Schedule: Temporal data and time-series sessions

Ira Cohen shares a novel approach for building more reliable prediction models by integrating anomalies in them. Arun Kejariwal then walks you through how to marry correlation analysis with anomaly detection, discusses how the topics are intertwined, and details the challenges you may encounter based on production data. Read more.

KC Tung explains why LSTM provides great flexibility to model the consumer touchpoint sequence problem in a way that allows just-in-time insights about an advertising campaign's effectiveness across all touchpoints (channels), empowering advertisers to evaluate, adjust, or reallocate resources or investments in order to maximize campaign effectiveness. Read more.

Jian Wu discusses an end-to-end engineering project to train and evaluate deep Q-learning models for targeting sequential marketing campaigns using the 10-fold cross-validation method. Jian also explains how to evaluate trained DQN models with neural network-based baseline models and shows that trained deep Q-learning models generally produce better-optimized long-term rewards. Read more.

Online fraud is often orchestrated by organized crime rings, who use malicious user accounts to actively target modern online services for financial gain. Ting-Fang Yen shares a real-time, scalable fraud detection solution backed by deep learning and built on Spark and TensorFlow and demonstrates how the system outperforms traditional solutions such as blacklists and machine learning. Read more.

Estimating the performance of heavy earth-moving equipment on large construction projects is a complex task that can be riddled with uncertainty. Ramzi Roy Labban details how CCC uses machine learning, leveraging large datasets of actual performance of trucks on construction sites, to more accurately predict future performance and allow the company to make realistic performance assumptions. Read more.

Lydia Liu discusses the results of research on how static fairness criteria interact with temporal indicators of well-being. These results highlight the importance of measurement and temporal modeling in the evaluation of fairness criteria and suggest a range of new challenges and trade-offs. Read more.