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In this paper, we introduce Multi-Task Multi-View (M^2TV) learning for such complicated learning problems with both feature heterogeneity and task heterogeneity.

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The key idea is to formulate the search for the optimal consistent multi-label as the finding of the best subgraph in a tree/DAG.

Using a simple greedy strategy, the proposed algorithm is computationally efficient, easy to implement, does not suffer from the problem of insufficient/skewed training data in classifier training, and can be readily used on large hierarchies.

Awards Printed Proceedings Online Proceedings Cross-conference papers In honor of its 25th anniversary, the Machine Learning Journal is sponsoring the awards for the student authors of the best and distinguished papers. The Best Paper award goes to Kevin Waugh, Brian Ziebart and Drew Bagnell for Computational Rationalization: The Inverse Equilibrium Problem. Hashing is becoming increasingly popular for efficient nearest neighbor search in massive databases.

Abhimanyu Das and David Kempe: Submodular meets Spectral: Greedy Algorithms for Subset Selection, Sparse Approximation and Dictionary Selection Miguel Lazaro-Gredilla and Michalis Titsias: Variational Heteroscedastic Gaussian Process Regression Jascha Sohl-Dickstein, Peter Battaglino, and Michael De Weese: Minimum Probability Flow Learning Lauren Hannah and David Dunson: Approximate Dynamic Programming for Storage Problems Sean Gerrish and David Blei: Predicting Legislative Roll Calls from Text Richard Socher, Cliff Chiung-Yu Lin, Andrew Ng, and Chris Manning: Parsing Natural Scenes and Natural Language with Recursive Neural Networks This award is given to papers that time and hindsight proved to be of lasting value to the Machine Learning community. However, learning short codes that yield good search performance is still a challenge.

A single learning task might have features in multiple views (i.e., feature heterogeneity); multiple learning tasks might be related with each other through one or more shared views (i.e., task heterogeneity).

Existing multi-task learning or multi-view learning algorithms only capture one type of heterogeneity.

OSCAR is a recent sparse modeling tool that achieves this by using a $\ell_1$-regularizer and a pairwise $\ell_\infty$-regularizer.

However, its optimization is computationally expensive. In this paper, we propose an efficient solver based on the accelerated gradient methods.

Our formulation allows constant time hashing of a new data point by extrapolating graph Laplacian eigenvectors to eigenfunctions.

Finally, we describe a hierarchical threshold learning procedure in which each eigenfunction yields multiple bits, leading to higher search accuracy.

Experimental results on various real data sets demonstrate its effectiveness.


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