OpenRaiser/Intern-Atlas huggingface dataset:
Applications of AI/ML in Established and New Industries
Authors: Hassan Sawaf
Venue: KDD
Year: 2019

The advent of advanced modeling for general machine learning, and in particular computer vision, speech recognition and natural language processing, the applications of AI is enabling classical businesses to reinvent themselves, and new business fields to arise which were even not imaginable a few years back. Hassan will present some of these use cases, and dive into some in more detail, showing where current and future AI/ML technology is accelerating innovation.
The Error is the Feature: How to Forecast Lightning using a Model Prediction Error
Authors: Christian Schön, Jens Dittrich, Richard Müller
Venue: KDD
Year: 2019

Despite the progress within the last decades, weather forecasting is still a challenging and computationally expensive task. Current satellite-based approaches to predict thunderstorms are usually based on the analysis of the observed brightness temperatures in different spectral channels and emit a warning if a critical threshold is reached. Recent progress in data science however demonstrates that machine learning can be successfully applied to many research fields in science, especially in areas dealing with large datasets. We therefore present a new approach to the problem of predicting thunderstorms based on machine learning. The core idea of our work is to use the error of two-dimensional optical flow algorithms applied to images of meteorological satellites as a feature for machine learning models. We interpret that optical flow error as an indication of convection potentially leading to thunderstorms and lightning. To factor in spatial proximity we use various manual convolution steps. We also consider effects such as the time of day or the geographic location. We train different tree classifier models as well as a neural network to predict lightning within the next few hours (called nowcasting in meteorology) based on these features. In our evaluation section we compare the predictive power of the different models and the impact of different features on the classification result. Our results show a high accuracy of 96% for predictions over the next 15 minutes which slightly decreases with increasing forecast period but still remains above 83% for forecasts of up to five hours. The high false positive rate of nearly 6% however needs further investigation to allow for an operational use of our approach.
Context by Proxy: Identifying Contextual Anomalies Using an Output Proxy
Authors: Jan-Philipp Schulze, Artur Mrowca, Elizabeth Ren, Hans-Andrea Loeliger, Konstantin Böttinger
Venue: KDD
Year: 2019

Contextual anomalies arise only under special internal or external stimuli in a system, often making it infeasible to detect them by a rule-based approach. Labelling the underlying problem sources is hard because complex, time-dependent relationships between the inputs arise. We propose a novel unsupervised approach that combines tools from deep learning and signal processing, working in a purely data-driven way. Many systems show a desirable target behaviour which can be used as a proxy quantity removing the need to manually label data. The methodology was evaluated on real-life test car traces in the form of multivariate state message sequences. We successfully identified contextual anomalies during the cars' timeout process along with possible explanations. Novel input encodings allow us to summarise the entire system context including the timing such that more information is available during the decision process.
Constructing and Mining Heterogeneous Information Networks from Massive Text
Authors: Jingbo Shang, Jiaming Shen, Liyuan Liu, Jiawei Han
Venue: KDD
Year: 2019

Real-world data exists largely in the form of unstructured texts. A grand challenge on data mining research is to develop effective and scalable methods that may transform unstructured text into structured knowledge. Based on our vision, it is highly beneficial to transform such text into structured heterogeneous information networks, on which actionable knowledge can be generated based on the user's need. In this tutorial, we provide a comprehensive overview on recent research and development in this direction. First, we introduce a series of effective methods that construct heterogeneous information networks from massive, domain-specific text corpora. Then we discuss methods that mine such text-rich networks based on the user's need. Specifically, we focus on scalable, effective, weakly supervised, language-agnostic methods that work on various kinds of text. We further demonstrate, on real datasets (including news articles, scientific publications, and product reviews), how information networks can be constructed and how they can assist further exploratory analysis.
Challenges, Best Practices and Pitfalls in Evaluating Results of Online Controlled Experiments
Authors: Xiaolin Shi, Pavel A. Dmitriev, Somit Gupta, Xin Fu
Venue: KDD
Year: 2019

A/B Testing is the gold standard to estimate the causal relationship between a change in a product and its impact on key outcome measures. It is widely used in the industry to test changes ranging from simple copy change or UI change to more complex changes like using machine learning models to personalize user experience. The key aspect of A/B testing is evaluation of experiment results. Designing the right set of metrics - correct outcome measures, data quality indicators, guardrails that prevent harm to business, and a comprehensive set of supporting metrics to understand the "why" behind the key movements is the #1 challenge practitioners face when trying to scale their experimentation program [18, 22]. On the technical side, improving sensitivity of experiment metrics is a hard problem and an active research area, with large practical implications as more and more small and medium size businesses are trying to adopt A/B testing and suffer from insufficient power. In this tutorial we will discuss challenges, best practices, and pitfalls in evaluating experiment results, focusing on both lessons learned and practical guidelines as well as open research questions.
State-Sharing Sparse Hidden Markov Models for Personalized Sequences
Authors: Hongzhi Shi, Chao Zhang, Quanming Yao, Yong Li, Funing Sun, Depeng Jin
Venue: KDD
Year: 2019

