OpenRaiser/Intern-Atlas huggingface dataset:
Preventing Rhino Poaching through Machine Learning
Authors: Olga Liakhovich, Gabriel Domínguez-Conde
Venue: KDD
Year: 2019

Official figures in Africa indicate that 1,349 rhinos were killed in 2015. This marked the most critical moment of the current rhino poaching crisis that began in 2008. This trend has since reversed to the minimum of 1,124 poached rhinos achieved in 2017. Although this brings hope, this emergency is still a formidable challenge that needs to be tackled from a multi-angle approach including anti-poaching collaboration among countries to enforce effective wildlife crime laws, target campaigns in the illegal horn rhino end-user countries like China and Vietnam, and the adoption of cutting-edge technology by governmental agencies and conservationist NGOs. In this talk, we will highlight the recent efforts of Peace Parks Foundation (PPF), the advocate for the creation of transfrontier conservation areas in South Africa, and Microsoft to address this crisis. We will explain how, through the joint use of deep learning and Cloud, PPF and Microsoft developed a fast and accurate potential poacher detection solution that allows PPF to allocate the park resources in a smarter and more efficient manner.
Advances in Cost-sensitive Multiclass and Multilabel Classification
Authors: Hsuan-Tien Lin
Venue: KDD
Year: 2019

Classification is an important problem for data mining and knowledge discovery and comes with a wide range of applications. Different applications usually evaluate the classification performance with different criteria. The variety of criteria calls for cost-sensitive classification algorithms, which take the specific criterion as input to the learning algorithm and adapt to different criteria more easily. While the cost-sensitive binary classification problem has been relatively well-studied, the cost-sensitive multiclass and multilabel classification problems are harder to solve because of the sophisticated nature of their evaluation criteria. The tutorial aims to review current techniques for solving cost-sensitive multiclass and multilabel classification problems, with the hope of helping more real-world applications enjoy the benefits of cost-sensitive classification.
Nonparametric Mixture of Sparse Regressions on Spatio-Temporal Data - An Application to Climate Prediction
Authors: Yumin Liu, Junxiang Chen, Auroop R. Ganguly, Jennifer G. Dy
Venue: KDD
Year: 2019

Climate prediction is a very challenging problem. Many institutes around the world try to predict climate variables by building climate models called General Circulation Models (GCMs), which are based on mathematical equations that describe the physical processes. The prediction abilities of different GCMs may vary dramatically across different regions and time. Motivated by the need of identifying which GCMs are more useful for a particular region and time, we introduce a clustering model combining Dirichlet Process (DP) mixture of sparse linear regression with Markov Random Fields (MRFs). This model incorporates DP to automatically determine the number of clusters, imposes MRF constraints to guarantee spatio-temporal smoothness, and selects a subset of GCMs that are useful for prediction within each spatio-temporal cluster with a spike-and-slab prior. We derive an effective Gibbs sampling method for this model. Experimental results are provided for both synthetic and real-world climate data.
Automating Feature Subspace Exploration via Multi-Agent Reinforcement Learning
Authors: Kunpeng Liu, Yanjie Fu, Pengfei Wang, Le Wu, Rui Bo, Xiaolin Li
Venue: KDD
Year: 2019

Feature selection is the preprocessing step in machine learning which tries to select the most relevant features for the subsequent prediction task. Effective feature selection could help reduce dimensionality, improve prediction accuracy and increase result comprehensibility. It is very challenging to find the optimal feature subset from the subset space as the space could be very large. While much effort has been made by existing studies, reinforcement learning can provide a new perspective for the searching strategy in a more global way. In this paper, we propose a multi-agent reinforcement learning framework for the feature selection problem. Specifically, we first reformulate feature selection with a reinforcement learning framework by regarding each feature as an agent. Then, we obtain the state of environment in three ways, i.e., statistic description, autoencoder and graph convolutional network (GCN), in order to make the algorithm better understand the learning progress. We show how to learn the state representation in a graph-based way, which could tackle the case when not only the edges, but also the nodes are changing step by step. In addition, we study how the coordination between different features would be improved by more reasonable reward scheme. The proposed method could search the feature subset space globally and could be easily adapted to the real-time case (real-time feature selection) due to the nature of reinforcement learning. Also, we provide an efficient strategy to accelerate the convergence of multi-agent reinforcement learning. Finally, extensive experimental results show the significant improvement of the proposed method over conventional approaches.
Characterizing and Forecasting User Engagement with In-App Action Graph: A Case Study of Snapchat
Authors: Yozen Liu, Xiaolin Shi, Lucas Pierce, Xiang Ren
Venue: KDD
Year: 2019

