How to model dynamic, sparse correlations in multi-output GPs?
Non-stationary and Sparsely-correlated Multi-output Gaussian Process with Spike-and-Slab Prior*
This research paper introduces a novel non-stationary Multi-output Gaussian Process (MGP) model designed for transfer learning in scenarios where data characteristics change over time. It specifically addresses the challenge of dynamic and sparse correlations between multiple outputs (agents) to improve prediction accuracy and mitigate negative transfer from unrelated agents. This is achieved by incorporating a dynamic spike-and-slab prior on correlation parameters, allowing the model to identify and leverage information only from relevant agents at different time points.
The key points relevant to LLM-based multi-agent systems include:
- Dynamic Agent Interactions: The paper's focus on non-stationary data and dynamic correlation aligns with the concept of agents in a multi-agent system whose interactions change over time.
- Sparse Interactions: The proposed dynamic spike-and-slab prior enables the identification of sparse correlations, analogous to situations in multi-agent systems where only certain agents are relevant to a specific task or at a particular time.
- Negative Transfer Mitigation: The paper addresses the issue of negative transfer, which can also occur in multi-agent systems when information from irrelevant agents hinders learning for a specific agent.
The model presents a framework that can be adapted to LLM-based multi-agent systems for tasks like dynamically choosing which agents to query for specific requests, identifying key influencers in agent communication networks, or adapting communication strategies based on evolving agent relationships.