Search Results for author:
Found 6 papers, 4 papers with code
Generative Models in Decision Making: A Survey
In recent years, the exceptional performance of generative models in generative tasks has sparked significant interest in their integration into decision-making processes.
Calibrated One Round Federated Learning with Bayesian Inference in the Predictive Space
To improve scalability for larger models, one common Bayesian approach is to approximate the global predictive posterior by multiplying local predictive posteriors.
Model-Based Offline Reinforcement Learning with Pessimism-Modulated Dynamics Belief
To make practical, we further devise an offline RL algorithm to approximately find the solution.
Ranked #1 on
D4RL
on D4RL
Robust One Round Federated Learning with Predictive Space Bayesian Inference
In contrast, our method performs the aggregation on the predictive posteriors, which are typically easier to approximate owing to the low-dimensionality of the output space.
Personalized Federated Learning via Variational Bayesian Inference
Federated learning faces huge challenges from model overfitting due to the lack of data and statistical diversity among clients.
Federated Bayesian Neural Regression: A Scalable Global Federated Gaussian Process
We present Federated Bayesian Neural Regression (FedBNR), an algorithm that learns a scalable stand-alone global federated GP that respects clients' privacy.
