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Home Authors Posts by Michael Mui

Michael Mui

3 BLOG ARTICLES 1 RESEARCH PAPERS
Michael Mui is a Senior Software Engineer on Uber's Machine Learning Platform team. He is based in San Francisco. He works on the distributed training infrastructure, hyperparameter optimization, model representation, and evaluation.

Engineering Blog Articles

Tuning Model Performance

Introduction

Uber uses machine learning (ML) models to power critical business decisions. An ML model goes through many experiment iterations before making it to production. During the experimentation phase, data scientists or machine learning engineers explore adding features, tuning parameters,

Productionizing Distributed XGBoost to Train Deep Tree Models with Large Data Sets at Uber

Michelangelo, Uber’s machine learning (ML) platform, powers machine learning model training across various use cases at Uber, such as forecasting rider demand, fraud detection, food discovery and recommendation for Uber Eats, and improving the accuracy of

Evolving Michelangelo Model Representation for Flexibility at Scale

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Michelangelo, Uber’s machine learning (ML) platform, supports the training and serving of thousands of models in production across the company. Designed to cover the end-to-end ML workflow, the system currently supports classical machine learning, time series forecasting, and deep

Research Papers

Learning Continuous Treatment Policy and Bipartite Embeddings for Matching with Heterogeneous Causal Effects

W. Y. Zou, S. Shyam, M. Mui, M. Wang, J. Pedersen, Z. Ghahramani
Causal inference methods are widely applied in the fields of medicine, policy, and economics. Central to these applications is the estimation of treatment effects to make decisions. Current methods make binary yes-or-no decisions based on the treatment effect of a single outcome dimension. These methods are unable to capture continuous space treatment policies with a measure of intensity. [...] [PDF]
2020

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