While mobile social apps have become increasingly important in people's daily life, we have limited understanding on what motivates users to engage with these apps. In this paper, we answer the question whether users' in-app activity patterns help inform their future app engagement (e.g., active days in a future time window)? Previous studies on predicting user app engagement mainly focus on various macroscopic features (e.g., time-series of activity frequency), while ignoring fine-grained inter-dependencies between different in-app actions at the microscopic level. Here we propose to formalize individual user's in-app action transition patterns as a temporally evolving action graph, and analyze its characteristics in terms of informing future user engagement. Our analysis suggested that action graphs are able to characterize user behavior patterns and inform future engagement. We derive a number of high-order graph features to capture in-app usage patterns and construct interpretable models for predicting trends of engagement changes and active rates. To further enhance predictive power, we design an end-to-end, multi-channel neural model to encode both temporal action graphs, activity sequences, and other macroscopic features. Experiments on predicting user engagement for 150k Snapchat new users over a 28-day period demonstrate the effectiveness of the proposed prediction models. The analysis and prediction framework is also deployed at Snapchat to deliver real world business insights. Our proposed framework is also general and can be applied to any online platform.
Automatic Dialogue Summary Generation for Customer Service
Authors: Chunyi Liu, Peng Wang, Jiang Xu, Zang Li, Jieping Ye
Venue: KDD
Year: 2019

Dialogue summarization extracts useful information from a dialogue. It helps people quickly capture the highlights of a dialogue without going through long and sometimes twisted utterances. For customer service, it saves human resources currently required to write dialogue summaries. A main challenge of dialogue summarization is to design a mechanism to ensure the logic, integrity, and correctness of the summaries. In this paper, we introduce auxiliary key point sequences to solve this problem. A key point sequence describes the logic of the summary. In our training procedure, a key point sequence acts as an auxiliary label. It helps the model learn the logic of the summary. In the prediction procedure, our model predicts the key point sequence first and then uses it to guide the prediction of the summary. Along with the auxiliary key point sequence, we propose a novel Leader-Writer network. The Leader net predicts the key point sequence, and the Writer net predicts the summary based on the decoded key point sequence. The Leader net ensures the summary is logical and integral. The Writer net focuses on generating fluent sentences. We test our model on customer service scenarios. The results show that our model outperforms other models not only on BLEU and ROUGE-L score but also on logic and integrity.
Uncovering the Co-driven Mechanism of Social and Content Links in User Churn Phenomena
Authors: Yunfei Lu, Linyun Yu, Peng Cui, Chengxi Zang, Renzhe Xu, Yihao Liu, Lei Li, Wenwu Zhu
Venue: KDD
Year: 2019

Recent years witness the merge of social networks and user-generated content (UGC) platforms. In these new platforms, users establish links to others not only driven by their social relationships in the physical world but also driven by the contents published by others. During this merging process, social networks gradually integrate both social and content links and become unprecedentedly complicated, with the motivation to exploit both the advantages of social viscosity and content attractiveness to reach the best customer retention situation. However, due to the lack of fine-grained data recording such merging phenomena, the co-driven mechanism of social and content links in churn remains unexplored. How do social and content factors jointly influence customers' churn? What is the best ratio of social and content links for retention? Is there a model to capture this co-driven mechanism in churn phenomena? In this paper, we collect a real-world dataset with more than 5.77 million users and 1.15 billion links, with each link being tagged as a social one or a content one. We find that both social and content links have a significant impact on users' churn and they work jointly as a complicated mixture effect. As a result, we propose a novel survival model, which incorporates both social and content factors, to predict churn probability over time. Our model successfully fits the churn distribution in reality and accurately predicts the churn rate of different subpopulations in the future. By analyzing the modeling parameters, we try to strike a balance between social-driven and content-driven links in a user's social network to reach the lowest churn rate. Our model and findings may have potential implications for the design of future social media.
AccuAir: Winning Solution to Air Quality Prediction for KDD Cup 2018
Authors: Zhipeng Luo, Jianqiang Huang, Ke Hu, Xue Li, Peng Zhang
Venue: KDD
Year: 2019

