Abstract:This study is concerned with the determination of optimal appointment times for a sequence of jobs with uncertain duration. We investigate the data-driven Appointment Scheduling Problem (ASP) when one has $n$ observations of $p$ features (covariates) related to the jobs as well as historical data. We formulate ASP as an Integrated Estimation and Optimization problem using a task-based loss function. We justify the use of contexts by showing that not including the them yields to inconsistent decisions, which translates to sub-optimal appointments. We validate our approach through two numerical experiments.
Abstract:Fraudulent activities related to online advertising can potentially harm the trust advertisers put in advertising networks and sour the gaming experience for users. Pay-Per-Click/Install (PPC/I) advertising is one of the main revenue models in game monetization. Widespread use of the PPC/I model has led to a rise in click/install fraud events in games. The majority of traffic in ad networks is non-fraudulent, which imposes difficulties on machine learning based fraud detection systems to deal with highly skewed labels. From the ad network standpoint, user activities are multi-type sequences of temporal events consisting of event types and corresponding time intervals. Time Long Short-Term Memory (Time-LSTM) network cells have been proved effective in modeling intrinsic hidden patterns with non-uniform time intervals. In this study, we propose using a variant of Time-LSTM cells in combination with a modified version of Sequence Generative Adversarial Generative (SeqGAN)to generate artificial sequences to mimic the fraudulent user patterns in ad traffic. We also propose using a Critic network instead of Monte-Carlo (MC) roll-out in training SeqGAN to reduce computational costs. The GAN-generated sequences can be used to enhance the classification ability of event-based fraud detection classifiers. Our extensive experiments based on synthetic data have shown the trained generator has the capability to generate sequences with desired properties measured by multiple criteria.