How can machine-learning systems reduce computation, communication, and memory costs without losing rigor?
Methods for making machine learning more efficient, scalable, and robust under communication, computation, and memory constraints.
Methods and questions
- bandit models
- distributed optimization
- communication-efficient learning
- transformer memory