Hidden Markov Model (HMM) is a powerful tool that has been widely adopted in sequence modeling tasks, such as mobility analysis, healthcare informatics, and online recommendation. However, using HMM for modeling personalized sequences remains a challenging problem: training a unified HMM with all the sequences often fails to uncover interesting personalized patterns; yet training one HMM for each individual inevitably suffers from data scarcity. We address this challenge by proposing a state-sharing sparse hidden Markov model (S3HMM) that can uncover personalized sequential patterns without suffering from data scarcity. This is achieved by two design principles: (1) all the HMMs in the ensemble share the same set of latent states; and (2) each HMM has its own transition matrix to model the personalized transitions. The result optimization problem for S3HMM becomes nontrivial, because of its two-layer hidden state design and the non-convexity in parameter estimation. We design a new Expectation-Maximization algorithm based, which treats the difference of convex programming as a sub-solver to optimize the non-convex function in the M-step with convergence guarantee. Our experimental results show that, S3HMM can successfully uncover personalized sequential patterns in various applications and outperforms baselines significantly in downstream prediction tasks.
Blending Noisy Social Media Signals with Traditional Movement Variables to Predict Forced Migration
Authors: Lisa Singh, Laila Wahedi, Yanchen Wang, Yifang Wei, Christo Kirov, Susan Martin, Katharine M. Donato, Yaguang Liu, Kornraphop Kawintiranon
Venue: KDD
Year: 2019

Worldwide displacement due to war and conflict is at all-time high. Unfortunately, determining if, when, and where people will move is a complex problem. This paper proposes integrating both publicly available organic data from social media and newspapers with more traditional indicators of forced migration to determine when and where people will move. We combine movement and organic variables with spatial and temporal variation within different Bayesian models and show the viability of our method using a case study involving displacement in Iraq. Our analysis shows that incorporating open-source generated conversation and event variables maintains or improves predictive accuracy over traditional variables alone. This work is an important step toward understanding how to leverage organic big data for societal--scale problems.
Mining and Model Understanding on Medical Data
Authors: Myra Spiliopoulou, Panagiotis Papapetrou
Venue: KDD
Year: 2019

What are the basic forms of healthcare data? How are Electronic Health Records and Cohorts structured? How can we identify the key variables in such data and how important are temporal abstractions? What are the main challenges in knowledge extraction from medical data sources? What are the key machine algorithms used for this purpose? What are the main questions that clinicians and medical experts pose to machine learning researchers? In this tutorial, we provide answers to these questions by presenting state-of-the-art methods, workflows, and tools for mining and understanding medical data. Particular emphasis is given on temporal abstractions, knowledge extraction from cohorts, machine learning model interpretability, and mHealth.
AI for Small Businesses and Consumers: Applications and Innovations
Authors: Ashok Srivastava
Venue: KDD
Year: 2019

Small businesses are the lifeblood of the U.S. economy, representing an astounding 99.9 percent of all businesses, creating two-thirds of net new jobs, and accounting for 44 percent of economic activity. Yet, 50 percent of small businesses go out of business in the first 5 years. What's behind this dismal statistic? Among the top contributing factors is cash flow management. Owners who cannot efficiently manage the inflow and outflow of cash are almost certain to fail. And, those who can are more likely to break through the statistical 5-year barrier to build thriving businesses. In this talk, we'll describe novel applications of artificial intelligence and large-scale machine learning aimed at addressing the problem of forecasting cash flow for small businesses. These are sparse, high-dimensional correlated time series. We'll present new results on forecasting this type of time series, using scalable Gaussian Processes with kernels formed through the use of deep learning. These methods yield highly accurate predictions but also include a principled approach for generating confidence intervals.
Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learning
Authors: Megha Srivastava, Hoda Heidari, Andreas Krause
Venue: KDD
Year: 2019

Fairness for Machine Learning has received considerable attention, recently. Various mathematical formulations of fairness have been proposed, and it has been shown that it is impossible to satisfy all of them simultaneously. The literature so far has dealt with these impossibility results by quantifying the tradeoffs between different formulations of fairness. Our work takes a different perspective on this issue. Rather than requiring all notions of fairness to (partially) hold at the same time, we ask which one of them is the most appropriate given the societal domain in which the decision-making model is to be deployed. We take a descriptive approach and set out to identify the notion of fairness that best captures lay people's perception of fairness. We run adaptive experiments designed to pinpoint the most compatible notion of fairness with each participant's choices through a small number of tests. Perhaps surprisingly, we find that the most simplistic mathematical definition of fairness---namely, demographic parity---most closely matches people's idea of fairness in two distinct application scenarios. This conclusion remains intact even when we explicitly tell the participants about the alternative, more complicated definitions of fairness, and we reduce the cognitive burden of evaluating those notions for them. Our findings have important implications for the Fair ML literature and the discourse on formalizing algorithmic fairness.
Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network
Authors: Ya Su, Youjian Zhao, Chenhao Niu, Rong Liu, Wei Sun, Dan Pei
Venue: KDD
Year: 2019

