81 results for "data+analytics" across all locations
Find any ride with the new activity search bar
We’ve updated the Uber for Business dashboard to allow you to search through your activity history.
General Latent Feature Modeling for Data Exploration Tasks
I. Valera, M. Pradier, Z. Ghahramani
This paper introduces a general Bayesian non- parametric latent feature model suitable to per- form automatic exploratory analysis of heterogeneous datasets, where the attributes describing each object can be either discrete, continuous or mixed variables. The proposed model presents several important properties. […] [PDF]
ICML Workshop on Human Interpretability in Machine Learning (ICML), 2017
Uber Movement: data to help the city of Amsterdam
Uber Movement: data to help the city of Amsterdam
Uplift Modeling for Multiple Treatments with Cost Optimization
Z. Zhao, T. Harinen
Uplift modeling is an emerging machine learning approach for estimating the treatment effect at an individual or subgroup level. It can be used for optimizing the performance of interventions such as marketing campaigns and product designs. […] [PDF]
IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2019
Unified Session for Analytical Events
Discover our journey in designing a new analytical session definition and successfully migrating thousands of tables, bringing data metric parity to our organization–a scalable and robust architecture, capable of managing 45M session life cycles per day.
3 quick tips to better manage your organization’s Uber usage
Learn how to use your Uber for Business data to better structure and manage your organisation’s employee and guest travel.
3 quick tips to better manage your organization’s Uber usage
Learn how to use your Uber for Business data to better structure and manage your organization’s employee and guest travel.
VINE: An Open Source Interactive Data Visualization Tool for Neuroevolution
R. Wang, J. Clune, K. Stanley
Recent advances in deep neuroevolution have demonstrated that evolutionary algorithms, such as evolution strategies (ES) and genetic algorithms (GA), can scale to train deep neural networks to solve difficult reinforcement learning (RL) problems. However, it remains a challenge to analyze and interpret the underlying process of neuroevolution in such high dimensions. To begin to address this challenge, this paper presents an interactive data visualization tool called VINE (Visual Inspector for NeuroEvolution) aimed at helping neuroevolution researchers and end-users better understand and explore this family of algorithms. […] [PDF]
Visualization Workshop at The Genetic and Evolutionary Computation Conference (GECCO), 2018
Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform
Z. Zhao, R. Anand, M. Wang
In machine learning applications for online product offerings and marketing strategies, there are often hundreds or thousands of features available to build such models. Feature selection is one essential method in such applications for multiple objectives: improving the prediction accuracy by eliminating irrelevant features, accelerating the model training and prediction speed, reducing the monitoring and maintenance workload for feature data pipeline, and providing better model interpretation and diagnosis capability. […] [PDF]
IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2019
Uber Movement – finding smarter ways to move forward
We’re excited to announce the launch of Movement – a website that uses Uber’s data to help urban planners make informed decisions about our cities.