1 citations · 1 across the 4 of their papers we have counts for
6 papers
Optimal Network Dependence in Distributed Stochastic Optimization via Tree Routing
Runze You, Shi Pu
Communication is a central bottleneck in distributed optimization, but its effect is often summarized by the spectral gap of a chosen mixing matrix. Since this gap depends on link…
FedSUM Family: Efficient Federated Learning Methods under Arbitrary Client Participation
Runze You, Shi Pu
Federated Learning (FL) methods are often designed for specific client participation patterns, limiting their applicability in practical deployments. We introduce the FedSUM family…
In-in worldline formalism in pair creating fields
Patrick Copinger, Shi Pu
An in-in framework under Schwinger pair creating fields in strong-field quantum electrodynamics is formulated using in-out propagators in coordinate space, that have first-quantize…
Stochastic Push-Pull for Decentralized Nonconvex Optimization
Runze You, Shi Pu
To understand the convergence behavior of the Push-Pull method for decentralized optimization with stochastic gradients (Stochastic Push-Pull), this paper presents a comprehensive…
Distributed Learning over Arbitrary Topology: Linear Speed-Up with Polynomial Transient Time
Runze You, Shi Pu
We study a distributed learning problem in which agents, each with potentially heterogeneous local data, collaboratively minimize the sum of their local cost functions via peer…
B-ary Tree Push-Pull Method is Provably Efficient for Distributed Learning on Heterogeneous Data
Runze You, Shi Pu
This paper considers the distributed learning problem where a group of agents cooperatively minimizes the summation of their local cost functions based on peer-to-peer communicatio…