Multi-output 2018 - 2018 Workshop on Multi-output Learning

Multi-output learning aims to predict multiple outputs for an input, where the output values are characterized by diverse data types, such as binary, nominal, ordinal and real-valued variables. Such learning tasks arise in a variety of real-world applications, ranging from document classification, computer emulation, sensor network analysis, concept-based information retrieval, human action/causal induction, to video analysis, image annotation/retrieval, gene function prediction and brain science. Due to its popularity in applications, multi-output learning has also been widely explored in machine learning community, such as multi-label/multi-class classification, multi-target regression, hierarchical classification with class taxonomies, label sequence learning, sequence alignment learning, and supervised grammar learning, and so on.
The theoretical properties of existing approaches for multi-output data are still not well understood. This triggers practitioners to develop novel methodologies and theories to deeply understand multi-output learning tasks. Moreover, the emerging trends of ultrahigh input and output dimensionality, and the complexly structured objects, lead to formidable challenges for multi-output learning. Therefore, it is imperative to propose practical mechanisms and efficient optimization algorithms for large-scale applications. Deep learning has gained much popularity in today’s research, and has been developed in recent years to deal with multi-label and multi-class classification problems. However, it remains non-trivial for practitioners to design novel deep neural networks that are appropriate for more comprehensive multi-output learning domains.
Topics of Interest
Interested topics include, but are not limited to:
Novel deep learning methods for multi-output learning tasks.
Novel modellings for multi-output learning from new perspectives.
Statistical theory analysis for multiple output learning.
Large-scale optimization algorithms for multiple output learning.
Sparse representation learning for large-scale multiple output learning.
Active learning for multi-output data.
Online learning for multi-output data.
Metric learning for multi-output data.
Multi-output learning with noisy data.
Multi-output learning with imbalanced data.
New applications.