Maplebear Inc.

Konrad Gustav Miziolek

Bryan Daniel Bor

Ajay Pankaj Sampat

Darren Johnson

Parikshit Verma

Jacob Jensen

Nicholas William Sturm

A variation testing system environment for simulating adaptive experiments of objects is disclosed. An experiment system conducts one or more simulations of an adaptive experiment that includes a plurality of variants of an object. Simulation results based on the one or more simulations are generated that are indicative of at least an estimated amount of time to conduct a real-world adaptive experiment based on the one or more simulations.

Simulating adaptive experiments for feasibility analysis

A method or a system for using machine learning to dynamically boost order delivery time. The system receives an order associated with a delivery time and a compensation value. The system applies a machine-learning model to an order to predict an amount of lateness time that an order will be fulfilled late. The system then determines a lateness value based in part on the predicted amount of lateness time. The lateness value indicates a penalty caused by the predicted amount of lateness time. For each of a plurality of proposed boost amounts for the compensation value, the system determines an uplift, indicating a reduction of the lateness value caused by the boost amount. The system then selects a boost amount from the plurality of boost amounts based in part on the determined uplifts, causing the order to be accepted sooner to thereby boost order delivery time.

Machine learning model for dynamically boosting order delivery time

A user treatment engine uses user data describing characteristics of a user to evaluate a set of treatments that the user treatment engine may apply to the user. The user treatment engine generates treatment cost predictions for the treatments and generates treatment scores for the set of treatments based on the treatment cost predictions for the treatments and the user data for the user. The user treatment engine selects and applies a treatment from the set of treatments based on the generated treatment scores. The user treatment engine determines a reward to the online concierge system for the application of the treatment to the user and updates treatment selection parameters for the applied treatment based on the determined reward.

Treatment selection and application using machine learning to predict cost of treatment where information about cost of treatment is unknown

An online system generates a set of genomic representations, each including multiple genes, in which each gene represents users assigned to a control or test group for performing a test. A metric is identified based on a treatment associated with the test group and a score for each representation is computed based on a difference between two values, in which each value is based on the metric associated with users assigned to the test or control group. A propagation process is executed by identifying representations having at least a threshold score, propagating genes included in the representations to an additional set of representations through recombination and/or mutation, and computing the score for each additional representation. The propagation process is repeated for each additional set of representations until stopping criteria are met and a representation is selected based on scores associated with one or more representations.

Using a genetic algorithm to identify a balanced assignment of online system users to a control group and a test group for performing a test

An online concierge system receives orders from users that include items from one or more warehouses. The online concierge system identifies the orders to shoppers, who select one or more orders to fulfill. The online concierge system uses models to estimate orders likely to be received at different times and shoppers likely to be available to fulfill orders at different times. Responsive to greater than a threshold difference between estimated orders and estimated shoppers during a time interval, the online concierge system selects one or more incentives for shoppers to select orders during the time interval to entice shoppers to select orders during the time interval. An interface displayed to the shoppers by the online concierge system may present a map of warehouses and their estimated number of orders and allow shoppers to identify incentives offered for fulfilling orders at different warehouses during the time interval.

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San Francisco, CA, United States of America

Konrad Gustav Miziolek