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Found 4 papers, 0 papers with code
No-Regret Learning of Nash Equilibrium for Black-Box Games via Gaussian Processes
This paper investigates the challenge of learning in black-box games, where the underlying utility function is unknown to any of the agents.
Learning in Online Principal-Agent Interactions: The Power of Menus
We study a ubiquitous learning challenge in online principal-agent problems during which the principal learns the agent's private information from the agent's revealed preferences in historical interactions.
On the Effect of Defections in Federated Learning and How to Prevent Them
Federated learning is a machine learning protocol that enables a large population of agents to collaborate over multiple rounds to produce a single consensus model.
A Data-Centric Online Market for Machine Learning: From Discovery to Pricing
We envision that the synergy of our data and model discovery algorithm and pricing mechanism will be an important step towards building a new data-centric online market that serves ML users effectively.
