{"id":599946,"date":"2019-07-30T11:54:21","date_gmt":"2019-07-30T18:54:21","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-event&p=599946"},"modified":"2025-08-06T11:56:22","modified_gmt":"2025-08-06T18:56:22","slug":"kdd-2019","status":"publish","type":"msr-event","link":"https:\/\/www.microsoft.com\/en-us\/research\/event\/kdd-2019\/","title":{"rendered":"Microsoft at KDD 2019"},"content":{"rendered":"\n\n
Venue:<\/strong> Dena\u2019ina Convention Center and William Egan Convention Center<\/p>\n Website:<\/strong> KDD 2019 (opens in new tab)<\/span><\/a>Opens in a new tab<\/span><\/p>\n Microsoft is excited to be a Bronze sponsor of the 25th<\/sup> ACM SIGKDD Conference on Knowledge Discovery and Data Mining (opens in new tab)<\/span><\/a>. We will have over 30 Microsoft attendees present at the conference. Stop by our booth to chat with our experts, see demos of our latest research and find out about career opportunities (opens in new tab)<\/span><\/a>\u00a0with Microsoft.<\/p>\n Find more information about KDD 2019 on Microsoft Academic site: aka.ms\/KDD2019Papers (opens in new tab)<\/span><\/a><\/p>\n Andreas Argyriou 8:00 AM\u201312:00 PM | Tutorial 8:00 AM\u201312:00 PM | Tutorial 8:00 AM\u20135:00 PM | Tutorial 8:00 AM\u201312:00 PM 1:00 PM\u20135:00 PM 7:00 PM\u20139:00 PM | Idlughet (Eklutna) Exhibit Hall 7:00 PM\u20139:00 PM | Idlughet (Eklutna) Exhibit Hall 7:00 PM\u20139:00 PM | Idlughet (Eklutna) Exhibit Hall 7:00 PM\u20139:00 PM | Idlughet (Eklutna) Exhibit Hall 7:00 PM\u20139:00 PM | Idlughet (Eklutna) Exhibit Hall 7:00 PM\u20139:00 PM | Idlughet (Eklutna) Exhibit Hall 7:00 PM\u20139:00 PM | Idlughet (Eklutna) Exhibit Hall 8:00 AM\u20139:30 AM | Dena\u2019ina Center 9:30 AM\u201312:30 PM | Hands-on Tutorial 10:00 AM\u201312:00 PM | Auto-ML and Development Frameworks | Summit 1 1:30 PM\u20133:30 PM | Applied Data Science | Cook Room 1:30 PM\u20133:30 PM | Language Models and Text Mining | Summit 1 1:30 PM\u20134:30 PM | Hands-on Tutorial 4:00 PM\u20136:00 PM | Real-Time and Online | Summit 1 7:00 PM\u20139:00 PM | Idlughet (Eklutna) Exhibit Hall 7:00 PM\u20139:00 PM | Idlughet (Eklutna) Exhibit Hall 7:00 PM\u20139:00 PM | Idlughet (Eklutna) Exhibit Hall 7:00 PM\u20139:00 PM | Idlughet (Eklutna) Exhibit Hall 7:00 PM\u20139:00 PM | Idlughet (Eklutna) Exhibit Hall 8:00 AM\u201311:45 AM | Social Impact Workshop 8:30 AM\u20139:00 AM | Project Showcase 9:30 AM\u201312:30 PM | Hands-on Tutorial 10:00 AM\u201312:00 PM | Embeddings II | Summit 2 10:00 AM\u201312:00 PM | Clustering and Visualization | Summit 3 10:00 AM\u201312:00 PM | Clustering and Visualization | Summit 3 10:00 AM\u201312:00 PM | Applied Data Science | Cook Room 10:00 AM\u201312:00 PM | Scalability and Novel Applications | Summit 1 1:30 PM\u20133:30 PM | Applied Data Science | Cook Room 1:30 PM\u20133:30 PM | Environment and Sustainability | Summit 1 9:30 AM\u20134:00 PM | Hands-on Tutorial 10:00 AM\u201312:00 PM | Sensor and Consumer Services | Summit 1 10:00 AM\u201312:00 PM | Machine Learning Themes I | Summit 2Microsoft attendees<\/h3>\n
