Adaptive Data Fusion for Multi-task Non-smooth Optimization

In most machine-learning contexts, algorithm developers and theorists are concerned with solving a single task or optimizing a single metric at a time. Nonetheless, even in the big data era, the datasets are expensive and oftentimes collected for a large number of tasks, and models based on a single task likely hit the performance ceiling due to the limited sample size without fully exploiting the dataset featuring multiple tasks. For instance, in inventory management, the hype cycle of technology is getting shortened. It is increasingly critical for retailers to recognize the consumption patterns of customers as early as possible, so as to minimize the cost caused by backordering and holding. Since the selling data is limited at the early stage of the operations, decision making can generally be challenging.