Since air pollution seriously affects human heath and daily life, the air quality prediction has attracted increasing attention and become an active and important research topic. In this paper, we present AccuAir, our winning solution to the KDD Cup 2018 of Fresh Air, where the proposed solution has won the 1st place in two tracks, and the 2nd place in the other one. Our solution got the best accuracy on average in all the evaluation days. The task is to accurately predict the air quality (as indicated by the concentration of PM2.5, PM10 or O3) of the next 48 hours for each monitoring station in Beijing and London. Aiming at a cutting-edge solution, we first presents an analysis of the air quality data, identifying the fundamental challenges, such as the long-term but suddenly changing air quality, and complex spatial-temporal correlations in different stations. To address the challenges, we carefully design both global and local air quality features, and develop three prediction models including LightGBM, Gated-DNN and Seq2Seq, each with novel ingredients developed for better solving the problem. Specifically, a spatial-temporal gate is proposed in our Gated-DNN model, to effectively capture the spatial-temporal correlations as well as temporal relatedness, making the prediction more sensitive to spatial and temporal signals. In addition, the Seq2Seq model is adapted in such a way that the encoder summarizes useful historical features while the decoder concatenate weather forecast as input, which significantly improves prediction accuracy. Assembling all these components together, the ensemble of three models outperforms all competing methods in terms of the prediction accuracy of 31 days average, 10 days average and 24-48 hours.
AutoCross: Automatic Feature Crossing for Tabular Data in Real-World Applications
Authors: Yuanfei Luo, Mengshuo Wang, Hao Zhou, Quanming Yao, Wei-Wei Tu, Yuqiang Chen, Wenyuan Dai, Qiang Yang
Venue: KDD
Year: 2019

Feature crossing captures interactions among categorical features and is useful to enhance learning from tabular data in real-world businesses. In this paper, we present AutoCross, an automatic feature crossing tool provided by 4Paradigm to its customers, ranging from banks, hospitals, to Internet corporations. By performing beam search in a tree-structured space, AutoCross enables efficient generation of high-order cross features, which is not yet visited by existing works. Additionally, we propose successive mini-batch gradient descent and multi-granularity discretization to further improve efficiency and effectiveness, while ensuring simplicity so that no machine learning expertise or tedious hyper-parameter tuning is required. Furthermore, the algorithms are designed to reduce the computational, transmitting, and storage costs involved in distributed computing. Experimental results on both benchmark and real-world business datasets demonstrate the effectiveness and efficiency of AutoCross. It is shown that AutoCross can significantly enhance the performance of both linear and deep models.
Facebook Disaster Maps: Aggregate Insights for Crisis Response & Recovery
Authors: Paige Maas
Venue: KDD
Year: 2019

After a natural disaster or other crisis, humanitarian organizations need to know where affected people are located and what resources they need. While this information is difficult to capture quickly through conventional methods, aggregate usage patterns of social media apps like Facebook can help fill these information gaps. This talk will describe the data and methodology that power Facebook Disaster Maps. These maps utilize information about Facebook usage in areas impacted by natural hazards, producing insights into how the population is affected by and responding to the hazard. In addition to methodology details, including efforts taken to ensure the security and privacy of Facebook users, I'll also discuss how we worked with humanitarian partners to develop the maps, which are actively used in disaster response today. I'll give examples of insights generated from the maps and I'll also discuss some limitations of the current methodologies, challenges, and opportunities for improvement.
A Hierarchical Career-Path-Aware Neural Network for Job Mobility Prediction
Authors: Qingxin Meng, Hengshu Zhu, Keli Xiao, Le Zhang, Hui Xiong
Venue: KDD
Year: 2019

The understanding of job mobility can benefit talent management operations in a number of ways, such as talent recruitment, talent development, and talent retention. While there is extensive literature showing the predictability of the organization-level job mobility patterns (e.g., in terms of the employee turnover rate), there are no effective solutions for supporting the understanding of job mobility at an individual level. To this end, in this paper, we propose a hierarchical career-path-aware neural network for learning individual-level job mobility. Specifically, we aim at answering two questions related to individuals in their career paths: 1) who will be the next employer? 2) how long will the individual work in the new position? Specifically, our model exploits a hierarchical neural network structure with embedded attention mechanism for characterizing the internal and external job mobility. Also, it takes personal profile information into consideration in the learning process. Finally, the extensive results on real-world data show that the proposed model can lead to significant improvements in prediction accuracy for the two aforementioned prediction problems. Moreover, we show that the above two questions are well addressed by our model with a certain level of interpretability. For the case studies, we provide data-driven evidence showing interesting patterns associated with various factors (e.g., job duration, firm type, etc.) in the job mobility prediction process.
Earth Observations from a New Generation of Geostationary Satellites
Authors: Ramakrishna R. Nemani
Venue: KDD
Year: 2019

The latest generation of geostationary satellites carry sensors such as the Advanced Baseline Imager (GOES-16/17) and the Advanced Himawari Imager (Himawari-8/9) that closely mimic the spatial and spectral characteristics of widely used polar orbiting sensors such as EOS/MODIS. More importantly, they provide observations at 1-5-15 minute intervals, instead of twice a day from MODIS, offering unprecedented opportunities for monitoring large parts of the Earth. In addition to serving the needs of weather forecasting, these observations offer new and exciting opportunities in managing solar power, fighting wildfires, and tracking air pollution. Creation of actionable information in near realtime from these data streams is a challenge that is best addressed through collaborative efforts among the industry, academia and government agencies.
A Permutation Approach to Assess Confounding in Machine Learning Applications for Digital Health
Authors: Elias Chaibub Neto, Abhishek Pratap, Thanneer M. Perumal, Meghasyam Tummalacherla, Brian M. Bot, Lara M. Mangravite, Larsson Omberg
Venue: KDD
Year: 2019

