Channel-Driven Monte Carlo Sampling for Bayesian Distributed Learning in Wireless Data Centers

Keyphrases

• Channel Noise 100%
• Monte Carlo Sampling 100%
• Distributed Learning 100%
• Wireless Data Centers 100%
• Bayesian Learning 100%
• MCMC Sampling 100%
• Frequentist 100%
• Wireless 66%
• Learning Protocols 66%
• Over the Air 66%
• Uncoded Transmission 66%
• Consensus Monte Carlo 66%
• Active Learning 33%
• Computational Complexity 33%
• Optimization Strategy 33%
• Posterior Distribution 33%
• Variational Optimization 33%
• Parameter Space 33%
• Asymptotically Optimal 33%
• Embarrassingly Parallel 33%
• Nuisance 33%
• Order of Accuracy 33%
• Learning Objectives 33%
• Central Server 33%
• Sampling Process 33%
• Principled Approach 33%
• Continual Learning 33%
• Federated Learning 33%
• Distributed Bayesian Learning 33%
• Monte Carlo Scheme 33%
• Distributed Workers 33%

Computer Science

• Data Center 100%
• Distributed Learning 100%
• Bayesian Learning 100%
• Uncoded Transmission 50%
• Active Learning 25%
• Approximation (Algorithm) 25%
• Computational Complexity 25%
• Sampling Process 25%
• Posterior Distribution 25%
• Optimization Strategy 25%
• Parameter Space 25%
• Federated Learning 25%