Adaptive Smoothed Online Multi-Task Learning

This paper addresses the challenge of jointly learning both the per-task model parameters and the inter-task relationships in a multi-task online learning setting. The proposed algorithm features probabilistic interpretation, efficient updating rules and flexible modulation on whether learners focus on their specific task or on jointly address all tasks. The paper also proves a sub-linear regret bound as compared to the best linear predictor in hindsight. Experiments over three multi-task learning benchmark datasets show advantageous performance of the proposed approach over several state-of-the-art online multi-task learning baselines.

[1]  Jaime G. Carbonell,et al.  Multisource transfer learning for host-pathogen protein interaction prediction in unlabeled tasks , 2013 .

[2]  Jaime G. Carbonell,et al.  Multitask learning for host–pathogen protein interactions , 2013, Bioinform..

[3]  Peter L. Bartlett,et al.  Matrix regularization techniques for online multitask learning , 2008 .

[4]  Daphne Koller,et al.  Self-Paced Learning for Latent Variable Models , 2010, NIPS.

[5]  John Blitzer,et al.  Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification , 2007, ACL.

[6]  Gábor Lugosi,et al.  Online Multi-task Learning with Hard Constraints , 2009, COLT.

[7]  Claudio Gentile,et al.  Linear Algorithms for Online Multitask Classification , 2010, COLT.

[8]  Shiguang Shan,et al.  Self-Paced Learning with Diversity , 2014, NIPS.

[9]  John Darzentas,et al.  Problem Complexity and Method Efficiency in Optimization , 1983 .

[10]  Peter L. Bartlett,et al.  Multitask Learning with Expert Advice , 2007, COLT.

[11]  Eric Eaton,et al.  ELLA: An Efficient Lifelong Learning Algorithm , 2013, ICML.

[12]  Shai Shalev-Shwartz,et al.  Online learning: theory, algorithms and applications (למידה מקוונת.) , 2007 .

[13]  Dit-Yan Yeung,et al.  A Regularization Approach to Learning Task Relationships in Multitask Learning , 2014, ACM Trans. Knowl. Discov. Data.

[14]  Philip M. Long,et al.  Online Learning of Multiple Tasks with a Shared Loss , 2007, J. Mach. Learn. Res..

[15]  Koby Crammer,et al.  A theory of learning from different domains , 2010, Machine Learning.

[16]  Koby Crammer,et al.  Learning Multiple Tasks using Shared Hypotheses , 2012, NIPS.

[17]  Massimiliano Pontil,et al.  Convex multi-task feature learning , 2008, Machine Learning.

[18]  Lawrence Carin,et al.  Multi-Task Learning for Classification with Dirichlet Process Priors , 2007, J. Mach. Learn. Res..

[19]  Kilian Q. Weinberger,et al.  Feature hashing for large scale multitask learning , 2009, ICML '09.

[20]  Koby Crammer,et al.  Online Passive-Aggressive Algorithms , 2003, J. Mach. Learn. Res..

[21]  Avishek Saha,et al.  Online Learning of Multiple Tasks and Their Relationships , 2011, AISTATS.