多任务神经扩散过程
Multi-task Neural Diffusion Processes
AI总结：
本文提出多任务神经扩散过程，通过引入任务编码器实现任务感知的条件回归和小样本适应，提升预测精度和不确定性校准。
摘要：
神经扩散过程提供了一种可扩展的、非高斯的方法来建模函数上的分布，但现有的公式仅限于单任务推断，并不能捕捉相关任务之间的依赖关系。在许多多任务回归设置中，联合建模相关函数并启用任务感知的条件化对于提高预测性能和不确定性校准至关重要，尤其是在数据量少的环境中。我们提出了多任务神经扩散过程，这是一种扩展，它引入了一个任务编码器，以实现任务条件的概率回归和跨相关函数的少量样本适应。任务编码器从上下文观察中提取低维表示，并将扩散模型条件化在此表示上，允许在任务之间共享信息，同时保持输入大小无关性和神经扩散过程的等价性属性。所得到的框架保留了神经扩散过程的表达能力和可扩展性，同时能够高效地转移到未见过的任务。实证结果表明，与单任务神经扩散过程和高斯过程基线相比，点预测精度有所提高，预测不确定性校准也更准确。我们验证了该方法在适合风力发电预测的现实风场数据上的有效性。在这一高影响应用中，可靠的不确定性量化直接支持风场管理中的操作决策，展示了在具有挑战性的多任务回归设置中有效的小样本适应。
英文摘要：
Neural diffusion processes provide a scalable, non-Gaussian approach to modelling distributions over functions, but existing formulations are limited to single-task inference and do not capture dependencies across related tasks. We propose multi-task neural diffusion processes (MT-NDPs), an extension that incorporates a task encoder to enable task-conditioned probabilistic regression and few-shot adaptation across related functions. The task encoder extracts a low-dimensional representation from context observations and conditions the diffusion model on this representation, allowing information sharing across tasks while accommodating variable-sized context sets and preserving permutation invariance with respect to the ordering of context observations. We evaluate MT-NDPs through synthetic experiments and two real-world applications involving wind turbine data and school examination outcomes. The results demonstrate improved predictive accuracy relative to single-task diffusion baselines when context observations are available, alongside competitive performance against neural-process and Gaussian-process alternatives. In the wind-energy application, MT-NDPs achieve the lowest average coverage error among the evaluated models at larger context sizes, demonstrating improved predictive uncertainty calibration. Overall, our findings highlight the potential of task-conditioned diffusion models for uncertainty-aware multi-task regression and few-shot adaptation to previously unseen tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
