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Found 5 papers, 0 papers with code
FedFNN: Faster Training Convergence Through Update Predictions in Federated Recommender Systems
Federated Learning (FL) has emerged as a key approach for distributed machine learning, enhancing online personalization while ensuring user data privacy.
Online Meta-Learning for Model Update Aggregation in Federated Learning for Click-Through Rate Prediction
In Federated Learning (FL) of click-through rate (CTR) prediction, users' data is not shared for privacy protection.
Projecting Markov Random Field Parameters for Fast Mixing
Markov chain Monte Carlo (MCMC) algorithms are simple and extremely powerful techniques to sample from almost arbitrary distributions.
Projecting Ising Model Parameters for Fast Mixing
Inference in general Ising models is difficult, due to high treewidth making tree-based algorithms intractable.
Learning as MAP Inference in Discrete Graphical Models
Instead of relying on convex losses and regularisers such as in SVMs, logistic regression and boosting, or instead non-convex but continuous formulations such as those encountered in neural networks and deep belief networks, our framework entails a non-convex but \emph{discrete} formulation, where estimation amounts to finding a MAP configuration in a graphical model whose potential functions are low-dimensional discrete surrogates for the misclassification loss.
