A New Generation of Trainable Machines for Multi-Task Learning

机器学习在计算机科学和相关学科中发挥着越来越重要的作用。在过去的十年中，强大的台式计算机的可用性为机器学习的经验和理论研究之间的协同互动打开了大门，显示了“从示例中学习”范式在各种应用中的价值。机器学习的研究者们已经在标准的单任务学习问题上投入了大量的精力，并取得了令人兴奋的结果。然而，与人类相比，机器学习的能力仍然非常有限。在当前的机器学习系统中，人类概括在一个任务中学习到的知识以解决新任务的能力是不可用的。多任务学习的研究在该领域尚未得到足够的重视。标准的单一任务学习方法建立在假设的基础上，这些假设限制性太强，难以扩展到本提案中设想的新学习场景。虽然多任务学习已经提供了有趣的见解，但目前还没有全面的多任务学习框架，也没有在该领域奠定基础。因此，本提案的主要目的是发展机器学习研究的这一领域。该提案的重点是统计机器学习方法，用于学习多个相关（分类或回归）任务并整合它们之间的信息。我们将设计任务之间关系的正式模型，并开发（学习算法）从数据中学习这些关系。我们还将开发多任务学习的数学基础（泛化界限，近似结果，收敛结果），扩展单任务学习的一些关键理论结果。此外，学习算法将应用于两个关键应用，即用户偏好建模和多个微阵列基因表达数据分析。在我们的方法中的一个核心作用是发挥某些图形结构，使我们能够建模任务关系。这种方法非常通用，可以适应日益复杂的学习场景。计算方法基于通过与任务相关联的大量超参数来最小化某些罚函数。这项研究将为多任务学习带来新一代可训练机器，这将是更强大、更灵活的学习模型，比以前开发的机器学习框架更接近人类学习。

The field of Machine Learning plays an increasingly important role in Computer Science and related disciplines. Over the past decade, the availability of powerful desktop computers has opened the door to synergistic interactions between empirical and theoretical studies of Machine Learning, showing thevalue of the ``learning from example'' paradigm in a wide variety of applications. Much effort has been devoted by Machine Learning researchers to the standard single task learning problem and exciting results have been derived. However, Machine Learning capabilities are still extremely limited when compared to those of humans. The human ability to generalise knowledge learned in one task in order to solve a new task is not available in current Machine Learning systems. Multi-task learning research has not yet received sufficient attention in the field. The standard single task learning approach builds on assumptions that are too restrictive to be easily extended to the novel learning scenarios which are envisaged in this proposal. Although interesting insights on multi-task learning have been provided, at present there is no comprehensive framework for multi-task learning and no cornerstone has yet been placed in the field. Thus, the main purpose of this proposal is to develop this area of Machine Learning research. The proposal focuses on Statistical Machine Learning methods for learning multiple related (classification or regression) tasks and integrating information across them. We shall design formal models of relationships between the tasks and develop (learning algorithms) for learning these relationships from data. We shall also develop the mathematical foundations (generalisation bounds, approximation results, convergence results) for multi-task learning, extending some key theoretical results for single tasklearning. Furthermore, the learning algorithms will be applied to two key applications, namely user preference modelling and multiple microarray gene expression data analysis. A central role in our approach is played by certain graph structures which allow us to model task relationships. This approach is very general and can be adapted to increasingly complex learning scenarios. The computational methods are based on the minimisation of certain penalty functionals via a large number of hyper-parameters associated with the tasks. The proposed research will lead to a new generation of trainable machines for multi-task learning, which will be more powerful and flexible models of learning, closer to human learning than previously developed Machine Learning frameworks.