Matrix analysis
Topics in matrix analysis
Mistake bounds and logarithmic linear-threshold learning algorithms
Mistake bounds and logarithmic linear-threshold learning algorithms
Large Margin Classification Using the Perceptron Algorithm
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Relative Loss Bounds for Multidimensional Regression Problems
Machine Learning
General Convergence Results for Linear Discriminant Updates
Machine Learning
The Robustness of the p-Norm Algorithms
Machine Learning
A Second-Order Perceptron Algorithm
SIAM Journal on Computing
Learning Multiple Tasks with Kernel Methods
The Journal of Machine Learning Research
Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection
The Journal of Machine Learning Research
ICML '05 Proceedings of the 22nd international conference on Machine learning
Prediction, Learning, and Games
Prediction, Learning, and Games
Efficient co-regularised least squares regression
ICML '06 Proceedings of the 23rd international conference on Machine learning
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
The Journal of Machine Learning Research
Bounds for Linear Multi-Task Learning
The Journal of Machine Learning Research
Proceedings of the 24th international conference on Machine learning
Online Learning of Multiple Tasks with a Shared Loss
The Journal of Machine Learning Research
An RKHS for multi-view learning and manifold co-regularization
Proceedings of the 25th international conference on Machine learning
Multi-view regression via canonical correlation analysis
COLT'07 Proceedings of the 20th annual conference on Learning theory
Multitask learning with expert advice
COLT'07 Proceedings of the 20th annual conference on Learning theory
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Mirror descent and nonlinear projected subgradient methods for convex optimization
Operations Research Letters
Ensembles and multiple classifiers: a game-theoretic view
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Inductive multi-task learning with multiple view data
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Online multi-task collaborative filtering for on-the-fly recommender systems
Proceedings of the 7th ACM conference on Recommender systems
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We introduce new Perceptron-based algorithms for the online multitask binary classification problem. Under suitable regularity conditions, our algorithms are shown to improve on their baselines by a factor proportional to the number of tasks. We achieve these improvements using various types of regularization that bias our algorithms towards specific notions of task relatedness. More specifically, similarity among tasks is either measured in terms of the geometric closeness of the task reference vectors or as a function of the dimension of their spanned subspace. In addition to adapting to the online setting a mix of known techniques, such as the multitask kernels of Evgeniou et al., our analysis also introduces a matrix-based multitask extension of the p-norm Perceptron, which is used to implement spectral co-regularization. Experiments on real-world data sets complement and support our theoretical findings.