Speaker: Yang Wang from UBER
Interpretation and diagnosis of machine learning models have gained renewed interest in recent years with breakthroughs in new approaches. We present Manifold, Uber’s in-house model-agnostic visualization tool for ML performance diagnosis and model debugging. Manifold is designed as a generic framework that does not rely on or access the internal logic of the model and solely observes the input (i.e., instances or features) and the output (i.e., the predicted result and probability distribution). We describe the workflow of Manifold as an iterative process consisting of three major phases that are commonly involved in the model development and diagnosis process: inspection (hypothesis), explanation (reasoning), and refinement (verification). And demonstrate current applications of the framework on the classification and regression tasks and discuss other potential machine learning use scenarios where Manifold can be applied.
Yang Wang is a Sr. Research Engineer leading the Machine Learning Visualization team at Uber. His research interests lie in Human-Computer Interaction and High-Performance Computing, specifically, methodologies of modeling the Interpretability and Actionability of AI-aided decision-making processes. At Uber, Yang and team build ML infrastructures, publish & tech-transfer research papers, and work across business units to help Data Scientists, Engineers, and City-Ops accelerate their model iteration process.