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Found 6 papers, 4 papers with code
Impact of Decentralized Learning on Player Utilities in Stackelberg Games
To better understand such cases, we examine the learning dynamics of the two-agent system and the implications for each agent's objective.
When Are Two Lists Better than One?: Benefits and Harms in Joint Decision-making
Surprisingly, we show that for multiple of noise models, it is optimal to set $k \in [2, n-1]$ - that is, there are strict benefits to collaborating, even when the human and algorithm have equal accuracy separately.
Human-Algorithm Collaboration: Achieving Complementarity and Avoiding Unfairness
In many settings, however, the final prediction or decision of a system is under the control of a human, who uses an algorithm's output along with their own personal expertise in order to produce a combined prediction.
Models of fairness in federated learning
These agents can collaborate to build a machine learning model based on data from multiple agents, potentially reducing the error each experiences.
Optimality and Stability in Federated Learning: A Game-theoretic Approach
One branch of this research has taken a game-theoretic approach, and in particular, prior work has viewed federated learning as a hedonic game, where error-minimizing players arrange themselves into federating coalitions.
Model-sharing Games: Analyzing Federated Learning Under Voluntary Participation
Federated learning is a setting where agents, each with access to their own data source, combine models from local data to create a global model.
