KDD
Year: 2020

Dr. Yolanda Gil Dr. Yolanda Gil is Director of Knowledge Technologies and Associate Division Director at the Information Sciences Institute of the University of Southern California, and Research Professor in Computer Science and in Spatial Sciences. She is also Associate Director of Interdisciplinary Programs in Informatics. She received her M.S. and Ph. D. degrees in Computer Science from Carnegie Mellon University, with a focus on artificial intelligence. Her research is on intelligent interfaces for knowledge capture and discovery, which she investigates in a variety of projects concerning knowledge-based planning and problem solving, information analysis and assessment of trust, semantic annotation and metadata, and community-wide development of knowledge bases. Dr. Gil collaborates with scientists in different domains on semantic workflows and metadata capture, social knowledge collection, computer-mediated collaboration, and automated discovery. Dr. Gil has served in the Advisory Committee of the Computer Science and Engineering Directorate of the National Science Foundation. She initiated and chaired the W3C Provenance Group that led to a community standard in this area. Dr. Gil is a Fellow of the Association for Computing Machinery (ACM), and Past Chair of its Special Interest Group in Artificial Intelligence. She is also Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), and was elected as its 24th President in 2016.
A Geometric Approach to Predicting Bounds of Downstream Model Performance
Authors: Brian J. Goode, Debanjan Datta
Venue: KDD
Year: 2020

This paper presents the motivation and methodology for including model application criteria into baseline analysis. We will focus on detailing the interplay between the common measures of mean square error (MSE) and accuracy as it relates to perceived model performance. MSE is a common aggregate measure for the performance of predictive regression models. The advantages are numerous. MSE is agnostic to the choice of model given that the set of possible outcome values are defined on the appropriate metric space. In practice, decisions on how to subsequently use a trained model are based on predictive performance, relative to a baseline where input features are not used - colloquially a "random model". However, the relative performance gains of a model in terms of MSE to the baseline does not guarantee commensurate gains when deployed in downstream applications, systems, or processes. This paper demonstrates one derivation of a distribution to qualify MSE performance for multi-class decision making systems desiring a certain level of accuracy. The model error is qualified through comparison to relevant baselines tied to the application suited to evaluating individual outcome performance criteria.
Neural Structured Learning: Training Neural Networks with Structured Signals
Authors: Arjun Gopalan, Da-Cheng Juan, Cesar Ilharco Magalhaes, Chun-Sung Ferng, Allan Heydon, Chun-Ta Lu, Philip Pham, George Yu
Venue: KDD
Year: 2020

We present Neural Structured Learning (NSL) in TensorFlow [2], a new learning paradigm to train neural networks by leveraging structured signals in addition to feature inputs. Structure can be explicit as represented by a graph, or implicit, either induced by adversarial perturbation or inferred using techniques like embedding learning. NSL is open-sourced as part of the TensorFlow [3] ecosystem and is widely used in Google across many products and services. In this tutorial, we provide an overview of the NSL framework including various libraries, tools, and APIs as well as demonstrate the practical use of NSL in different applications. The NSL website is hosted at www.tensorflow.org/neural_structured_learning, which includes details about the theoretical foundations of the technology, extensive API documentation, and hands-on tutorials.
Preserving Integrity in Online Social Media
Authors: Alon Y. Halevy
Venue: KDD
Year: 2020

Online social networks provide a platform for sharing information and free expression. However, these networks are also used for malicious purposes, such as distributing misinformation and hate speech, selling illegal drugs, and coordinating sex trafficking or child exploitation. Keeping users on these platforms safe from such harm, known as the problem of Integrity, is a major focus for social media companies. This talk, coming from the perspective of addressing many of these challenges at Facebook, highlights some of the recent progress made in the area of integrity and some of the challenges that lie ahead.
Recurrent Halting Chain for Early Multi-label Classification
Authors: Thomas Hartvigsen, Cansu Sen, Xiangnan Kong, Elke A. Rundensteiner
Venue: KDD
Year: 2020

Early multi-label classification of time series, the assignment of a label set to a time series before the series is entirely observed, is critical for time-sensitive domains such as healthcare. In such cases, waiting too long to classify can render predictions useless, regardless of their accuracy, while predicting prematurely can result in potentially costly erroneous results. When predicting multiple labels (for example, types of infections), dependencies between labels can be learned and leveraged to improve overall accuracy. Together, reliably predicting the correct label set of a time series while observing as few timesteps as possible is challenging because these goals are contradictory in that fewer timesteps often means worse accuracy. To achieve early yet sufficiently accurate predictions, correlations between labels must be accounted for since direct evidence of some labels may only appear late in the series. We design an effective solution to this open problem, the Recurrent Halting Chain (RHC), that for the first time integrates key innovations in both Early and Multi-label Classification into one multi-objective model. RHC uses a recurrent neural network to jointly model raw time series as well as correlations between labels, resulting in a novel order-free classifier chain that tackles this time-sensitive multi-label learning task. Further, RHC employs a reinforcement learning-based halting network to decide at each timestep which, if any, classes should be predicted, learning to build the label set over time. Using two real-world time-sensitive datasets and popular multi-label metrics, we show that RHC outperforms recent alternatives by predicting more-accurate label sets earlier.
Attention and Memory-Augmented Networks for Dual-View Sequential Learning
Authors: Yong He 0008, Cheng Wang, Nan Li 0027, Zhenyu Zeng
Venue: KDD
Year: 2020

