Search Results for author:
Found 6 papers, 2 papers with code
Competition over data: how does data purchase affect users?
We introduce a new environment that allows ML predictors to use active learning algorithms to purchase labeled data within their budgets while competing against each other to attract users.
Submix: Practical Private Prediction for Large-Scale Language Models
Recent data-extraction attacks have exposed that language models can memorize some training samples verbatim.
MLDemon: Deployment Monitoring for Machine Learning Systems
Post-deployment monitoring of ML systems is critical for ensuring reliability, especially as new user inputs can differ from the training distribution.
Competing AI: How does competition feedback affect machine learning?
A service that is more often queried by users, perhaps because it more accurately anticipates user preferences, is also more likely to obtain additional user data (e. g. in the form of a Yelp review).
Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems
Embedding representations power machine intelligence in many applications, including recommendation systems, but they are space intensive -- potentially occupying hundreds of gigabytes in large-scale settings.
Making AI Forget You: Data Deletion in Machine Learning
Intense recent discussions have focused on how to provide individuals with control over when their data can and cannot be used --- the EU's Right To Be Forgotten regulation is an example of this effort.