Industry devices (i.e., entities) such as server machines, spacecrafts, engines, etc., are typically monitored with multivariate time series, whose anomaly detection is critical for an entity's service quality management. However, due to the complex temporal dependence and stochasticity of multivariate time series, their anomaly detection remains a big challenge. This paper proposes OmniAnomaly, a stochastic recurrent neural network for multivariate time series anomaly detection that works well robustly for various devices. Its core idea is to capture the normal patterns of multivariate time series by learning their robust representations with key techniques such as stochastic variable connection and planar normalizing flow, reconstruct input data by the representations, and use the reconstruction probabilities to determine anomalies. Moreover, for a detected entity anomaly, OmniAnomaly can provide interpretations based on the reconstruction probabilities of its constituent univariate time series. The evaluation experiments are conducted on two public datasets from aerospace and a new server machine dataset (collected and released by us) from an Internet company. OmniAnomaly achieves an overall F1-Score of 0.86 in three real-world datasets, signicantly outperforming the best performing baseline method by 0.09. The interpretation accuracy for OmniAnomaly is up to 0.89.
The Impact of Person-Organization Fit on Talent Management: A Structure-Aware Convolutional Neural Network Approach
Authors: Ying Sun, Fuzhen Zhuang, Hengshu Zhu, Xin Song, Qing He, Hui Xiong
Venue: KDD
Year: 2019

Person-Organization fit (P-O fit) refers to the compatibility between employees and their organizations. The study of P-O fit is important for enhancing proactive talent management. While considerable efforts have been made in this direction, it still lacks a quantitative and holistic way for measuring P-O fit and its impact on talent management. To this end, in this paper, we propose a novel data-driven neural network approach for dynamically modeling the compatibility in P-O fit and its meaningful relationships with two critical issues in talent management, namely talent turnover and job performance. Specifically, inspired by the practical management scenarios, we first creatively design an Organizational Structure-aware Convolutional Neural Network (OSCN) for hierarchically extracting organization-aware compatibility features for measuring P-O fit. Then, to capture the dynamic nature of P-O fit and its consequent impact, we further exploit an adapted Recurrent Neural Network with attention mechanism to model the temporal information of P-O fit. Finally, we compare our approach with a number of state-of-the-art baseline methods on real-world talent data. Experimental results clearly demonstrate the effectiveness in terms of turnover prediction and job performance prediction. Moreover, we also show some interesting indicators of talent management through the visualization of network layers.
From Code to Data: AI at Scale for Developer Productivity
Authors: Neel Sundaresan
Venue: KDD
Year: 2019

The last decade has seen three great phenomena in computing - the rebirth of AI algorithms and AI hardware; the evolution of cloud computing and distributed software development; and the explosive growth of open source software that has led to the availability of code as data, and its associated metadata, at scale. In this talk, we will describe how we take advantage of innovations in these dimensions to improve developer productivity and infuse AI and automation into software processes. We will discuss examples of how we built intelligent software by creating AI algorithms driven by deep understanding of code as data. In addition, we will talk about how data can also be treated as code for the next-generation AI-infused software development.
TV Advertisement Scheduling by Learning Expert Intentions
Authors: Yasuhisa Suzuki, Wemer M. Wee, Itaru Nishioka
Venue: KDD
Year: 2019

This paper considers the automation of a typical complex advertisement scheduling system in broadcast television (TV) networks. Compared to traditional TV advertisement scheduling, we consider the case where not all requests for advertising slots are known at the same time, and time-consuming negotiations related to balancing the TV network's and advertisers' priorities have to be minimized. Although there are existing works that automatically provide schedules using mathematical optimization, the applicability of these techniques to our problem is limited due to the cumbersome formulations necessary for handling vague conditions and aesthetic domain-specific rules necessary for advertisers' satisfaction. To automate the system, we propose a data-driven approach that uses intention learning on top of mathematical optimization and clustering to imitate the decision-making process of scheduling experts. The scheduling of TV ads is automated via mathematical programming, and the expert objectives and constraints are learned from historical demonstrations using inverse optimization. The clustering of TV ads and the learning of associated intentions are used to deal with the cold start problem related to ad requests from new companies or products. Our proposed system is validated on actual dataset from a Japanese TV network. Experiments show that our system can more closely reproduce the experts' schedules compared to standard optimization approaches, demonstrating the potential of our work in reducing personnel costs and improving advertisers' experience. Based on its promising results, our proposed system is being prepared for commercial deployment.
MSURU: Large Scale E-commerce Image Classification with Weakly Supervised Search Data
Authors: Yina Tang, Fedor Borisyuk, Siddarth Malreddy, Yixuan Li, Yiqun Liu, Sergey Kirshner
Venue: KDD
Year: 2019