In recent years, sequential learning has been of great interest due to the advance of deep learning with applications in time-series forecasting, natural language processing, and speech recognition. Recurrent neural networks (RNNs) have achieved superior performance in single-view and synchronous multi-view sequential learning comparing to traditional machine learning models. However, the method remains less explored in asynchronous multi-view sequential learning, and the unalignment nature of multiple sequences poses a great challenge to learn the inter-view interactions. We develop an AMANet (Attention and Memory-Augmented Networks) architecture by integrating both attention and memory to solve asynchronous multi-view learning problem in general, and we focus on experiments in dual-view sequences in this paper. Self-attention and inter-attention are employed to capture intra-view interaction and inter-view interaction, respectively. History attention memory is designed to store the historical information of a specific object, which serves as local knowledge storage. Dynamic external memory is used to store global knowledge for each view. We evaluate our model in three tasks: medication recommendation from a patient's medical records, diagnosis-related group (DRG) classification from a hospital record, and invoice fraud detection through a company's taxation behaviors. The results demonstrate that our model outperforms all baselines and other state-of-the-art models in all tasks. Moreover, the ablation study of our model indicates that the inter-attention mechanism plays a key role in the model and it can boost the predictive power by effectively capturing the inter-view interactions from asynchronous views.
DeepLine: AutoML Tool for Pipelines Generation using Deep Reinforcement Learning and Hierarchical Actions Filtering
Authors: Yuval Heffetz, Roman Vainshtein, Gilad Katz, Lior Rokach
Venue: KDD
Year: 2020

Automatic Machine Learning (AutoML) is an area of research aimed at automating Machine Learning (ML) activities that currently require the involvement of human experts. One of the most challenging tasks in this field is the automatic generation of end-to-end ML pipelines: combining multiple types of ML algorithms into a single architecture used for analysis of previously-unseen data. This task has two challenging aspects: the first is the need to explore a large search space of algorithms and pipeline architectures. The second challenge is the computational cost of training and evaluating multiple pipelines. In this study we present DeepLine, a reinforcement learning-based approach for automatic pipeline generation. Our proposed approach utilizes an efficient representation of the search space together with a novel method for operating in environments with large and dynamic action spaces. By leveraging past knowledge gained from previously-analyzed datasets, our approach only needs to generate and evaluate few dozens of pipelines to reach comparable or better performance than current state-of-the-art AutoML systems that evaluate hundreds and even thousands of pipelines in their optimization process. Evaluation on 56 classification datasets demonstrates the merits of our approach.
How AI Can Help Build Resiliency for Small Businesses in a Global Economic Crisis
Authors: Nhung Ho
Venue: KDD
Year: 2020

In the midst of COVID-19, a global economic crisis is threatening the livelihoods of small business owners everywhere. In ordinary times, 50 percent of small businesses go out of business in the first 5 years. In today's extraordinary times, nearly 7.5 million (~25 percent) of small businesses in the U.S. alone have been at risk of closing permanently in a matter of months (Source: Main Street America's Small Business Survey 2020).
Tutorial on Online User Engagement: Metrics and Optimization
Authors: Liangjie Hong, Mounia Lalmas
Venue: KDD
Year: 2020

User engagement plays a central role in companies operating online services, such as search engines, news portals, e-commerce sites, entertainment services, and social networks. A main challenge is to leverage collected knowledge about the daily online behavior of millions of users to understand what engages them short-term and more importantly long-term. Two critical steps of improving user engagement are metrics and their optimization. The most common way that engagement is measured is through various online metrics, acting as proxy measures of user engagement. This tutorial will review these metrics, their advantages and drawbacks, and their appropriateness to various types of online services. Once metrics are defined, how to optimize them will become the key issue. We will survey methodologies including machine learning models and experimental designs that are utilized to optimize these metrics via direct or indirect ways. As case studies, we will focus on four types of services, news, search, entertainment, and e-commerce.
HetETA: Heterogeneous Information Network Embedding for Estimating Time of Arrival
Authors: Huiting Hong, Yucheng Lin, Xiaoqing Yang, Zang Li, Kun Fu 0002, Zheng Wang 0010, Xiaohu Qie, Jieping Ye
Venue: KDD
Year: 2020

The estimated time of arrival (ETA) is a critical task in the intelligent transportation system, which involves the spatiotemporal data. Despite a significant amount of prior efforts have been made to design efficient and accurate systems for ETA task, few of them take structural graph data into account, much less the heterogeneous information network. In this paper, we propose HetETA to leverage heterogeneous information graph in ETA task. Specifically, we translate the road map into a multi-relational network and introduce a vehicle-trajectories based network to jointly consider the traffic behavior pattern. Moreover, we employ three components to model temporal information from recent periods, daily periods and weekly periods respectively. Each component comprises temporal convolutions and graph convolutions to learn representations of the spatiotemporal heterogeneous information for ETA task. Experiments on large-scale datasets illustrate the effectiveness of the proposed HetETA beyond the state-of-the-art methods, and show the importance of representation learning of heterogeneous information networks for ETA task.
Data-Driven Never-Ending Learning Question Answering Systems
Authors: Estevam R. Hruschka Jr.
Venue: KDD
Year: 2020

This tutorial focuses on how to build Question Answering (QA) syetems based on the Never-Ending Learning (NEL) approach. NEL systems can be roughly described as computer systems that learn over time to become better in solving a specific task. Different NEL approaches have been proposed and applied in different tasks and domains. Recent advances encourage us to keep addressing the problem of how to build computer systems that can take advantage of NEL principles. Considering that it is not always so straightforward to have NEL principles applied to ML models, this tutorial guides the audience (with hands-on examples and supporting theory, algorithms and models) on how to model a system in a NEL fashion and intends to help KDD community to become familiar with such approaches. Question Answering is chosen as application domain mainly because of the relevance of the topic (QA) for KDD and AI communities in general.