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Self-Paced Learning for Latent Variable Models
Latent variable models are a powerful tool for addressing several tasks in machine learning.
Learning a Small Mixture of Trees
The problem of approximating a given probability distribution using a simpler distribution plays an important role in several areas of machine learning, e. g. variational inference and classification.
Improved Moves for Truncated Convex Models
Compared to previous approaches based on the LP relaxation, e. g. interior-point algorithms or tree-reweighted message passing (TRW), our method is faster as it uses only the efficient st-mincut algorithm in its design.
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