Abstract of the talk:

Budget allocation of marketplace levers, such as incentives for drivers to complete certain trips or promotions for riders to take more trips have long been both a technical and business challenge at Uber. It is crucial to understand the impact of lever budget changes on the market and to estimate their cost efficiency given the need to achieve predefined budgets, where the eventual goal is to find the optimal allocations under those constraints that maximize some objective of value to the business. We introduce an end-to-end machine learning and optimization procedure to automate budget decision-making for cities where Uber operates. We propose a state-of-the-art deep learning (DL) estimator based on S-Learner that leverages a massive amount of user experimental and temporal-spatial observational data. We also built a novel tensor B-Spline regression model to enforce efficiency shape control using experimental data while retaining the sophistication of the DL models’ response surface. This procedure has demonstrated substantial improvement in Uber’s ability to allocate resources efficiently.

About the Speaker:

Vinayak Iyer, Senior Applied Scientist at Uber, with a PhD in Economics from Columbia University. Vinayak specializes in incentive budget allocation and structural pricing optimization.