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Christopher Cox

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2 papers

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AAAI Conference 2020 Conference Paper

Side Information Dependence as a Regularizer for Analyzing Human Brain Conditions across Cognitive Experiments

Shuo Zhou
Wenwen Li
Christopher Cox
Haiping Lu

The increasing of public neuroimaging datasets opens a door to analyzing homogeneous human brain conditions across datasets by transfer learning (TL). However, neuroimaging data are high-dimensional, noisy, and with small sample sizes. It is challenging to learn a robust model for data across different cognitive experiments and subjects. A recent TL approach minimizes domain dependence to learn common cross-domain features, via the Hilbert-Schmidt Independence Criterion (HSIC). Inspired by this approach and the multisource TL theory, we propose a Side Information Dependence Regularization (SIDeR) learning framework for TL in brain condition decoding. Speciﬁcally, SIDeR simultaneously minimizes the empirical risk and the statistical dependence on the domain side information, to reduce the theoretical generalization error bound. We construct 17 brain decoding TL tasks using public neuroimaging data for evaluation. Comprehensive experiments validate the superiority of SIDeR over ten competing methods, particularly an average improvement of 15. 6% on the TL tasks with multi-source experiments.

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NeurIPS Conference 2013 Conference Paper

Sparse Overlapping Sets Lasso for Multitask Learning and its Application to fMRI Analysis

Nikhil Rao
Christopher Cox
Rob Nowak
Timothy Rogers

Multitask learning can be effective when features useful in one task are also useful for other tasks, and the group lasso is a standard method for selecting a common subset of features. In this paper, we are interested in a less restrictive form of multitask learning, wherein (1) the available features can be organized into subsets according to a notion of similarity and (2) features useful in one task are similar, but not necessarily identical, to the features best suited for other tasks. The main contribution of this paper is a new procedure called {\em Sparse Overlapping Sets (SOS) lasso}, a convex optimization that automatically selects similar features for related learning tasks. Error bounds are derived for SOSlasso and its consistency is established for squared error loss. In particular, SOSlasso is motivated by multi-subject fMRI studies in which functional activity is classified using brain voxels as features. Experiments with real and synthetic data demonstrate the advantages of SOSlasso compared to the lasso and group lasso.

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