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Found 5 papers, 0 papers with code
Regret-Minimization Algorithms for Multi-Agent Cooperative Learning Systems
The regret bounds I present in Chapter 3, 4 and 5 quantify how the regret depends on the connectivity of the communication network and the communication delay, thus giving useful guidance on design of the communication protocol in MACL systems
Doubly Adversarial Federated Bandits
For the bandit feedback setting, we propose a near-optimal federated bandit algorithm called FEDEXP3.
On Regret-optimal Cooperative Nonstochastic Multi-armed Bandits
We show that with suitable regularizers and communication protocols, a collaborative multi-agent \emph{follow-the-regularized-leader} (FTRL) algorithm has an individual regret upper bound that matches the lower bound up to a constant factor when the number of arms is large enough relative to degrees of agents in the communication graph.
Automatic Loss Function Search for Predict-Then-Optimize Problems with Strong Ranking Property
Combinatorial optimization problems with parameters to be predicted from side information are commonly seen in a variety of problems during the paradigm shift from reactive decision making to proactive decision making.
Pure Exploration and Regret Minimization in Matching Bandits
Finding an optimal matching in a weighted graph is a standard combinatorial problem.
