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Found 10 papers, 0 papers with code
An empirical learning-based validation procedure for simulation workflow
Simulation workflow is a top-level model for the design and control of simulation process.
Taming Communication and Sample Complexities in Decentralized Policy Evaluation for Cooperative Multi-Agent Reinforcement Learning
To our knowledge, this paper is the first work that achieves both $\mathcal{O}(\epsilon^{-2})$ sample complexity and $\mathcal{O}(\epsilon^{-2})$ communication complexity in decentralized policy evaluation for cooperative MARL.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
+1
FD-GATDR: A Federated-Decentralized-Learning Graph Attention Network for Doctor Recommendation Using EHR
In the past decade, with the development of big data technology, an increasing amount of patient information has been stored as electronic health records (EHRs).
INTERACT: Achieving Low Sample and Communication Complexities in Decentralized Bilevel Learning over Networks
Our main contributions in this paper are two-fold: i) We first propose a deterministic algorithm called INTERACT (inner-gradient-descent-outer-tracked-gradient) that requires the sample complexity of $\mathcal{O}(n \epsilon^{-1})$ and communication complexity of $\mathcal{O}(\epsilon^{-1})$ to solve the bilevel optimization problem, where $n$ and $\epsilon > 0$ are the number of samples at each agent and the desired stationarity gap, respectively.
SYNTHESIS: A Semi-Asynchronous Path-Integrated Stochastic Gradient Method for Distributed Learning in Computing Clusters
To increase the training speed of distributed learning, recent years have witnessed a significant amount of interest in developing both synchronous and asynchronous distributed stochastic variance-reduced optimization methods.
NET-FLEET: Achieving Linear Convergence Speedup for Fully Decentralized Federated Learning with Heterogeneous Data
Moreover, whether or not the linear speedup for convergence is achievable under fully decentralized FL with data heterogeneity remains an open question.
SAGDA: Achieving $\mathcal{O}(ε^{-2})$ Communication Complexity in Federated Min-Max Learning
To lower the communication complexity of federated min-max learning, a natural approach is to utilize the idea of infrequent communications (through multiple local updates) same as in conventional federated learning.
DIAMOND: Taming Sample and Communication Complexities in Decentralized Bilevel Optimization
Decentralized bilevel optimization has received increasing attention recently due to its foundational role in many emerging multi-agent learning paradigms (e. g., multi-agent meta-learning and multi-agent reinforcement learning) over peer-to-peer edge networks.
PRECISION: Decentralized Constrained Min-Max Learning with Low Communication and Sample Complexities
Decentralized min-max optimization problems with domain constraints underpins many important ML applications, including multi-agent ML fairness assurance, and policy evaluations in multi-agent reinforcement learning.
Federated Multi-Objective Learning
In recent years, multi-objective optimization (MOO) emerges as a foundational problem underpinning many multi-agent multi-task learning applications.