\nSarah Bird<\/a>
\nJohnny Chan
\nTom Drabas
\nAleksander Fabijan
\nShengyu Fu
\nJohannes Gehrke<\/a>
\nSomit Gupta
\nJuan-Arturo Herrera
\nCongrui Huang
\nPawel Janowski
\nLi Jiang
\nRonny Kohavi
\nGopi Kumar
\nPlatina Liu
\nYuchao Liu
\nVani Mandava<\/a>
\nDaniel Miller
\nNeel Sundaresan
\nAlexey Svyatkovskiy
\nVinitra Swamy
\nAngus Taylor
\nChi Wang<\/a>
\nYujing Wang
\nMarkus Weimer<\/a>
\nXing Xie<\/a>
\nMichele ZunkerOpens in a new tab<\/span><\/p>\nSunday, August 4<\/h3>\n
\nChallenges, Best Practices and Pitfalls in Evaluating Results of Online Controlled Experiments<\/strong>
\nXiaolin Shi, Pavel Dmitriev, Somit Gupta<\/strong>, Xin Fu<\/p>\n
\nFairness-Aware Machine Learning: Practical Challenges and Lessons Learned<\/strong>
\nSarah Bird<\/strong><\/a>, Ben Hutchinson, Krishnaram Kenthapadi, Emre K\u0131c\u0131man<\/strong><\/a>, Margaret Mitchell<\/p>\n
\nLearning From Networks: Algorithms, Theory, & Applications<\/strong>
\nXiao Huang, Peng Cui, Yuxiao Dong<\/strong><\/a>, Jundong Li, Huan Liu, Jian Pei, Le Song, Jie Tang, Fei Wang, Hongxia Yang, Wenwu Zhu<\/p>\nMonday, August 5<\/h3>\n
\nKDD 2019 Workshop on Causal Discovery (CD2019)<\/strong>
\nOrganizers: Thuc Le, Jiuyong Li, Kun Zhang, Emre K\u0131c\u0131man<\/strong><\/a>, Peng Cui, Aapo Hyv\u00e4rinen
\nKeynote speaker: Ronny Kohavi<\/strong><\/p>\n
\nTruth Discovery and Fact Checking: Theory and Practice<\/strong>
\nOrganizers: Subhabrata Mukherjee<\/strong><\/a>, Qi Li, Cong Yu, Jiawei Han
\nKeynote speaker: Emre Kiciman<\/strong><\/a>
\nSpotlight speaker: Subhabrata Mukherjee<\/strong><\/a><\/p>\n
\nIndividualized Indicator for All: Stock-wise Technical Indicator Optimization with Stock Embedding<\/strong>
\nZhige Li, Derek Yang, Li Zhao<\/strong><\/a>, Jiang Bian<\/strong><\/a>, Tao Qin<\/strong><\/a>, Tie-Yan Liu<\/strong><\/a><\/p>\n
\nA Multiscale Scan Statistic for Adaptive Submatrix Localization<\/strong>
\nYuchao Liu<\/strong>, Ery Arias-Castro<\/p>\n
\nDeepGBM: A Deep Learning Framework Distilled by GBDT for Online Prediction Tasks<\/strong>
\nGuolin Ke<\/strong><\/a>, Zhenhui Xu, Jia Zhang<\/strong>, Jiang Bian<\/strong><\/a>, Tie-Yan Liu<\/strong><\/a><\/p>\n
\nAxiomatic Interpretability for Multiclass Additive Models<\/strong>
\nXuezhou Zhang, Sarah Tan, Paul Koch<\/strong><\/a>, Urszula Chajewska<\/strong>, Rich Caruana<\/strong><\/a><\/p>\n
\n\u03bbOpt: Learn to Regularize Recommender Models in Finer Levels<\/strong>
\nYihong Chen, Bei Chen<\/strong><\/a>, Xiangnan He, Chen Gao, Yong Li, Jian-Guang Lou<\/strong><\/a>, Yue Wang<\/p>\n