Machine learning applications are often plagued with confounders that can impact the generalizability of the learners. In clinical settings, demographic characteristics often play the role of confounders. Confounding is especially problematic in remote digital health studies where the participants self-select to enter the study, thereby making it difficult to balance the demographic characteristics of participants. One effective approach to combat confounding is to match samples with respect to the confounding variables in order to improve the balance of the data. This procedure, however, leads to smaller datasets and hence negatively impact the inferences drawn from the learners. Alternatively, confounding adjustment methods that make more efficient use of the data (such as inverse probability weighting) usually rely on modeling assumptions, and it is unclear how robust these methods are to violations of these assumptions. Here, instead of proposing a new method to control for confounding, we develop novel permutation based statistical tools to detect and quantify the influence of observed confounders, and estimate the unconfounded performance of the learner. Our tools can be used to evaluate the effectiveness of existing confounding adjustment methods. We evaluate the statistical properties of our methods in a simulation study, and illustrate their application using real-life data from a Parkinson's disease mobile health study collected in an uncontrolled environment.
Spatio-temporal Event Forecasting and Precursor Identification
Authors: Yue Ning, Liang Zhao, Feng Chen, Chang-Tien Lu, Huzefa Rangwala
Venue: KDD
Year: 2019

Spatio-temporal societal event forecasting, which has traditionally been prohibitively challenging, is now becoming possible and experiencing rapid growth thanks to the big data from Open Source Indicators (OSI) such as social media, news sources, blogs, economic indicators, and other meta-data sources. Spatio-temporal societal event forecasting and their precursor discovery benefit the society by providing insight into events such as political crises, humanitarian crises, mass violence, riots, mass migrations, disease outbreaks, economic instability, resource shortages, natural disasters, and others. In contrast to traditional event detection that identifies ongoing events, event forecasting focuses on predicting future events yet to happen. Also different from traditional spatio-temporal predictions on numerical indices, spatio-temporal event forecasting needs to leverage the heterogeneous information from OSI to discover the predictive indicators and mappings to future societal events. While studying large scale societal events, policy makers and practitioners aim to identify precursors to such events to help understand causative attributes and ensure accountability. The resulting problems typically require the predictive modeling techniques that can jointly handle semantic, temporal, and spatial information, and require a design of efficient and interpretable algorithms that scale to high-dimensional large real-world datasets. In this tutorial, we will present a comprehensive review of the state-of-the-art methods for spatio-temporal societal event forecasting. First, we will categorize the inputs OSI and the predicted societal events commonly researched in the literature. Then we will review methods for temporal and spatio-temporal societal event forecasting. Next, we will also discuss the foundations of precursor identification with an introduction to various machine learning approaches that aim to discover precursors while forecasting events. Through the tutorial, we expect to illustrate the basic theoretical and algorithmic ideas and discuss specific applications in all the above settings.
Probabilistic Latent Variable Modeling for Assessing Behavioral Influences on Well-Being
Authors: Ehimwenma Nosakhare, Rosalind W. Picard
Venue: KDD
Year: 2019

Health research has an increasing focus on promoting well-being and positive mental health, to prevent disease and to more effectively treat disorders. The availability of rich multi-modal datasets and advances in machine learning methods are now enabling data science research to begin to objectively assess well-being. However, most existing studies focus on detecting the current state or predicting the future state of well-being using stand-alone health behaviors. There is a need for methods that can handle a complex combination of health behaviors, as arise in real-world data. In this paper, we present a framework to 1) map multi-modal messy data collected in the "wild" to meaningful feature representations of health behavior, and 2) uncover latent patterns comprising multiple health behaviors that best predict well-being. We show how to use supervised latent Dirichlet allocation (sLDA) to model the observed behaviors, and we apply variational inference to uncover the latent patterns. Implementing and evaluating the model on 5,397 days of data from a group of 244 college students, we find that these latent patterns are indeed predictive of self-reported stress, one of the largest components affecting well-being. We investigate the modifiable behaviors present in these patterns and uncover some ways in which the factors work together to influence well-being. This work contributes a new method using objective data analysis to help individuals monitor their well-being using real-world measurements. Insights from this study advance scientific knowledge on how combinations of daily modifiable human behaviors relate to human well-being.