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This paper provides convergence theory for knowledge distillation in asynchronous peer-to-peer gossip learning networks, demonstrating that it contracts function disagreement and analyzing theoretical convergence rates.
This paper proposes a t-step lookahead threshold policy for Whittle index in partially observable restless bandits, proving geometric convergence to the exact index and enhancing numerical accuracy.
This paper proposes LMO-IGT, a new class of stochastic optimization methods that accelerates convergence using implicit gradient transport while maintaining a single-gradient-per-iteration structure. It introduces a unified theoretical framework and demonstrates improved performance over existing LMO-based optimizers like Muon.