Presented By
O’Reilly + Cloudera
Make Data Work
March 25-28, 2019
San Francisco, CA
Shradha Agrawal

Shradha Agrawal
Data Scientist, Adobe

Shradha Agrawal is a data scientist at Adobe in San Jose. She holds a master’s degree in computer science with a focus on AI and machine learning from the University of California, San Diego. She is the author of a number of papers and patent applications.

Sessions

4:40pm–5:20pm Thursday, March 28, 2019
Efficient multi-armed bandit with Thompson sampling for applications with delayed feedback
Data Science, Machine Learning & AI
 Location: 2010
Secondary topics:  Media, Marketing, Advertising
Shradha Agrawal (Adobe)
Average rating: ****.
(4.17, 6 ratings)
Decision making often struggles with the exploration-exploitation dilemma. Multi-armed bandits (MAB) are a popular reinforcement learning solution, but increasing the number of decision criteria leads to an exponential blowup in complexity, and observational delays don’t allow for optimal performance. Shradha Agrawal offers an overview of MABs and explains how to overcome the above challenges. Read more.