Statistical Learning
Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-Tuning
Most previous work on federated learning assumes that clients possess static batches of training data. However, clients may also need to make real-time predictions on streaming data in non-stationary environments.
On the Overlooked Structure of Stochastic Gradients Zeke Xie
Stochastic gradients closely relate to both optimization and generalization of deep neural networks (DNNs).
A Kernel Perspective on Distillation-based Collaborative Learning
To tackle this problem, we rigorously analyze a representative distillation-based algorithm in the view of kernel regression.
Markov Equivalence and Consistency in Differentiable Structure Learning Chang Deng Kevin Bello
These results are then generalized to general models and likelihoods, where the same claims hold.