\nFactorization Bandits for Online Influence Maximization<\/strong>
\nQingyun Wu, Zhige Li, Huazheng Wang, Wei Chen<\/strong><\/a>, Hongning Wang<\/p>\n
\nTime-Series Anomaly Detection Service at Microsoft<\/strong>
\nHansheng Ren<\/strong>, Bixiong Xu<\/strong>, Yujing Wang<\/strong>, Chao Yi<\/strong>, Congrui Huang<\/strong>, Tony Xing<\/strong>, Xiaoyu Kou<\/strong>, Mao Yang<\/strong><\/a>, Jie Tong<\/strong>, Qi Zhang<\/strong><\/p>\nTuesday, August 6<\/h3>\n
\nOpening Keynote Address<\/strong>
\nKeynote Speaker: Peter Lee<\/strong><\/a><\/p>\n
\nDemocratizing & Accelerating AI through Automated Machine Learning<\/strong>
\nParashar Shah<\/strong>, Krishna Anumalasetty<\/strong><\/p>\n
\nPythia: AI-assisted Code Completion System<\/strong>
\nAlexey Svyatkovskiy<\/strong>, Ying Zhao<\/strong>, Shengyu Fu<\/strong>, Neel Sundaresan<\/strong><\/p>\n
\nPreventing Rhino Poaching through Machine Learning<\/strong>
\nOlga Liakhovich<\/strong>, Gabriel Dominguez Conde<\/p>\n
\nDetection of Review Abuse via Semi-Supervised Binary Multi-Target Tensor Decomposition<\/strong>
\nAnil R Yelundur, Vineet Chaoji, Bamdev Mishra<\/strong><\/p>\n
\nCloud-Based Data Science at the Speed of Thought Using RAPIDS – the Open GPU Data Science Ecosystem<\/strong>
\nBrad Rees, Bartley Richardson, Tom Drabas<\/strong>, Keith Kraus, Corey Nolet, Juan-Arturo Herrera<\/strong><\/p>\n
\nTime-Series Anomaly Detection Service at Microsoft<\/strong>
\nHansheng Ren<\/strong>, Bixiong Xu<\/strong>, Yujing Wang<\/strong>, Chao Yi<\/strong>, Congrui Huang<\/strong>, Tony Xing<\/strong>, Xiaoyu Kou<\/strong>, Mao Yang<\/strong><\/a>, Jie Tong<\/strong>, Qi Zhang<\/strong><\/p>\n
\nDiagnosing Sample Ratio Mismatch in Online Controlled Experiments: A Taxonomy and Rules of Thumb for Practitioners<\/strong>
\nAleksander Fabijan<\/strong>, Jayant Gupchup<\/strong>, Somit Gupta<\/strong>, Jeff Omhover<\/strong>, Wen Qin<\/strong>, Lukas Vermeer, Pavel Dmitriev<\/p>\n
\nFast Approximation of Empirical Entropy via Subsampling<\/strong>
\nChi Wang<\/strong><\/a>, Bailu Ding<\/strong><\/a><\/p>\n
\nMachine Learning at Microsoft with ML.NET<\/strong>
\nZeeshan Ahmed<\/strong>, Saeed Amizadeh<\/strong>, Mikhail Bilenko<\/strong>, Rogan Carr<\/strong>, Wei-Sheng Chin<\/strong>, Yael Dekel<\/strong>, Xavier Dupre<\/strong>, Vadim Eksarevskiy<\/strong>, Senja Filipi<\/strong>, Tom Finley<\/strong>, Abhishek Goswami<\/strong>, Monte Hoover<\/strong>, Scott Inglis<\/strong>, Matteo Interlandi<\/strong>, Najeeb Kazmi<\/strong>, Gleb Krivosheev<\/strong>, Pete Luferenko<\/strong>, Ivan Matantsev<\/strong>, Sergiy Matusevych<\/strong>, Shahab Moradi<\/strong>, Gani Nazirov<\/strong>, Justin Ormont<\/strong>, Gal Oshri<\/strong>, Artidoro Pagnoni<\/strong>, Jignesh Parmar<\/strong>, Prabhat Roy<\/strong>, Zeeshan Siddiqui<\/strong>, Markus Weimer<\/strong><\/a>, Shauheen Zahirazami<\/strong>, Yiwen Zhu<\/strong><\/p>\n
