3 accepted papers
In many critical applications, sensitive data is inherently distributed and cannot be centralized due to privacy concerns. A wide range of federated learning approaches have been proposed to train models locally at each client without sharing their sensitive data, typically by exchanging model param…
There are many applications that benefit from computing the exact divergence between 2 discrete probability measures, including machine learning. Unfortunately, in the absence of any assumptions on the structure or independencies within these distributions, computing the divergence between them is a…
Many businesses nowadays rely on large quantities of time series data making time series forecasting an important research area. Global forecasting models and multivariate models that are trained across sets of time series have shown huge potential in providing accurate forecasts compared with the t…