10 citations · 29 across the 14 of their papers we have counts for
3 papers · 1 filter
Uncertainty Quantification for Regression using Proper Scoring Rules
Alexander Fishkov, Kajetan Schweighofer, Mykyta Ielanskyi +3
Quantifying uncertainty of machine learning model predictions is essential for reliable decision-making, especially in safety-critical applications. Recently, uncertainty quantific…
Who to Trust? Aggregating Client Predictions in Federated Distillation
Viktor Kovalchuk, Denis Son, Arman Bolatov +6
Under data heterogeneity (e.g., ), clients may produce unreliable predictions for instances belonging to unfamiliar classes. An equally weighted combinatio…
FedPop: A Bayesian Approach for Personalised Federated Learning
Nikita Kotelevskii, Maxime Vono, Eric Moulines +1
Personalised federated learning (FL) aims at collaboratively learning a machine learning model taylored for each client. Albeit promising advances have been made in this direction,…