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Found 5 papers, 0 papers with code
Wide Network Learning with Differential Privacy
Despite intense interest and considerable effort, the current generation of neural networks suffers a significant loss of accuracy under most practically relevant privacy training regimes.
Accelerated learning from recommender systems using multi-armed bandit
However, there are a number of issues with A/B testing that make it impractical to be the sole method of testing, including long lead time, and high cost of exploration.
Deep Personalized Re-targeting
The footprint of the travelers in their discovery is a useful data source to help these marketplaces to predict shopping probability and value.
A Simple Deep Personalized Recommendation System
Recommender systems are critical tools to match listings and travelers in two-sided vacation rental marketplaces.
A/B Testing Measurement Framework for Recommendation Models Based on Expected Revenue
We use the two-part test suggested by Lachenbruch to determine if the data generating process in the new system is different.
