A New Generation of Trainable Machines for Multi-Task Learning

Lead Research Organisation: University College London
Department Name: Computer Science

Abstract

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.

Publications

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Argyriou A (2008) Convex multi-task feature learning in Machine Learning

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Argyriou A. (2010) On spectral learning in Journal of Machine Learning Research

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Argyriou A. (2007) Multi-task feature learning in Advances in Neural Information Processing Systems

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Argyriou A. (2009) When is there a representer theorem? Vector versus matrix regularizers in Journal of Machine Learning Research

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Caponnetto A (2008) Entropy conditions for L r -convergence of empirical processes in Advances in Computational Mathematics

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Caponnetto A. (2008) Universal multi-task Kernels in Journal of Machine Learning Research

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Herbster M. (2009) Fast prediction on a tree in Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference

 
Description We developed a novel machine learning framework, which combines information from multiple data sources to improve the problems of learning tasks (e.g. visual object classification, user preference modelling, etc.).

Multitask learning (MTL) has been studied since the early 90's. Before the publication of our work, MTL consisted mainly of ad-hoc approaches, lacking a rigorous mathematical foundation. During the course of the project we established a novel framework for MTL based on spectral regularisation and convex optimisation. We provided a detailed analysis of the proposed learning methods and demonstrate numerically
that they improve over the state of the art methods.
Exploitation Route The techniques developed during the project are now mainstream in machine learning and related disciplines. Some of the papers published are listed as highly cited by ISI Thomson.
Sectors Digital/Communication/Information Technologies (including Software),Healthcare

 
Description Several companies working on Big Data are aware of the importance of multitask and transfer learning; the methodology developed in the project has been used by researchers in some of the leading internet companies such a Google, Yahoo, as well as IBM.
First Year Of Impact 2013
Sector Healthcare,Retail
Impact Types Economic

 
Description Collaboration with Prof. Charles Micchelli, University at Albany SUNY 
Organisation University at Buffalo
Country United States of America 
Sector Academic/University 
PI Contribution Prof. Micchelli is an expert in approximation theory and convex analysis
Collaborator Contribution Prof. Micchelli provided key insight into issues pertaining the study of convex learning algorithm, the study of so-called representer theorem and the analysis of certain regularisers which arise in structured sparsity learning problems and in multitask learning.
Impact This is multidisciplinary collaboration across Computer Science and Applied Mathematics Please see the section "Publications" for specific research outputs.
 
Description Collaboration with Prof. Theodoros Evgeniou 
Organisation INSEAD
Country France, French Republic 
Sector Academic/University 
PI Contribution Prof. Evgeniou offers complementaries expertise on practical aspects on machine learning, particularly he is a world leading expert on machine learning applications arising in marketing science and user preference modelling.
Collaborator Contribution Prof. Evgeniou discussed with me several multitask learning methodologies as well as provided key insight into their applications to in marketing science and user preference modelling.
Impact This is multidisciplinary collaboration across computer science and management science. Please see the section "Publications" for research outputs.