\nNPA: Neural News Recommendation with Personalized Attention<\/strong>
\nChuhan Wu, Fangzhao Wu<\/strong><\/a>, Mingxiao An, Jianqiang Huang, Yongfeng Huang, Xing Xie<\/strong><\/a><\/p>\n
\nOn Dynamic Network Models and Application to Causal Impact<\/strong>
\nYu-Chia Chen, Avleen S. Bijral<\/strong>, Juan Lavista Ferres<\/strong><\/p>\nWednesday, August 7<\/h3>\n
\nMachine Learning for Humanitarian Data: Tag Prediction using the HXL Standard<\/strong>
\nVinitra Swamy<\/strong>, Elisa Chen, Anish Vankayalapati, Abhay Aggarwal, Chloe Liu, Vani Mandava<\/strong><\/a>, Simon Johnson<\/p>\n
\nBig Data Challenges and Opportunities: A Case Study in Microsoft Academic<\/strong>
\nKeynote speaker: Kuansan Wang<\/strong><\/a><\/p>\n
\nDeep Learning for Time Series Forecasting<\/strong>
\nYijing Chen<\/strong>, Dmitry Pechyoni<\/strong>, Angus Taylor<\/strong>, Vanja Paunic<\/strong><\/p>\n
\nIndividualized Indicator for All: Stock-wise Technical Indicator Optimization with Stock Embedding<\/strong>
\nZhige Li, Derek Yang, Li Zhao<\/strong><\/a>, Jiang Bian<\/strong><\/a>, Tao Qin<\/strong><\/a>, Tie-Yan Liu<\/strong><\/a><\/p>\n
\nA Multiscale Scan Statistic for Adaptive Submatrix Localization<\/strong>
\nYuchao Liu<\/strong>, Ery Arias-Castro<\/p>\n
\nScalable Hierarchical Clustering with Tree Grafting<\/strong>
\nNicholas Monath, Ari Kobren, Akshay Krishnamurthy<\/strong><\/a>, Michael Glass, Andrew McCallum<\/p>\n
\nFrom Code to Data: AI at Scale for Developer Productivity<\/strong>
\nNeel Sundaresan<\/strong><\/p>\n
\nDeepRoof: A Data-driven Approach For Solar Potential Estimation Using Rooftop Imagery<\/strong>
\nStephen Lee, Srinivasan Iyengar<\/strong>, Menghong Feng, Prashant Shenoy, Subhransu Maji<\/p>\n
\nFriends Don\u2019t Let Friends Deploy Black-Box Models: The Importance of Intelligibility in Machine Learning<\/strong>
\nRich Caruana<\/strong><\/a><\/p>\n
\nAccuAir: Winning Solution to Air Quality Prediction for KDD Cup 2018<\/strong>
\nZhipeng Luo, Jianqiang Huang, Ke Hu, Xue Li<\/strong>, Peng Zhang<\/p>\nThursday, August 8<\/h3>\n
\nFrom Graph to Knowledge Graph: Mining Large-scale Heterogeneous Networks Using Spark<\/strong>
\nIris Shen<\/strong><\/a>, Charles Huang<\/strong><\/a>, Chieh-Han Wu<\/strong>, Anshul Kanakia<\/strong><\/p>\n
\nLearning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data<\/strong>
\nJackson A. Killian, Bryan Wilder, Amit Sharma<\/strong><\/a>, Vinod Choudhary, Bistra Dilkina, Milind Tambe<\/p>\n
\nDeepGBM: A Deep Learning Framework Distilled by GBDT for Online Prediction Tasks<\/strong>
\nGuolin Ke<\/strong><\/a>, Zhenhui Xu, Jia Zhang<\/strong>, Jiang Bian<\/strong><\/a>,