In this paper we present a deployed image recognition system used in a large scale commerce search engine, which we call MSURU. It is designed to process product images uploaded daily to Facebook Marketplace. Social commerce is a growing area within Facebook and understanding visual representations of product content is important for search and recommendation applications on Marketplace. In this paper, we present techniques we used to develop efficient large-scale image classifiers using weakly supervised search log data. We perform extensive evaluation of presented techniques, explain practical experience of developing large-scale classification systems and discuss challenges we faced. Our system, MSURU out-performed current state of the art system developed at Facebook [23] by 16% in e-commerce domain. MSURU is deployed to production with significant improvements in search success rate and active interactions on Facebook Marketplace.
Analytics Journey Map: An Approach Enable to ML at Scale
Authors: Ganesh Thondikulam
Venue: KDD
Year: 2019

Data scientists today spend significant portions of their time in finding and preparing data rather than analyzing, building and deploying models. The challenges and opportunities will be explored by using a design thinking approach, to increase Analytics / ML throughput at scale.
A Severity Score for Retinopathy of Prematurity
Authors: Peng Tian, Yuan Guo, Jayashree Kalpathy-Cramer, Susan Ostmo, John Peter Campbell, Michael F. Chiang, Jennifer G. Dy, Deniz Erdogmus, Stratis Ioannidis
Venue: KDD
Year: 2019

Retinopathy of Prematurity (ROP) is a leading cause for childhood blindness worldwide. An automated ROP detection system could significantly improve the chance of a child receiving proper diagnosis and treatment. We propose a means of producing a continuous severity score in an automated fashion, regressed from both (a) diagnostic class labels as well as (b) comparison outcomes. Our generative model combines the two sources, and successfully addresses inherent variability in diagnostic outcomes. In particular, our method exhibits an excellent predictive performance of both diagnostic and comparison outcomes over a broad array of metrics, including AUC, precision, and recall.
AutoNE: Hyperparameter Optimization for Massive Network Embedding
Authors: Ke Tu, Jianxin Ma, Peng Cui, Jian Pei, Wenwu Zhu
Venue: KDD
Year: 2019

Network embedding (NE) aims to embed the nodes of a network into a vector space, and serves as the bridge between machine learning and network data. Despite their widespread success, NE algorithms typically contain a large number of hyperparameters for preserving the various network properties, which must be carefully tuned in order to achieve satisfactory performance. Though automated machine learning (AutoML) has achieved promising results when applied to many types of data such as images and texts, network data poses great challenges to AutoML and remains largely ignored by the literature of AutoML. The biggest obstacle is the massive scale of real-world networks, along with the coupled node relationships that make any straightforward sampling strategy problematic. In this paper, we propose a novel framework, named AutoNE, to automatically optimize the hyperparameters of a NE algorithm on massive networks. In detail, we employ a multi-start random walk strategy to sample several small sub-networks, perform each trial of configuration selection on the sampled sub-network, and design a meta-leaner to transfer the knowledge about optimal hyperparameters from the sub-networks to the original massive network. The transferred meta-knowledge greatly reduces the number of trials required when predicting the optimal hyperparameters for the original network. Extensive experiments demonstrate that our framework can significantly outperform the existing methods, in that it needs less time and fewer trials to find the optimal hyperparameters.
Towards Identifying Impacted Users in Cellular Services
Authors: Shobha Venkataraman, Jia Wang
Venue: KDD
Year: 2019

An essential step in the customer care routine of cellular service carriers is determining whether an individual user is impacted by on-going service issues. This is traditionally done by monitoring the network and the services. However, user feedback data, generated when users call customer care agents with problems, is a complementary source of data for this purpose. User feedback data is particularly valuable as it provides the user perspective of the service issues. However, this data is extremely noisy, due to range of issues that users have and the diversity of the language used by care agents. In this paper, we present LOTUS, a system that identifies users impacted by a common root cause (such as a network outage) from user feedback. LOTUS is based on novel algorithmic framework that tightly couples co-training and spatial scan statistics. To model the text in the user feedback, LOTUS also incorporates custom-built language models using deep sequence learning. Through experimental analysis on synthetic and live data, we demonstrate the accuracy of LOTUS. LOTUS has been deployed for several months, and has identified the impact over 200 events.