Machine Learning
Artificial Intelligence, made beautifully human.
Machine learning is the process by which computers learn patterns in data. These patterns can be used to examine the past (causality), or predict the future (forecasting), or imagine new possibilities altogether (generation).
We build systems that learn from data without seeing a single data point and algorithms that forget data when asked. We create models that adapt without retraining from scratch to preserve energy and the Earth. We do this work because we believe that models have the right to forget, and people have the right to be forgotten.
The questions we ask here are not solely technical. We ask: how do we build systems that respect user privacy? What is the role of machine intelligence in a world where every person is unique? How do we create models that reflect resilient and diverse human communities?
Sometimes the answers are surprising, and sometimes they are not. But they are always interesting.
Check out the papers we're reading.
- Remember What You Want to ForgetSekhari et al.
This inspired my first paper. The authors tackle the notion of statistical memory. They specifically investigate deleting the data from a trained model, and how to do so efficiently.
unlearningprivacy - Hessian-Free Online UnlearningQiao et al.
One of the biggest challenges with unlearning is efficiency. Retraining from scratch is often too expensive. This paper proposes a second-order method to unlearn data points efficiently.
unlearningprivacy - Bandit AlgorithmsLattimore et al.
This book provides a comprehensive introduction to bandit algorithms, covering both theory and practical applications.
banditsreinforcement learning